     [file version.texi not included]

Dynare
******

This is Dynare Reference Manual, version VERSION.

Copyright © 1996-2012, Dynare Team.

     Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.

     A copy of the license can be found at http://www.gnu.org/licenses/fdl.txt.

Introduction
************

What is Dynare ?
================

Dynare is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium (DSGE) and overlapping generations (OLG) models. The models solved by Dynare include those relying on the rational expectations hypothesis, wherein agents form their expectations about the future in a way consistent with the model. But Dynare is also able to handle models where expectations are formed differently: on one extreme, models where agents perfectly anticipate the future; on the other extreme, models where agents have limited rationality or imperfect knowledge of the state of the economy and, hence, form their expectations through a learning process. In terms of types of agents, models solved by Dynare can incorporate consumers, productive firms, governments, monetary authorities, investors and financial intermediaries. Some degree of heterogeneity can be achieved by including several distinct classes of agents in each of the aforementioned agent categories.

Dynare offers a user-friendly and intuitive way of describing these models. It is able to perform simulations of the model given a calibration of the model parameters and is also able to estimate these parameters given a dataset. In practice, the user will write a text file containing the list of model variables, the dynamic equations linking these variables together, the computing tasks to be performed and the desired graphical or numerical outputs.

A large panel of applied mathematics and computer science techniques are internally employed by Dynare: multivariate nonlinear solving and optimization, matrix factorizations, local functional approximation, Kalman filters and smoothers, MCMC techniques for Bayesian estimation, graph algorithms, optimal control, ...

Various public bodies (central banks, ministries of economy and finance, international organisations) and some private financial institutions use Dynare for performing policy analysis exercises and as a support tool for forecasting exercises. In the academic world, Dynare is used for research and teaching purposes in postgraduate macroeconomics courses.

Dynare is a free software, which means that it can be downloaded free of charge, that its source code is freely available, and that it can be used for both non-profit and for-profit purposes. Most of the source files are covered by the GNU General Public Licence (GPL) version 3 or later (there are some exceptions to this, see the file license.txt in Dynare distribution). It is available for the Windows, Mac and Linux platforms and is fully documented through a user guide and a reference manual. Part of Dynare is programmed in C++, while the rest is written using the MATLAB (http://www.mathworks.com/products/matlab/) programming language.  The latter implies that commercially-available MATLAB software is required in order to run Dynare. However, as an alternative to MATLAB, Dynare is also able to run on top of GNU Octave (http://www.octave.org) (basically a free clone of MATLAB): this possibility is particularly interesting for students or institutions who cannot afford, or do not want to pay for, MATLAB and are willing to bear the concomitant performance loss.

The development of Dynare is mainly done at Cepremap (http://www.cepremap.ens.fr) by a core team of researchers who devote part of their time to software development. Currently the development team of Dynare is composed of Stéphane Adjemian (Université du Maine, Gains and Cepremap), Houtan Bastani (Cepremap), Michel Juillard (Banque de France), Frédéric Karamé (Université d'Évry, Epee and Cepremap), Junior Maih (Norges Bank), Ferhat Mihoubi (Université d'Évry, Epee and Cepremap), George Perendia, Johannes Pfeifer, Marco Ratto (JRC) and Sébastien Villemot (Cepremap and Paris School of Economics). Increasingly, the developer base is expanding, as tools developed by researchers outside of Cepremap are integrated into Dynare. Financial support is provided by Cepremap, Banque de France and DSGE-net (an international research network for DSGE modeling). The Dynare project also received funding through the Seventh Framework Programme for Research (FP7) of the European Commission's Socio-economic Sciences and Humanities (SSH) Program from October 2008 to September 2011 under grant agreement SSH-CT-2009-225149.

Interaction between developers and users of Dynare is central to the project. A web forum (http://www.dynare.org/phpBB3) is available for users who have questions about the usage of Dynare or who want to report bugs. Training sessions are given through the Dynare Summer School, which is organized every year and is attended by about 40 people. Finally, priorities in terms of future developments and features to be added are decided in cooperation with the institutions providing financial support.

Documentation sources
=====================

The present document is the reference manual for Dynare. It documents all commands and features in a systematic fashion.

New users should rather begin with Dynare User Guide (Mancini (2007)), distributed with Dynare and also available from the official Dynare web site (http://www.dynare.org).

Other useful sources of information include the Dynare wiki (http://www.dynare.org) and the Dynare forums (http://www.dynare.org/phpBB3).

Citing Dynare in your research
==============================

If you would like to refer to Dynare in a research article, the recommended way is to cite the present manual, as follows:

     Stéphane Adjemian, Houtan Bastani, Michel Juillard, Ferhat Mihoubi, George Perendia, Marco Ratto and Sébastien Villemot (2011), ``Dynare: Reference Manual, Version 4,'' Dynare Working Papers, 1, CEPREMAP

Note that citing the Dynare Reference Manual in your research is a good way to help the Dynare project.

If you want to give a URL, use the address of the Dynare website: http://www.dynare.org.

Installation and configuration
******************************

Software requirements
=====================

Packaged versions of Dynare are available for Windows XP/Vista/Seven, Debian GNU/Linux (http://www.debian.org), Ubuntu (http://www.ubuntu.com/) and Mac OS X Leopard/Snow Leopard.  Dynare should work on other systems, but some compilation steps are necessary in that case.

In order to run Dynare, you need one of the following:

  * MATLAB version 7.0 (R14) or above;

  * GNU Octave version 3.2.2 or above.

Some installation instructions for GNU Octave can be found on the Dynare Wiki (http://www.dynare.org/DynareWiki/DynareOctave).

The following optional extensions are also useful to benefit from extra features, but are in no way required:

  * If under MATLAB: the optimization toolbox, the statistics toolbox, the control system toolbox;

  * If under GNU Octave, the following Octave-Forge (http://octave.sourceforge.net/) packages: optim, io, java, statistics, control.

If you plan to use the use_dll option of the model command, you will need to install the necessary requirements for compiling MEX files on your machine. If you are using MATLAB under Windows, install a C++ compiler on your machine and configure it with MATLAB: see instructions on the Dynare wiki (http://www.dynare.org/DynareWiki/ConfigureMatlabWindowsForMexCompilation). Users of Octave under Linux should install the package for MEX file compilation (under Debian or Ubuntu, it is called liboctave-dev or octave3.2-headers). If you are using Octave or MATLAB under Mac OS X, you should install the latest version of XCode: see instructions on the Dynare wiki (http://www.dynare.org/DynareWiki/InstallOnMacOSX). Mac OS X Octave users will also need to install gnuplot if they want graphing capabilities. Users of MATLAB under Linux and Mac OS X, and users of Octave under Windows, normally need to do nothing, since a working compilation environment is available by default.

Installation of Dynare
======================

After installation, Dynare can be used in any directory on your computer. It is best practice to keep your model files in directories different from the one containing the Dynare toolbox. That way you can upgrade Dynare and discard the previous version without having to worry about your own files.

On Windows
----------

Execute the automated installer called dynare-4.X.Y-win.exe (where 4.X.Y is the version number), and follow the instructions. The default installation directory is c:\dynare\4.X.Y.

After installation, this directory will contain several sub-directories, among which are matlab, mex and doc.

The installer will also add an entry in your Start Menu with a shortcut to the documentation files and uninstaller.

Note that you can have several versions of Dynare coexisting (for example in c:\dynare), as long as you correctly adjust your path settings (see Some words of warning).

On Debian GNU/Linux and Ubuntu
------------------------------

Please refer to the Dynare Wiki (http://www.dynare.org/DynareWiki/InstallOnDebianOrUbuntu) for detailed instructions.

Dynare will be installed under /usr/share/dynare and /usr/lib/dynare. Documentation will be under /usr/share/doc/dynare.

On Mac OS X
-----------

Execute the automated installer called dynare-4.X.Y-macosx-10.5+10.6.pkg (where 4.X.Y is the version number), and follow the instructions. The default installation directory is /Applications/Dynare/4.X.Y.

Please refer to the Dynare Wiki (http://www.dynare.org/DynareWiki/InstallOnMacOSX) for detailed instructions.

After installation, this directory will contain several sub-directories, among which are matlab, mex and doc.

Note that you can have several versions of Dynare coexisting (for example in /Applications/Dynare), as long as you correctly adjust your path settings (see Some words of warning).

For other systems
-----------------

You need to download Dynare source code from the Dynare website (http://www.dynare.org) and unpack it somewhere.

Then you will need to recompile the pre-processor and the dynamic loadable libraries. Please refer to Dynare Wiki (http://www.dynare.org/DynareWiki/BuildingDynareFromSource).

Configuration
=============

For MATLAB
----------

You need to add the matlab subdirectory of your Dynare installation to MATLAB path. You have two options for doing that:

  * Using the addpath command in the MATLAB command window:

     Under Windows, assuming that you have installed Dynare in the standard location, and replacing 4.X.Y with the correct version number, type:

          addpath c:\dynare\4.X.Y\matlab

     Under Debian GNU/Linux or Ubuntu, type:

          addpath /usr/share/dynare/matlab

     Under Mac OS X, assuming that you have installed Dynare in the standard location, and replacing 4.X.Y with the correct version number, type:

          addpath /Applications/Dynare/4.X.Y/matlab

     MATLAB will not remember this setting next time you run it, and you will have to do it again.

  * Via the menu entries:

     Select the ``Set Path'' entry in the ``File'' menu, then click on ``Add Folder...'', and select the matlab subdirectory of your Dynare installation. Note that you should not use ``Add with Subfolders...''. Apply the settings by clicking on ``Save''. Note that MATLAB will remember this setting next time you run it.

For GNU Octave
--------------

You need to add the matlab subdirectory of your Dynare installation to Octave path, using the addpath at the Octave command prompt.

Under Windows, assuming that you have installed Dynare in the standard location, and replacing ``4.X.Y'' with the correct version number, type:

     addpath c:\dynare\4.X.Y\matlab

Under Debian GNU/Linux or Ubuntu, there is no need to use the addpath command; the packaging does it for you.

Under Mac OS X, assuming that you have installed Dynare in the standard location, and replacing ``4.X.Y'' with the correct version number, type:

     addpath /Applications/Dynare/4.X.Y/matlab

If you are using an Octave version strictly older than 3.2.0, you will also want to tell to Octave to accept the short syntax (without parentheses and quotes) for the dynare command, by typing:

     mark_as_command dynare

If you don't want to type this command every time you run Octave, you can put it in a file called .octaverc in your home directory (under Windows this will generally by c:\Documents and Settings\USERNAME\). This file is run by Octave at every startup.

Some words of warning
---------------------

You should be very careful about the content of your MATLAB or Octave path. You can display its content by simply typing path in the command window.

The path should normally contain system directories of MATLAB or Octave, and some subdirectories of your Dynare installation. You have to manually add the matlab subdirectory, and Dynare will automatically add a few other subdirectories at runtime (depending on your configuration). You must verify that there is no directory coming from another version of Dynare than the one you are planning to use.

You have to be aware that adding other directories to your path can potentially create problems, if some of your M-files have the same names than Dynare files. Your files would then override Dynare files, and make Dynare unusable.

Dynare invocation
*****************

In order to give instructions to Dynare, the user has to write a model file whose filename extension must be .mod. This file contains the description of the model and the computing tasks required by the user. Its contents is described in The Model file.

Once the model file is written, Dynare is invoked using the dynare command at the MATLAB or Octave prompt (with the filename of the .mod given as argument).

In practice, the handling of the model file is done in two steps: in the first one, the model and the processing instructions written by the user in a model file are interpreted and the proper MATLAB or GNU Octave instructions are generated; in the second step, the program actually runs the computations. Boths steps are triggered automatically by the dynare command.

 -- MATLAB/Octave command: dynare FILENAME[.mod] [OPTIONS...]

     Description

     This command launches Dynare and executes the instructions included in FILENAME.mod.  This user-supplied file contains the model and the processing instructions, as described in The Model file.

     dynare begins by launching the preprocessor on the .mod file.  By default (unless use_dll option has been given to model), the preprocessor creates three intermediary files:

     FILENAME.m
          Contains variable declarations, and computing tasks

     FILENAME_dynamic.m
          Contains the dynamic model equations

     FILENAME_static.m
          Contains the long run static model equations

     These files may be looked at to understand errors reported at the simulation stage.

     dynare will then run the computing tasks by executing FILENAME.m.

     Options

     noclearall
          By default, dynare will issue a clear all command to MATLAB or Octave, thereby deleting all workspace variables; this options instructs dynare not to clear the workspace

     debug
          Instructs the preprocessor to write some debugging information about the scanning and parsing of the .mod file

     notmpterms
          Instructs the preprocessor to omit temporary terms in the static and dynamic files; this generally decreases performance, but is used for debugging purposes since it makes the static and dynamic files more readable

     savemacro[=FILENAME]
          Instructs dynare to save the intermediary file which is obtained after macro-processing (see Macro-processing language); the saved output will go in the file specified, or if no file is specified in FILENAME-macroexp.mod

     onlymacro
          Instructs the preprocessor to only perform the macro-processing step, and stop just after. Mainly useful for debugging purposes or for using the macro-processor independently of the rest of Dynare toolbox.

     nolinemacro
          Instructs the macro-preprocessor to omit line numbering information in the intermediary .mod file created after the maco-processing step. Useful in conjunction with savemacro when one wants that to reuse the intermediary .mod file, without having it cluttered by line numbering directives.

     nolog
          Instructs Dynare to no create a logfile of this run in FILENAME.log. The default is to create the logfile.

     warn_uninit
          Display a warning for each variable or parameter which is not initialized. See Parameter initialization, or load_params_and_steady_state for initialization of parameters. See Initial and terminal conditions, or load_params_and_steady_state for initialization of endogenous and exogenous variables.

     console
          Activate console mode: Dynare will not use graphical waitbars for long computations. Note that this option is only useful under MATLAB, since Octave does not provide graphical waitbar capabilities.

     cygwin
          Tells Dynare that your MATLAB is configured for compiling MEX files with Cygwin (see Software requirements). This option is only available under Windows, and is used in conjunction with use_dll.

     msvc
          Tells Dynare that your MATLAB is configured for compiling MEX files with Microsoft Visual C++ (see Software requirements). This option is only available under Windows, and is used in conjunction with use_dll.

     parallel[=CLUSTER_NAME]
          Tells Dynare to perform computations in parallel. If CLUSTER_NAME is passed, Dynare will use the specified cluster to perform parallel computations. Otherwise, Dynare will use the first cluster specified in the configuration file. See The Configuration File, for more information about the configuration file.

     conffile=FILENAME
          Specifies the location of the configuration file if it differs from the default. See The Configuration File, for more information about the configuration file and its default location.

     parallel_slave_open_mode
          Instructs Dynare to leave the connection to the slave node open after computation is complete, closing this connection only when Dynare finishes processing.

     parallel_test
          Tests the parallel setup specified in the configuration file without executing the .mod file. See The Configuration File, for more information about the configuration file.

     -DMACRO_VARIABLE=MACRO_EXPRESSION
          Defines a macro-variable from the command line (the same effect as using the Macro directive @#define in a model file, see Macro-processing language).

     Output

     Depending on the computing tasks requested in the .mod file, executing command dynare will leave in the workspace variables containing results available for further processing. More details are given under the relevant computing tasks.

     The M_, oo_ and options_ structures are also saved in a file called FILENAME_results.mat.

     Example

          dynare ramst
          dynare ramst.mod savemacro

The output of Dynare is left into three main variables in the MATLAB/Octave workspace:

 -- MATLAB/Octave variable: M_
     Structure containing various informations about the model.

 -- MATLAB/Octave variable: options_
     Structure contains the values of the various options used by Dynare during the computation.

 -- MATLAB/Octave variable: oo_
     Structure containing the various results of the computations.

The Model file
**************

Conventions
===========

A model file contains a list of commands and of blocks.  Each command and each element of a block is terminated by a semicolon (;). Blocks are terminated by end;.

Most Dynare commands have arguments and several accept options, indicated in parentheses after the command keyword. Several options are separated by commas.

In the description of Dynare commands, the following conventions are observed:

  * optional arguments or options are indicated between square brackets: [];

  * repreated arguments are indicated by ellipses: ``...'';

  * mutually exclusive arguments are separated by vertical bars: |;

  * INTEGER indicates an integer number;

  * DOUBLE indicates a double precision number. The following syntaxes are valid: 1.1e3, 1.1E3, 1.1d3, 1.1D3;

  * NUMERICAL_VECTOR indicates a vector of numbers separated by spaces, enclosed by square brackets;

  * EXPRESSION indicates a mathematical expression valid outside the model description (see Expressions);

  * MODEL_EXPRESSION indicates a mathematical expression valid in the model description (see Expressions and Model declaration);

  * MACRO_EXPRESSION designates an expression of the macro-processor (see Macro expressions);

  * VARIABLE_NAME indicates a variable name starting with an alphabetical character and can't contain: ()+-*/^=!;:@#. or accentuated characters;

  * PARAMETER_NAME indicates a parameter name starting with an alphabetical character and can't contain: ()+-*/^=!;:@#. or accentuated characters;

  * LATEX_NAME indicates a valid LaTeX expression in math mode (not including the dollar signs);

  * FUNCTION_NAME indicates a valid MATLAB function name;

  * FILENAME indicates a filename valid in the underlying operating system; it is necessary to put it between quotes when specifying the extension or if the filename contains a non-alphanumeric character;

Variable declarations
=====================

Declarations of variables and parameters are made with the following commands:

 -- Command: var VARIABLE_NAME [$LATEX_NAME$]...;
 -- Command: var (deflator = MODEL_EXPRESSION) VARIABLE_NAME [$LATEX_NAME$]...;

     Description

     This required command declares the endogenous variables in the model. See Conventions, for the syntax of VARIABLE_NAME and MODEL_EXPRESSION. Optionally it is possible to give a LaTeX name to the variable or, if it is nonstationary, provide information regarding its deflator.

     var commands can appear several times in the file and Dynare will concatenate them.

     Options

     If the model is nonstationary and is to be written as such in the model block, Dynare will need the trend deflator for the appropriate endogenous variables in order to stationarize the model. The trend deflator must be provided alongside the variables that follow this trend.

     deflator = MODEL_EXPRESSION
          The expression used to detrend an endogenous variable. All trend variables, endogenous variables and parameters referenced in MODEL_EXPRESSION must already have been declared by the trend_var, var and parameters commands.

     Example

          var c gnp q1 q2;
          var(deflator=A) i b;

 -- Command: varexo VARIABLE_NAME [$LATEX_NAME$]...;

     Description

     This optional command declares the exogenous variables in the model. See Conventions, for the syntax of VARIABLE_NAME. Optionally it is possible to give a LaTeX name to the variable.

     Exogenous variables are required if the user wants to be able to apply shocks to her model.

     varexo commands can appear several times in the file and Dynare will concatenate them.

     Example

          varexo m gov;

 -- Command: varexo_det VARIABLE_NAME [$LATEX_NAME$]...;

     Description

     This optional command declares exogenous deterministic variables in a stochastic model. See Conventions, for the syntax of VARIABLE_NAME. Optionally it is possible to give a LaTeX name to the variable.

     It is possible to mix deterministic and stochastic shocks to build models where agents know from the start of the simulation about future exogenous changes. In that case stoch_simul will compute the rational expectation solution adding future information to the state space (nothing is shown in the output of stoch_simul) and forecast will compute a simulation conditional on initial conditions and future information.

     varexo_det commands can appear several times in the file and Dynare will concatenate them.

     Example

          varexo m gov;
          varexo_det tau;

 -- Command: parameters PARAMETER_NAME [$LATEX_NAME$]...;

     Description

     This command declares parameters used in the model, in variable initialization or in shocks declarations. See Conventions, for the syntax of PARAMETER_NAME. Optionally it is possible to give a LaTeX name to the parameter.

     The parameters must subsequently be assigned values (see Parameter initialization).

     parameters commands can appear several times in the file and Dynare will concatenate them.

     Example

          parameters alpha, bet;

 -- Command: change_type (var | varexo | varexo_det | parameters) VARIABLE_NAME | PARAMETER_NAME...;

     Description

     Changes the types of the specified variables/parameters to another type: endogenous, exogenous, exogenous deterministic or parameter.

     It is important to understand that this command has a global effect on the .mod file: the type change is effective after, but also before, the change_type command. This command is typically used when flipping some variables for steady state calibration: typically a separate model file is used for calibration, which includes the list of variable declarations with the macro-processor, and flips some variable.

     Example

          var y, w;
          parameters alpha, bet;
          ...
          change_type(var) alpha, bet;
          change_type(parameters) y, w;

     Here, in the whole model file, alpha and beta will be endogenous and y and w will be parameters.

 -- Command: predetermined_variables VARIABLE_NAME...;

     Description

     In Dynare, the default convention is that the timing of a variable reflects when this variable is decided. The typical example is for capital stock: since the capital stock used at current period is actually decided at the previous period, then the capital stock entering the production function is k(-1), and the law of motion of capital must be written:

          k = i + (1-delta)*k(-1)

     Put another way, for stock variables, the default in Dynare is to use a ``stock at the end of the period'' concept, instead of a ``stock at the beginning of the period'' convention.

     The predetermined_variables is used to change that convention. The endogenous variables declared as predetermined variables are supposed to be decided one period ahead of all other endogenous variables. For stock variables, they are supposed to follow a ``stock at the beginning of the period'' convention.

     Example

     The following two program snippets are strictly equivalent.

     Using default Dynare timing convention:

          var y, k, i;
          ...
          model;
          y = k(-1)^alpha;
          k = i + (1-delta)*k(-1);
          ...
          end;

     Using the alternative timing convention:

          var y, k, i;
          predetermined_variables k;
          ...
          model;
          y = k^alpha;
          k(+1) = i + (1-delta)*k;
          ...
          end;

 -- Command: trend_var (growth_factor = MODEL_EXPRESSION) VARIABLE_NAME [$LATEX_NAME$]...;

     Description

     This optional command declares the trend variables in the model. See Conventions, for the syntax of MODEL_EXPRESSION and VARIABLE_NAME. Optionally it is possible to give a LaTeX name to the variable.

     Trend variables are required if the user wants to be able to write a nonstationary model in the model block. The trend_var command must appear before the var command that references the trend variable.

     trend_var commands can appear several times in the file and Dynare will concatenate them.

     If the model is nonstationary and is to be written as such in the model block, Dynare will need the growth factor of every trend variable in order to stationarize the model. The growth factor must be provided within the declaration of the trend variable, using the growth_factor keyword. All endogenous variables and parameters referenced in MODEL_EXPRESSION must already have been declared by the var and parameters commands.

     Example

          trend_var (growth_factor=gA) A;

Expressions
===========

Dynare distinguishes between two types of mathematical expressions: those that are used to describe the model, and those that are used outside the model block (e.g. for initializing parameters or variables, or as command options). In this manual, those two types of expressions are respectively denoted by MODEL_EXPRESSION and EXPRESSION.

Unlike MATLAB or Octave expressions, Dynare expressions are necessarily scalar ones: they cannot contain matrices or evaluate to matrices (Note that arbitrary MATLAB or Octave expressions can be put in a .mod file, but those expressions have to be on separate lines, generally at the end of the file for post-processing purposes. They are not interpreted by Dynare, and are simply passed on unmodified to MATLAB or Octave. Those constructions are not addresses in this section.).

Expressions can be constructed using integers (INTEGER), floating point numbers (DOUBLE), parameter names (PARAMETER_NAME), variable names (VARIABLE_NAME), operators and functions.

The following special constants are also accepted in some contexts:

 -- Constant: inf
     Represents infinity.

 -- Constant: nan
     ``Not a number'': represents an undefined or unrepresentable value.

Parameters and variables
------------------------

Parameters and variables can be introduced in expressions by simply typing their names. The semantics of parameters and variables is quite different whether they are used inside or outside the model block.

Inside the model
................

Parameters used inside the model refer to the value given through parameter initialization (see Parameter initialization) or homotopy_setup when doing a simulation, or are the estimated variables when doing an estimation.

Variables used in a MODEL_EXPRESSION denote current period values when neither a lead or a lag is given. A lead or a lag can be given by enclosing an integer between parenthesis just after the variable name: a positive integer means a lead, a negative one means a lag. Leads or lags of more than one period are allowed. For example, if c is an endogenous variable, then c(+1) is the variable one period ahead, and c(-2) is the variable two periods before.

When specifying the leads and lags of endogenous variables, it is important to respect the following convention: in Dynare, the timing of a variable reflects when that variable is decided. A control variable --- which by definition is decided in the current period --- must have no lead. A predetermined variable --- which by definition has been decided in a previous period --- must have a lag. A consequence of this is that all stock variables must use the ``stock at the end of the period'' convention. Please refer to Mancini-Griffoli (2007) for more details and concrete examples.

Leads and lags are primarily used for endogenous variables, but can be used for exogenous variables. They have no effect on parameters and are forbidden for local model variables (see Model declaration).

Outside the model
.................

When used in an expression outside the model block, a parameter or a variable simply refers to the last value given to that variable. More precisely, for a parameter it refers to the value given in the corresponding parameter initialization (see Parameter initialization); for an endogenous or exogenous variable, it refers to the value given in the most recent initval or endval block.

Operators
---------

The following operators are allowed in both MODEL_EXPRESSION and EXPRESSION:

  * binary arithmetic operators: +, -, *, /, ^

  * unary arithmetic operators: +, -

  * binary comparison operators (which evaluate to either 0 or 1): <, >, <=, >=, ==, !=

The following special operators are accepted in MODEL_EXPRESSION (but not in EXPRESSION):

 -- Operator: STEADY_STATE (MODEL_EXPRESSION)
     This operator is used to take the value of the enclosed expression at the steady state. A typical usage is in the Taylor rule, where you may want to use the value of GDP at steady state to compute the output gap.

 -- Operator: EXPECTATION (INTEGER) (MODEL_EXPRESSION)
     This operator is used to take the expectation of some expression using a different information set than the information available at current period. For example, EXPECTATION(-1)(x(+1)) is equal to the expected value of variable x at next period, using the information set available at the previous period. See Auxiliary variables, for an explanation of how this operator is handled internally and how this affects the output.

Functions
---------

Built-in Functions
..................

The following standard functions are supported internally for both MODEL_EXPRESSION and EXPRESSION:

 -- Function: exp (X)
     Natural exponential.

 -- Function: log (X)
 -- Function: ln (X)
     Natural logarithm.

 -- Function: log10 (X)
     Base 10 logarithm.

 -- Function: sqrt (X)
     Square root.

 -- Function: abs (X)
     Absolute value.

 -- Function: sign (X)
     Signum function.

 -- Function: sin (X)
 -- Function: cos (X)
 -- Function: tan (X)
 -- Function: asin (X)
 -- Function: acos (X)
 -- Function: atan (X)
     Trigonometric functions.

 -- Function: max (A, B)
 -- Function: min (A, B)
     Maximum and minimum of two reals.

 -- Function: normcdf (X)
 -- Function: normcdf (X, MU, SIGMA)
     Gaussian cumulative density function, with mean MU and standard deviation SIGMA. Note that normcdf(X) is equivalent to normcdf(X,0,1).

 -- Function: normpdf (X)
 -- Function: normpdf (X, MU, SIGMA)
     Gaussian probability density function, with mean MU and standard deviation SIGMA. Note that normpdf(X) is equivalent to normpdf(X,0,1).

 -- Function: erf (X)
     Gauss error function.

External Functions
..................

Any other user-defined (or built-in) MATLAB or Octave function may be used in both a MODEL_EXPRESSION and an EXPRESSION, provided that this function has a scalar argument as a return value.

To use an external function in a MODEL_EXPRESSION, one must declare the function using the external_function statement. This is not necessary for external functions used in an EXPRESSION.

 -- Command: external_function (OPTIONS...);

     Description

     This command declares the external functions used in the model block. It is required for every unique function used in the model block.

     external_function commands can appear several times in the file and must come before the model block.

     Options

     name = NAME
          The name of the function, which must also be the name of the M-/MEX file implementing it. This option is mandatory.

     nargs = INTEGER
          The number of arguments of the function. If this option is not provided, Dynare assumes nargs = 1.

     first_deriv_provided [= NAME]
          If NAME is provided, this tells Dynare that the Jacobian is provided as the only output of the M-/MEX file given as the option argument. If NAME is not provided, this tells Dynare that the M-/MEX file specified by the argument passed to name returns the Jacobian as its second output argument.

     second_deriv_provided [= NAME]
          If NAME is provided, this tells Dynare that the Hessian is provided as the only output of the M-/MEX file given as the option argument. If NAME is not provided, this tells Dynare that the M-/MEX file specified by the argument passed to name returns the Hessian as its third output argument. NB: This option can only be used if the first_deriv_provided option is used in the same external_function command.

     Example

          external_function(name = funcname);
          external_function(name = otherfuncname, nargs = 2,
                            first_deriv_provided, second_deriv_provided);
          external_function(name = yetotherfuncname, nargs = 3,
                            first_deriv_provided = funcname_deriv);

Parameter initialization
========================

When using Dynare for computing simulations, it is necessary to calibrate the parameters of the model. This is done through parameter initialization.

The syntax is the following:

     PARAMETER_NAME = EXPRESSION;

Here is an example of calibration:

     parameters alpha, bet;

     beta = 0.99;
     alpha = 0.36;
     A = 1-alpha*beta;

Internally, the parameter values are stored in M_.params:

 -- MATLAB/Octave variable: M_.params

     Contains the values of model parameters. The parameters are in the order that was used in the parameters command.

Model declaration
=================

The model is declared inside a model block:

 -- Block: model ;
 -- Block: model (OPTIONS...);

     Description

     The equations of the model are written in a block delimited by model and end keywords.

     There must be as many equations as there are endogenous variables in the model, except when computing the unconstrained optimal policy with ramsey_policy or discretionary_policy.

     The syntax of equations must follow the conventions for MODEL_EXPRESSION as described in Expressions. Each equation must be terminated by a semicolon (;). A normal equation looks like:

          MODEL_EXPRESSION = MODEL_EXPRESSION;

     When the equations are written in homogenous form, it is possible to omit the =0 part and write only the left hand side of the equation. A homogenous equation looks like:

          MODEL_EXPRESSION;

     Inside the model block, Dynare allows the creation of model-local variables, which constitute a simple way to share a common expression between several equations. The syntax consists of a pound sign (#) followed by the name of the new model local variable (which must not be declared as in Variable declarations), an equal sign, and the expression for which this new variable will stand. Later on, every time this variable appears in the model, Dynare will substitute it by the expression assigned to the variable. Note that the scope of this variable is restricted to the model block; it cannot be used outside. A model local variable declaration looks like:

          # VARIABLE_NAME = MODEL_EXPRESSION;

     Options

     linear
          Declares the model as being linear. It spares oneself from having to declare initial values for computing the steady state, and it sets automatically order=1 in stoch_simul.

     use_dll
          Instructs the preprocessor to create dynamic loadable libraries (DLL) containing the model equations and derivatives, instead of writing those in M-files. You need a working compilation environment, i.e. a working mex command (see Software requirements for more details). Using this option can result in faster simulations or estimations, at the expense of some initial compilation time. (In particular, for big models, the compilation step can be very time-consuming, and use of this option may be counter-productive in those cases.)

     block
          Perform the block decomposition of the model, and exploit it in computations. See Dynare wiki (http://www.dynare.org/DynareWiki/FastDeterministicSimulationAndSteadyStateComputation) for details on the algorithm.

     bytecode
          Instead of M-files, use a bytecode representation of the model, i.e. a binary file containing a compact representation of all the equations.

     cutoff = DOUBLE
          Threshold under which a jacobian element is considered as null during the model normalization. Only available with option block. Default: 1e-15

     mfs = INTEGER
          Controls the handling of minimum feedback set of endogenous variables. Only available with option block. Possible values:

          0
               All the endogenous variables are considered as feedback variables (Default).

          1
               The endogenous variables assigned to equation naturally normalized (i.e. of the form x=f(Y) where x does not appear in Y) are potentially recursive variables. All the other variables are forced to belong to the set of feedback variables.

          2
               In addition of variables with mfs = 1 the endogenous variables related to linear equations which could be normalized are potential recursive variables. All the other variables are forced to belong to the set of feedback variables.

          3
               In addition of variables with mfs = 2 the endogenous variables related to non-linear equations which could be normalized are potential recursive variables. All the other variables are forced to belong to the set of feedback variables.

     no_static
          Don't create the static model file. This can be useful for models which don't have a steady state.

     Example 1: elementary RBC model

          var c k;
          varexo x;
          parameters aa alph bet delt gam;

          model;
          c =  - k + aa*x*k(-1)^alph + (1-delt)*k(-1);
          c^(-gam) = (aa*alph*x(+1)*k^(alph-1) + 1 - delt)*c(+1)^(-gam)/(1+bet);
          end;

     Example 2: use of model local variables

     The following program:

          model;
          # gamma = 1 - 1/sigma;
          u1 = c1^gamma/gamma;
          u2 = c2^gamma/gamma;
          end;

     ...is formally equivalent to:

          model;
          u1 = c1^(1-1/sigma)/(1-1/sigma);
          u2 = c2^(1-1/sigma)/(1-1/sigma);
          end;

     Example 3: a linear model

          model(linear);
          x = a*x(-1)+b*y(+1)+e_x;
          y = d*y(-1)+e_y;
          end;

Dynare has the ability to output the list of model equations to a LaTeX file, using the write_latex_dynamic_model command. The static model can also be written with the write_latex_static_model command.

 -- Command: write_latex_dynamic_model ;

     Description

     This command creates a LaTeX file containing the (dynamic) model.

     If your .mod file is FILENAME.mod, then Dynare will create a file called FILENAME_dynamic.tex, containing the list of all the dynamic model equations.

     If LaTeX names were given for variables and parameters (see Variable declarations), then those will be used; otherwise, the plain text names will be used.

     Time subscripts (t, t+1, t-1, ...) will be appended to the variable names, as LaTeX subscripts.

     Note that the model written in the TeX file will differ from the model declared by the user in the following dimensions:

       * the timing convention of predetermined variables (see predetermined_variables) will have been changed to the default Dynare timing convention; in other words, variables declared as predetermined will be lagged on period back,

       * the expectation operators (see expectation) will have been removed, replaced by auxiliary variables and new equations as explained in the documentation of the operator,

       * endogenous variables with leads or lags greater or equal than two will have been removed, replaced by new auxiliary variables and equations,

       * for a stochastic model, exogenous variables with leads or lags will also have been replaced by new auxiliary variables and equations.

     Compiling the TeX file requires the following Latex packages: geometry, fullpage, breqn.

 -- Command: write_latex_static_model ;

     Description

     This command creates a LaTeX file containing the static model.

     If your .mod file is FILENAME.mod, then Dynare will create a file called FILENAME_static.tex, containing the list of all the equations of the steady state model.

     If LaTeX names were given for variables and parameters (see Variable declarations), then those will be used; otherwise, the plain text names will be used.

     Note that the model written in the TeX file will differ from the model declared by the user in the some dimensions (see write_latex_dynamic_model for details).

     Also note that this command will not output the contents of the optional steady_state_model block (see steady_state_model); it will rather output a static version (i.e. without leads and lags) of the dynamic model declared in the model block.

     Compiling the TeX file requires the following Latex packages: geometry, fullpage, breqn.

Auxiliary variables
===================

The model which is solved internally by Dynare is not exactly the model declared by the user. In some cases, Dynare will introduce auxiliary endogenous variables---along with corresponding auxiliary equations---which will appear in the final output.

The main transformation concerns leads and lags. Dynare will perform a transformation of the model so that there is only one lead and one lag on endogenous variables and, in the case of a stochastic model, no leads/lags on exogenous variables.

This transformation is achieved by the creation of auxiliary variables and corresponding equations. For example, if x(+2) exists in the model, Dynare will create one auxiliary variable AUX_ENDO_LEAD = x(+1), and replace x(+2) by AUX_ENDO_LEAD(+1).

A similar transformation is done for lags greater than 2 on endogenous (auxiliary variables will have a name beginning with AUX_ENDO_LAG), and for exogenous with leads and lags (auxiliary variables will have a name beginning with AUX_EXO_LEAD or AUX_EXO_LAG respectively).

Another transformation is done for the EXPECTATION operator. For each occurence of this operator, Dynare creates an auxiliary variable defined by a new equation, and replaces the expectation operator by a reference to the new auxiliary variable. For example, the expression EXPECTATION(-1)(x(+1)) is replaced by AUX_EXPECT_LAG_1(-1), and the new auxiliary variable is declared as AUX_EXPECT_LAG_1 = x(+2).

Auxiliary variables are also introduced by the preprocessor for the ramsey_policy command. In this case, they are used to represent the Lagrange multipliers when first order conditions of the Ramsey problem are computed. The new variables take the form MULT_I, where I represents the constraint with which the multiplier is associated (counted from the order of declaration in the model block).

Once created, all auxiliary variables are included in the set of endogenous variables. The output of decision rules (see below) is such that auxiliary variable names are replaced by the original variables they refer to.

The number of endogenous variables before the creation of auxiliary variables is stored in M_.orig_endo_nbr, and the number of endogenous variables after the creation of auxiliary variables is stored in M_.endo_nbr.

See Dynare Wiki (http://www.dynare.org/DynareWiki/AuxiliaryVariables) for more technical details on auxiliary variables.

Initial and terminal conditions
===============================

For most simulation exercises, it is necessary to provide initial (and possibly terminal) conditions. It is also necessary to provide initial guess values for non-linear solvers. This section describes the statements used for those purposes.

In many contexts (determistic or stochastic), it is necessary to compute the steady state of a non-linear model: initval then specifies numerical initial values for the non-linear solver. The command resid can be used to compute the equation residuals for the given initial values.

Used in perfect foresight mode, the types of forward-loking models for which Dynare was designed require both initial and terminal conditions. Most often these initial and terminal conditions are static equilibria, but not necessarily.

One typical application is to consider an economy at the equilibrium, trigger a shock in first period, and study the trajectory of return at the initial equilbrium. To do that, one needs initval and shocks (see Shocks on exogenous variables.

Another one is to study, how an economy, starting from arbitrary initial conditions converges toward equilibrium. To do that, one needs initval and endval.

For models with lags on more than one period, the command histval permits to specify different historical initial values for periods before the beginning of the simulation.

 -- Block: initval ;

     Description

     The initval block serves two purposes: declaring the initial (and possibly terminal) conditions in a simulation exercise, and providing guess values for non-linear solvers.

     This block is terminated by end;, and contains lines of the form:

          VARIABLE_NAME = EXPRESSION;

     In a deterministic (i.e. perfect foresight) model

     First, it provides the initial conditions for all the endogenous and exogenous variables at all the periods preceeding the first simulation period (unless some of these initial values are modified by histval).

     Second, in the absence of an endval block, it sets the terminal conditions for all the periods succeeding the last simulation period.

     Third, in the absence of an endval block, it provides initial guess values at all simulation dates for the non-linear solver implemented in simul.

     For this last reason, it necessary to provide values for all the endogenous variables in an initval block (even though, theoretically, initial conditions are only necessary for lagged variables). If some exogenous variables are not mentionned in the initval block, a zero value is assumed.

     Note that if the initval block is immediately followed by a steady command, its semantics is changed. The steady command will compute the steady state of the model for all the endogenous variables, assuming that exogenous variables are kept constant to the value declared in the initval block, and using the values declared for the endogenous as initial guess values for the non-linear solver. An initval block followed by steady is formally equivalent to an initval block with the same values for the exogenous, and with the associated steady state values for the endogenous.

     In a stochastic model

     The main purpose of initval is to provide initial guess values for the non-linear solver in the steady state computation. Note that if the initval block is not followed by steady, the steady state computation will still be triggered by subsequent commands (stoch_simul, estimation...).

     It is not necessary to declare 0 as initial value for exogenous stochastic variables, since it is the only possible value.

     This steady state will be used as the initial condition at all the periods preceeding the first simulation period for the two possible types of simulations in stochastic mode:

       * in stoch_simul, if the periods options is specified

       * in forecast (in this case, note that it is still possible to modify some of these initial values with histval)

     Example

          initval;
          c = 1.2;
          k = 12;
          x = 1;
          end;

          steady;

 -- Block: endval ;

     Description

     This block is terminated by end;, and contains lines of the form:

          VARIABLE_NAME = EXPRESSION;

     The endval block makes only sense in a determistic model, and serves two purposes.

     First, it sets the terminal conditions for all the periods succeeding the last simulation period.

     Second, it provides initial guess values at all the simulation dates for the non-linear solver implemented in simul.

     For this last reason, it necessary to provide values for all the endogenous variables in an endval block (even though, theoretically, initial conditions are only necessary for forward variables). If some exogenous variables are not mentionned in the endval block, a zero value is assumed.

     Note that if the endval block is immediately followed by a steady command, its semantics is changed. The steady command will compute the steady state of the model for all the endogenous variables, assuming that exogenous variables are kept constant to the value declared in the endval block, and using the values declared for the endogenous as initial guess values for the non-linear solver. An endval block followed by steady is formally equivalent to an endval block with the same values for the exogenous, and with the associated steady state values for the endogenous.

     Example

          var c k;
          varexo x;
          ...
          initval;
          c = 1.2;
          k = 12;
          x = 1;
          end;

          steady;

          endval;
          c = 2;
          k = 20;
          x = 2;
          end;

          steady;

     The initial equilibrium is computed by steady for x=1, and the terminal one, for x=2.

 -- Block: histval ;

     Description

     In models with lags on more than one period, the histval block permits to specify different historical initial values for different periods.

     This block is terminated by end;, and contains lines of the form:

          VARIABLE_NAME(INTEGER) = EXPRESSION;

     EXPRESSION is any valid expression returning a numerical value and can contain already initialized variable names.

     By convention in Dynare, period 1 is the first period of the simulation. Going backward in time, the first period before the start of the simulation is period 0, then period -1, and so on.

     If your lagged variables are linked by identities, be careful to satisfy these identities when you set historical initial values.

     Variables not initialized in the histval block are assumed to have a value of zero at period 0 and before. Note that this behavior differs from the case where there is no histval block, where all variables are initialized at their steady state value at period 0 and before.

     Example

          var x y;
          varexo e;

          model;
          x = y(-1)^alpha*y(-2)^(1-alpha)+e;
          ...
          end;

          initval;
          x = 1;
          y = 1;
          e = 0.5;
          end;

          steady;

          histval;
          y(0) = 1.1;
          y(-1) = 0.9;
          end;

 -- Command: resid ;

     This command will display the residuals of the static equations of the model, using the values given for the endogenous in the last initval or endval block (or the steady state file if you provided one, see Steady state).

 -- Command: initval_file (filename = FILENAME);

     Description

     In a deterministic setup, this command is used to specify a path for all endogenous and exogenous variables. The length of these paths must be equal to the number of simulation periods, plus the number of leads and the number of lags of the model (for example, with 50 simulation periods, in a model with 2 lags and 1 lead, the paths must have a length of 53). Note that these paths cover two different things:

       * the constraints of the problem, which are given by the path for exogenous and the initial and terminal values for endogenous

       * the initial guess for the non-linear solver, which is given by the path for endogenous variables for the simulation periods (excluding initial and terminal conditions)

     The command accepts three file formats:

       * M-file (extension .m): for each endogenous and exogenous variable, the file must contain a row vector of the same name.

       * MAT-file (extension .mat): same as for M-files.

       * Excel file (extension .xls): for each endogenous and exogenous, the file must contain a column of the same name (supported under Octave if the io (http://octave.sourceforge.net/io/) and java (http://octave.sourceforge.net/java/) packages from Octave-Forge are installed, along with a Java Runtime Environment (http://www.java.com/download)).

     Warning

     The extension must be omitted in the command argument. Dynare will automatically figure out the extension and select the appropriate file type.

Shocks on exogenous variables
=============================

In a deterministic context, when one wants to study the transition of one equilibrium position to another, it is equivalent to analyze the consequences of a permanent shock and this in done in Dynare through the proper use of initval and endval.

Another typical experiment is to study the effects of a temporary shock after which the system goes back to the original equilibrium (if the model is stable...). A temporary shock is a temporary change of value of one or several exogenous variables in the model. Temporary shocks are specified with the command shocks.

In a stochastic framework, the exogenous variables take random values in each period. In Dynare, these random values follow a normal distribution with zero mean, but it belongs to the user to specify the variability of these shocks. The non-zero elements of the matrix of variance-covariance of the shocks can be entered with the shocks command. Or, the entire matrix can be direclty entered with Sigma_e (this use is however deprecated).

If the variance of an exogenous variable is set to zero, this variable will appear in the report on policy and transition functions, but isn't used in the computation of moments and of Impulse Response Functions. Setting a variance to zero is an easy way of removing an exogenous shock.

 -- Block: shocks ;

     In deterministic context

     For deterministic simulations, the shocks block specifies temporary changes in the value of exogenous variables. For permanent shocks, use an endval block.

     The block should contain one or more occurrences of the following group of three lines:

          var VARIABLE_NAME;
          periods INTEGER[:INTEGER] [[,] INTEGER[:INTEGER]]...;
          values DOUBLE | (EXPRESSION)  [[,] DOUBLE | (EXPRESSION) ]...;

     It is possible to specify shocks which last several periods and which can vary over time. The periods keyword accepts a list of several dates or date ranges, which must be matched by as many shock values in the values keyword. Note that a range in the periods keyword can be matched by only one value in the values keyword. If values represents a scalar, the same value applies to the whole range. If values represents a vector, it must have as many elements as there are periods in the range.

     Note that shock values are not restricted to numerical constants: arbitrary expressions are also allowed, but you have to enclose them inside parentheses.

     Here is an example:

          shocks;
          var e;
          periods 1;
          values 0.5;
          var u;
          periods 4:5;
          values 0;
          var v;
          periods 4:5 6 7:9;
          values 1 1.1 0.9;
          var w;
          periods 1 2;
          values (1+p) (exp(z));
          end;

     A second example with a vector of values:

          xx = [1.2; 1.3; 1];

          shocks;
          var e;
          periods 1:3;
          values (xx);
          end;

     In stochastic context

     For stochastic simulations, the shocks block specifies the non zero elements of the covariance matrix of the shocks of exogenous variables.

     You can use the following types of entries in the block:

     var VARIABLE_NAME; stderr EXPRESSION;
          Specifies the standard error of a variable.

     var VARIABLE_NAME = EXPRESSION;
          Specifies the variance error of a variable.

     var VARIABLE_NAME, VARIABLE_NAME = EXPRESSION;
          Specifies the covariance of two variables.

     corr VARIABLE_NAME, VARIABLE_NAME = EXPRESSION;
          Specifies the correlation of two variables.

     In an estimation context, it is also possible to specify variances and covariances on endogenous variables: in that case, these values are interpreted as the calibration of the measurement errors on these variables.

     Here is an example:

          shocks;
          var e = 0.000081;
          var u; stderr 0.009;
          corr e, u = 0.8;
          var v, w = 2;
          end;

     Mixing determininistic and stochastic shocks

     It is possible to mix deterministic and stochastic shocks to build models where agents know from the start of the simulation about future exogenous changes. In that case stoch_simul will compute the rational expectation solution adding future information to the state space (nothing is shown in the output of stoch_simul) and forecast will compute a simulation conditional on initial conditions and future information.

     Here is an example:

          varexo_det tau;
          varexo e;

          ...

          shocks;
          var e; stderr 0.01;
          var tau;
          periods 1:9;
          values -0.15;
          end;

          stoch_simul(irf=0);

          forecast;

 -- Block: mshocks ;

     The purpose of this block is similar to that of the shocks block for deterministic shocks, except that the numeric values given will be interpreted in a multiplicative way. For example, if a value of 1.05 is given as shock value for some exogenous at some date, it means 5% above its steady state value (as given by the last initval or endval block).

     The syntax is the same than shocks in a deterministic context.

     This command is only meaningful in two situations:

       * on exogenous variables with a non-zero steady state, in a deterministic setup,

       * on deterministic exogenous variables with a non-zero steady state, in a stochastic setup.

 -- Special variable: Sigma_e

     Warning

     The use of this special variable is deprecated and is strongly discouraged. You should use a shocks block instead.

     Description

     This special variable specifies directly the covariance matrix of the stochastic shocks, as an upper (or lower) triangular matrix. Dynare builds the corresponding symmetrix matrix. Each row of the triangular matrix, except the last one, must be terminated by a semi-colon ;. For a given element, an arbitrary EXPRESSION is allowed (instead of a simple constant), but in that case you need to enclose the expression in parentheses. The order of the covariances in the matrix is the same as the one used in the varexo declaration.

     Example

          varexo u, e;
          ...
          Sigma_e = [ 0.81 (phi*0.9*0.009);
                      0.000081];

     This sets the variance of u to 0.81, the variance of e to 0.000081, and the correlation between e and u to phi.

Other general declarations
==========================

 -- Command: dsample INTEGER [INTEGER];
     Reduces the number of periods considered in subsequent output commands.

 -- Command: periods INTEGER;

     Description

     This command is now deprecated (but will still work for older model files). It is not necessary when no simulation is performed and is replaced by an option periods in simul and stoch_simul.

     This command sets the number of periods in the simulation. The periods are numbered from 1 to INTEGER. In perfect foresight simulations, it is assumed that all future events are perfectly known at the beginning of period 1.

     Example

          periods 100;

Steady state
============

There are two ways of computing the steady state (i.e. the static equilibrium) of a model. The first way is to let Dynare compute the steady state using a nonlinear Newton-type solver; this should work for most models, and is relatively simple to use. The second way is to give more guidance to Dynare, using your knowledge of the model, by providing it with a ``steady state file''.

Finding the steady state with Dynare nonlinear solver
-----------------------------------------------------

 -- Command: steady ;
 -- Command: steady (OPTIONS...);

     Description

     This command computes the steady state of a model using a nonlinear Newton-type solver and displays it. When a steady state file is used steady displays the steady state and checks that it is a solution of the static model.

     More precisely, it computes the equilibrium value of the endogenous variables for the value of the exogenous variables specified in the previous initval or endval block.

     steady uses an iterative procedure and takes as initial guess the value of the endogenous variables set in the previous initval or endval block.

     For complicated models, finding good numerical initial values for the endogenous variables is the trickiest part of finding the equilibrium of that model. Often, it is better to start with a smaller model and add new variables one by one.

     Options

     maxit = INTEGER
          Determines the maximum number of iterations used in the non-linear solver. The default value of maxit is 10. The maxit option is shared with the simul command. So a change in maxit in a steady command will also be considered in the following simul commands.

     solve_algo = INTEGER
          Determines the non-linear solver to use. Possible values for the option are:

          0
               Use fsolve (under MATLAB, only available if you have the Optimization Toolbox; always available under Octave)

          1
               Use Dynare's own nonlinear equation solver

          2
               Splits the model into recursive blocks and solves each block in turn

          3
               Use Chris Sims' solver

          4
               Similar to value 2, except that it deals differently with nearly singular Jacobian

          5
               Newton algorithm with a sparse Gaussian elimination (SPE) (requires bytecode option, see Model declaration)

          6
               Newton algorithm with a sparse LU solver at each iteration (requires bytecode and/or block option, see Model declaration)

          7
               Newton algorithm with a Generalized Minimal Residual (GMRES) solver at each iteration (requires bytecode and/or block option, see Model declaration; not available under Octave)

          8
               Newton algorithm with a Stabilized Bi-Conjugate Gradient (BICGSTAB) solver at each iteration (requires bytecode and/or block option, see Model declaration)

          Default value is 2.

     homotopy_mode = INTEGER
          Use a homotopy (or divide-and-conquer) technique to solve for the steady state. If you use this option, you must specify a homotopy_setup block. This option can take three possible values:

          1
               In this mode, all the parameters are changed simultaneously, and the distance between the boudaries for each parameter is divided in as many intervals as there are steps (as defined by homotopy_steps option); the problem is solves as many times as there are steps.

          2
               Same as mode 1, except that only one parameter is changed at a time; the problem is solved as many times as steps times number of parameters.

          3
               Dynare tries first the most extreme values. If it fails to compute the steady state, the interval between initial and desired values is divided by two for all parameters. Every time that it is impossible to find a steady state, the previous interval is divided by two. When it succeeds to find a steady state, the previous interval is multiplied by two. In that last case homotopy_steps contains the maximum number of computations attempted before giving up.

     homotopy_steps = INTEGER
          Defines the number of steps when performing a homotopy. See homotopy_mode option for more details.

     homotopy_force_continue = INTEGER
          This option controls what happens when homotopy fails.

          0
               steady fails with an error message

          1
               steady keeps the values of the last homotopy step that was successful and continues. BE CAREFUL: parameters and/or exogenous variables are NOT at the value expected by the user

          Default is 0.

     nocheck
          Don't check the steady state values when they are provided explicitely either by a steady state file or a steady_state_model block. This is useful for models with unit roots as, in this case, the steady state is not unique or doesn't exist.

     Example

     See Initial and terminal conditions.

After computation, the steady state is available in the following variable:

 -- MATLAB/Octave variable: oo_.steady_state

     Contains the computed steady state.

     Endogenous variables are ordered in order of declaration used in var command (which is also the order used in M_.endo_names).

 -- Block: homotopy_setup ;

     Description

     This block is used to declare initial and final values when using a homotopy method. It is used in conjunction with the option homotopy_mode of the steady command.

     The idea of homotopy (also called divide-and-conquer by some authors) is to subdivide the problem of finding the steady state into smaller problems. It assumes that you know how to compute the steady state for a given set of parameters, and it helps you finding the steady state for another set of parameters, by incrementally moving from one to another set of parameters.

     The purpose of the homotopy_setup block is to declare the final (and possibly also the initial) values for the parameters or exogenous that will be changed during the homotopy. It should contain lines of the form:

          VARIABLE_NAME, EXPRESSION, EXPRESSION;

     This syntax specifies the initial and final values of a given parameter/exogenous.

     There is an alternative syntax:

          VARIABLE_NAME, EXPRESSION;

     Here only the final value is specified for a given parameter/exogenous; the initial value is taken from the preceeding initval block.

     A necessary condition for a successful homotopy is that Dynare must be able to solve the steady state for the initial parameters/exogenous without additional help (using the guess values given in the initval block).

     If the homotopy fails, a possible solution is to increase the number of steps (given in homotopy_steps option of steady).

     Example

     In the following example, Dynare will first compute the steady state for the initial values (gam=0.5 and x=1), and then subdivide the problem into 50 smaller problems to find the steady state for the final values (gam=2 and x=2).

          var c k;
          varexo x;

          parameters alph gam delt bet aa;
          alph=0.5;
          delt=0.02;
          aa=0.5;
          bet=0.05;

          model;
          c + k - aa*x*k(-1)^alph - (1-delt)*k(-1);
          c^(-gam) - (1+bet)^(-1)*(aa*alph*x(+1)*k^(alph-1) + 1 - delt)*c(+1)^(-gam);
          end;

          initval;
          x = 1;
          k = ((delt+bet)/(aa*x*alph))^(1/(alph-1));
          c = aa*x*k^alph-delt*k;
          end;

          homotopy_setup;
          gam, 0.5, 2;
          x, 2;
          end;

          steady(homotopy_mode = 1, homotopy_steps = 50);

Using a steady state file
-------------------------

If you know how to compute the steady state for your model, you can provide a MATLAB/Octave function doing the computation instead of using steady. If your MOD-file is called FILENAME.mod, the steady state file should be called FILENAME_steadystate.m.

Again, there are two options for creating this file:

  * The easiest way is to write a steady_state_model block.

  * You can write the corresponding Matlab function by hand. See fs2000_steadystate.m in the examples directory for an example. This option gives a bit more flexibility, at the expense of a heavier programming burden and a lesser efficiency.

 -- Block: steady_state_model ;

     Description

     When the analytical solution of the model is known, this command can be used to help Dynare find the steady state in a more efficient and reliable way, especially during estimation where the steady state has to be recomputed for every point in the parameter space.

     Each line of this block consists of a variable (either an endogenous, a temporary variable or a parameter) which is assigned an expression (which can contain parameters, exogenous at the steady state, or any endogenous or temporary variable already declared above). Each line therefore looks like:

          VARIABLE_NAME = EXPRESSION;

     Note that it is also possible to assign several variables at the same time, if the main function in the right hand side is a MATLAB/Octave function returning several arguments:

          [ VARIABLE_NAME, VARIABLE_NAME... ] = EXPRESSION;

     Dynare will automatically generate a steady state file using the information provided in this block.

     Steady state file for deterministic models

     steady_state_model block works also with deterministic models. An initval block and, when necessary, an endval block, is used to set the value of the exogenous variables. Each initval or endval block must be followed by steady to execute the function created by steady_state_model and set the initial, respectively terminal, steady state.

     Example

          var m P c e W R k d n l gy_obs gp_obs y dA;
          varexo e_a e_m;

          parameters alp bet gam mst rho psi del;

          ...
          // parameter calibration, (dynamic) model declaration, shock calibration...
          ...

          steady_state_model;
            dA = exp(gam);
            gst = 1/dA; // A temporary variable
            m = mst;

            // Three other temporary variables
            khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
            xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
            nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );

            n  = xist/(nust+xist);
            P  = xist + nust;
            k  = khst*n;

            l  = psi*mst*n/( (1-psi)*(1-n) );
            c  = mst/P;
            d  = l - mst + 1;
            y  = k^alp*n^(1-alp)*gst^alp;
            R  = mst/bet;

            // You can use MATLAB functions which return several arguments
            [W, e] = my_function(l, n);

            gp_obs = m/dA;
            gy_obs = dA;
          end;

          steady;

Getting information about the model
===================================

 -- Command: check ;
 -- Command: check (solve_algo = INTEGER) ;

     Description

     Computes the eigenvalues of the model linearized around the values specified by the last initval, endval or steady statement. Generally, the eigenvalues are only meaningful if the linearization is done around a steady state of the model. It is a device for local analysis in the neighborhood of this steady state.

     A necessary condition for the uniqueness of a stable equilibrium in the neighborhood of the steady state is that there are as many eigenvalues larger than one in modulus as there are forward looking variables in the system. An additional rank condition requires that the square submatrix of the right Schur vectors corresponding to the forward looking variables (jumpers) and to the explosive eigenvalues must have full rank.

     Options

     solve_algo = INTEGER
          See solve_algo, for the possible values and their meaning.

     Output

     check returns the eigenvalues in the global variable oo_.dr.eigval.

 -- MATLAB/Octave variable: oo_.dr.eigval
     Contains the eigenvalues of the model, as computed by the check command.

 -- Command: model_info ;

     Description

     This command provides information about:

       * the normalization of the model: an endogenous variable is attributed to each equation of the model;

       * the block structure of the model: for each block model_info indicates its type, the equations number and endogenous variables belonging to this block.

     This command can only be used in conjunction with the block option of the model block.

     There are five different types of blocks depending on the simulation method used:

     EVALUATE FORWARD
          In this case the block contains only equations where endogenous variable attributed to the equation appears currently on the left hand side and where no forward looking endogenous variables appear. The block has the form: y_j,t = f_j(y_t, y_t-1, \ldots, y_t-k).

     EVALUATE BACKWARD
          The block contains only equations where endogenous variable attributed to the equation appears currently on the left hand side and where no backward looking endogenous variables appear. The block has the form: y_j,t = f_j(y_t, y_t+1, \ldots, y_t+k).

     SOLVE FORWARD X
          The block contains only equations where endogenous variable attributed to the equation does not appear currently on the left hand side and where no forward looking endogenous variables appear. The block has the form: g_j(y_j,t, y_t, y_t-1, \ldots, y_t-k)=0. X is equal to SIMPLE if the block has only one equation. If several equation appears in the block, X is equal to COMPLETE.

     SOLVE FORWARD X
          The block contains only equations where endogenous variable attributed to the equation does not appear currently on the left hand side and where no backward looking endogenous variables appear. The block has the form: g_j(y_j,t, y_t, y_t+1, \ldots, y_t+k)=0. X is equal to SIMPLE if the block has only one equation. If several equation appears in the block, X is equal to COMPLETE.

     SOLVE TWO BOUNDARIES X
          The block contains equations depending on both forward and backward variables. The block looks like: g_j(y_j,t, y_t, y_t-1, \ldots, y_t-k ,y_t, y_t+1, \ldots, y_t+k)=0. X is equal to SIMPLE if the block has only one equation. If several equation appears in the block, X is equal to COMPLETE.

 -- Command: print_bytecode_dynamic_model ;
     Prints the equations and the Jacobian matrix of the dynamic model stored in the bytecode binary format file. Can only be used in conjunction with the bytecode option of the model block.

 -- Command: print_bytecode_static_model ;
     Prints the equations and the Jacobian matrix of the static model stored in the bytecode binary format file. Can only be used in conjunction with the bytecode option of the model block.

Deterministic simulation
========================

When the framework is deterministic, Dynare can be used for models with the assumption of perfect foresight. Typically, the system is supposed to be in a state of equilibrium before a period 1 when the news of a contemporaneous or of a future shock is learned by the agents in the model. The purpose of the simulation is to describe the reaction in anticipation of, then in reaction to the shock, until the system returns to the old or to a new state of equilibrium. In most models, this return to equilibrium is only an asymptotic phenomenon, which one must approximate by an horizon of simulation far enough in the future.  Another exercise for which Dynare is well suited is to study the transition path to a new equilibrium following a permanent shock.  For deterministic simulations, Dynare uses a Newton-type algorithm, first proposed by Laffargue (1990) and Boucekkine (1995), instead of a first order technique like the one proposed by Fair and Taylor (1983), and used in earlier generation simulation programs. We believe this approach to be in general both faster and more robust. The details of the algorithm can be found in Juillard (1996).

 -- Command: simul ;
 -- Command: simul (OPTIONS...);

     Description

     Triggers the computation of a deterministic simulation of the model for the number of periods set in the option periods.

     Options

     periods = INTEGER
          Number of periods of the simulation

     maxit = INTEGER
          Determines the maximum number of iterations used in the non-linear solver. The default value of maxit is 10. The maxit option is shared with the steady command. So a change in maxit in a simul command will also be considered in the following steady commands.

     stack_solve_algo = INTEGER
          Algorithm used for computing the solution. Possible values are:

          0
               Newton method to solve simultaneously all the equations for every period, see Juillard (1996) (Default).

          1
               Use a Newton algorithm with a sparse LU solver at each iteration (requires bytecode and/or block option, see Model declaration).

          2
               Use a Newton algorithm with a Generalized Minimal Residual (GMRES) solver at each iteration (requires bytecode and/or block option, see Model declaration; not available under Octave)

          3
               Use a Newton algorithm with a Stabilized Bi-Conjugate Gradient (BICGSTAB) solver at each iteration (requires bytecode and/or block option, see Model declaration).

          4
               Use a Newton algorithm with a optimal path length at each iteration (requires bytecode and/or block option, see Model declaration).

          5
               Use a Newton algorithm with a sparse Gaussian elimination (SPE) solver at each iteration (requires bytecode option, see Model declaration).

     markowitz = DOUBLE
          Value of the Markowitz criterion, used to select the pivot. Only used when stack_solve_algo = 5. Default: 0.5.

     minimal_solving_periods = INTEGER
          Specify the minimal number of periods where the model has to be solved, before using a constant set of operations for the remaining periods. Only used when stack_solve_algo = 5. Default: 1.

     datafile = FILENAME
          If the variables of the model are not constant over time, their initial values, stored in a text file, could be loaded, using that option, as initial values before a deteministic simulation.

     Output

     The simulated endogenous variables are available in global matrix oo_.endo_simul.

 -- MATLAB/Octave variable: oo_.endo_simul
     This variable stores the result of a deterministic simulation (computed by simul) or of a stochastic simulation (computed by stoch_simul with the periods option).

     The variables are arranged row by row, in order of declaration (as in M_.endo_names). Note that this variable also contains initial and terminal conditions, so it has more columns than the value of periods option.

Stochastic solution and simulation
==================================

In a stochastic context, Dynare computes one or several simulations corresponding to a random draw of the shocks. Dynare uses a Taylor approximation, up to third order, of the expectation functions (see Judd (1996), Collard and Juillard (2001a), Collard and Juillard (2001b), and Schmitt-Grohé and Uríbe (2004)). The details of the Dynare implementation of the first order solution are given in Villemot (2011).

Computing the stochastic solution
---------------------------------

 -- Command: stoch_simul [VARIABLE_NAME...];
 -- Command: stoch_simul (OPTIONS...) [VARIABLE_NAME...];

     Description

     stoch_simul solves a stochastic (i.e. rational expectations) model, using perturbation techniques.

     More precisely, stoch_simul computes a Taylor approximation of the decision and transition functions for the model. Using this, it computes impulse response functions and various descriptive statistics (moments, variance decomposition, correlation and autocorrelation coefficients). For correlated shocks, the variance decomposition is computed as in the VAR literature through a Cholesky decomposition of the covariance matrix of the exogenous variables. When the shocks are correlated, the variance decomposition depends upon the order of the variables in the varexo command.

     The Taylor approximation is computed around the steady state (see Steady state).

     The IRFs are computed as the difference between the trajectory of a variable following a shock at the beginning of period 1 and its steady state value. More details on the computation of IRFs can be found on the DynareWiki (http://www.dynare.org/DynareWiki/IrFs).

     Variance decomposition, correlation, autocorrelation are only displayed for variables with positive variance. Impulse response functions are only plotted for variables with response larger than 10^-10.

     Variance decomposition is computed relative to the sum of the contribution of each shock. Normally, this is of course equal to aggregate variance, but if a model generates very large variances, it may happen that, due to numerical error, the two differ by a significant amount. Dynare issues a warning if the maximum relative difference between the sum of the contribution of each shock and aggregate variance is larger than 0.01%.

     Currently, the IRFs are only plotted for 12 variables. Select the ones you want to see, if your model contains more than 12 endogenous variables.

     The covariance matrix of the shocks is specified with the shocks command (see Shocks on exogenous variables).

     When a list of VARIABLE_NAME is specified, results are displayed only for these variables.

     Options

     ar = INTEGER
          Order of autocorrelation coefficients to compute and to print. Default: 5.

     drop = INTEGER
          Number of points dropped at the beginning of simulation before computing the summary statistics. Default: 100.

     hp_filter = DOUBLE
          Uses HP filter with \lambda = DOUBLE before computing moments. Default: no filter.

     hp_ngrid = INTEGER
          Number of points in the grid for the discrete Inverse Fast Fourier Transform used in the HP filter computation. It may be necessary to increase it for highly autocorrelated processes. Default: 512.

     irf = INTEGER
          Number of periods on which to compute the IRFs. Setting irf=0, suppresses the plotting of IRF's. Default: 40.

     irf_shocks = ( VARIABLE_NAME [[,] VARIABLE_NAME ...] )
          The exogenous variables for which to compute IRFs. Default: all.

     relative_irf
          Requests the computation of normalized IRFs in percentage of the standard error of each shock.

     linear
          Indicates that the original model is linear (put it rather in the model command).

     nocorr
          Don't print the correlation matrix (printing them is the default).

     nofunctions
          Don't print the coefficients of the approximated solution (printing them is the default).

     nomoments
          Don't print moments of the endogenous variables (printing them is the default).

     nograph.
          Doesn't do the graphs. Useful for loops.

     noprint
          Don't print anything. Useful for loops.

     print
          Print results (opposite of noprint).

     order = INTEGER
          Order of Taylor approximation. Acceptable values are 1, 2 and 3. Note that for third order, k_order_solver option is implied and only empirical moments are available (you must provide a value for periods option). Default: 2.

     k_order_solver
          Use a k-order solver (implemented in C++) instead of the default Dynare solver. This option is not yet compatible with the bytecode option (see Model declaration. Default: disabled for order 1 and 2, enabled otherwise

     periods = INTEGER
          If different from zero, empirical moments will be computed instead of theoretical moments. The value of the option specifies the number of periods to use in the simulations. Values of the initval block, possibly recomputed by steady, will be used as starting point for the simulation. The simulated endogenous variables are made available to the user in a vector for each variable and in the global matrix oo_.endo_simul (see oo_.endo_simul). Default: 0.

     qz_criterium = DOUBLE
          Value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving 1^st order problems. Default: 1.000001 (except when estimating with lik_init option equal to 1: the default is 0.999999 in that case; see Estimation).

     replic = INTEGER
          Number of simulated series used to compute the IRFs. Default: 1 if order=1, and 50 otherwise.

     solve_algo = INTEGER
          See solve_algo, for the possible values and their meaning.

     aim_solver
          Use the Anderson-Moore Algorithm (AIM) to compute the decision rules, instead of using Dynare's default method based on a generalized Schur decomposition. This option is only valid for first order approximation. See AIM website (http://www.federalreserve.gov/Pubs/oss/oss4/aimindex.html) for more details on the algorithm.

     conditional_variance_decomposition = INTEGER
          See below.

     conditional_variance_decomposition = [INTEGER1:INTEGER2]
          See below.

     conditional_variance_decomposition = [INTEGER1 INTEGER2 ...]
          Computes a conditional variance decomposition for the specified period(s). The periods must be strictly positive. Conditional variances are given by var(y_t+k|t). For period 1, the conditional variance decomposition provides the decomposition of the effects of shocks upon impact. The results are stored in oo_.conditional_variance_decomposition (see oo_.conditional_variance_decomposition).

     pruning
          Discard higher order terms when iteratively computing simulations of the solution, as in Kim, Kim, Schaumburg and Sims (2008).

     partial_information

          Computes the solution of the model under partial information, along the lines of Pearlman, Currie and Levine (1986). Agents are supposed to observe only some variables of the economy. The set of observed variables is declared using the varobs command. Note that if varobs is not present or contains all endogenous variables, then this is the full information case and this option has no effect. More references can be found at http://www.dynare.org/DynareWiki/PartialInformation.

     sylvester = OPTION
          Determines the algorithm used to solve the Sylvester equation for block decomposed model. Possible values for OPTION are:

          default
               Uses the default solver for Sylvester equations (gensylv) based on Ondra Kamenik algorithm (see the Dynare Website (http://www.dynare.org/documentation-and-support/dynarepp/sylvester.pdf/at_download/file) for more information).

          fixed_point
               Uses a fixed point algorithm to solve the Sylvester equation (gensylv_fp). This method is faster than the default one for large scale models.

          Default value is default

     sylvester_fixed_point_tol = DOUBLE
          It is the convergence criterion used in the fixed point sylvester solver. Its default value is 1e-12.

     Output

     This command sets oo_.dr, oo_.mean, oo_.var and oo_.autocorr, which are described below.

     If option periods is present, sets oo_.endo_simul (see oo_.endo_simul), and also saves the simulated variables in MATLAB/Octave vectors of the global workspace with the same name as the endogenous variables.

     If options irf is different from zero, sets oo_.irfs (see below) and also saves the IRFs in MATLAB/Octave vectors of the global workspace (this latter way of accessing the IRFs is deprecated and will disappear in a future version).

     Example 1

          shocks;
          var e;
          stderr 0.0348;
          end;

          stoch_simul;

     Performs the simulation of the 2nd order approximation of a model with a single stochastic shock e, with a standard error of 0.0348.

     Example 2

          stoch_simul(linear,irf=60) y k;

     Performs the simulation of a linear model and displays impulse response functions on 60 periods for variables y and k.

 -- MATLAB/Octave variable: oo_.mean
     After a run of stoch_simul, contains the mean of the endogenous variables. Contains theoretical mean if the periods option is not present, and empirical mean otherwise. The variables are arranged in declaration order.

 -- MATLAB/Octave variable: oo_.var
     After a run of stoch_simul, contains the variance-covariance of the endogenous variables. Contains theoretical variance if the periods option is not present, and empirical variance otherwise. The variables are arranged in declaration order.

 -- MATLAB/Octave variable: oo_.autocorr
     After a run of stoch_simul, contains a cell array of the autocorrelation matrices of the endogenous variables. The element number of the matrix in the cell array corresponds to the order of autocorrelation. The option ar specifies the number of autocorrelation matrices available. Contains theoretical autocorrelations if the periods option is not present, and empirical autocorrelations otherwise.

     The element oo_.autocorr{i}(k,l) is equal to the correlation between y^k_t and y^l_t-i, where y^k (resp. y^l) is the k-th (resp. l-th) endogenous variable in the declaration order.

     Note that if theoretical moments have been requested, oo_.autocorr{i} is the same than oo_.gamma_y{i+1}.

 -- MATLAB/Octave variable: oo_.gamma_y
     After a run of stoch_simul, if theoretical moments have been requested (i.e. if the periods option is not present), this variable contains a cell array with the following values (where ar is the value of the option of the same name):

     oo_.gamma{1}
          Variance/co-variance matrix.

     oo_.gamma{i+1} (for i=1:ar)
          Autocorrelation function. see oo_.autocorr for more details. Beware, this is the autocorrelation function, not the autocovariance function.

     oo_.gamma{nar+2}
          Variance decomposition.

     oo_.gamma{nar+3}
          If a second order approximation has been requested, contains the vector of the mean correction terms.

 -- MATLAB/Octave variable: oo_.irfs
     After a run of stoch_simul with option irf different from zero, contains the impulse responses, with the following naming convention: VARIABLE_NAME_SHOCK_NAME.

     For example, oo_.irfs.gnp_ea contains the effect on gnp of a one standard deviation shock on ea.

The approximated solution of a model takes the form of a set of decision rules or transition equations expressing the current value of the endogenous variables of the model as function of the previous state of the model and shocks oberved at the beginning of the period. The decision rules are stored in the structure oo_.dr which is described below.

Typology and ordering of variables
----------------------------------

Dynare distinguishes four types of endogenous variables:

Purely backward (or purely predetermined) variables
     Those that appear only at current and past period in the model, but not at future period (i.e. at t and t-1 but not t+1). The number of such variables is equal to oo_.dr.npred - oo_.dr.nboth.

Purely forward variables
     Those that appear only at current and future period in the model, but not at past period (i.e. at t and t+1 but not t-1). The number of such variables is stored in oo_.dr.nfwrd.

Mixed variables
     Those that appear at current, past and future period in the model (i.e. at t, t+1 and t-1). The number of such variables is stored in oo_.dr.nboth.

Static variables
     Those that appear only at current, not past and future period in the model (i.e. only at t, not at t+1 or t-1). The number of such variables is stored in oo_.dr.nstatic.

Note that all endogenous variables fall into one of these four categories, since after the creation of auxiliary variables (see Auxiliary variables), all endogenous have at most one lead and one lag. We therefore have the following identity:

     oo_.dr.npred + oo_.dr.nfwrd + oo_.dr.nstatic = M_.endo_nbr

Internally, Dynare uses two orderings of the endogenous variables: the order of declaration (which is reflected in M_.endo_names), and an order based on the four types described above, which we will call the DR-order (``DR'' stands for decision rules). Most of the time, the declaration order is used, but for elements of the decision rules, the DR-order is used.

The DR-order is the following: static variables appear first, then purely backward variables, then mixed variables, and finally purely forward variables. Inside each category, variables are arranged according to the declaration order.

Variable oo_.dr.order_var maps DR-order to declaration order, and variable oo_.dr.inv_order_var contains the inverse map. In other words, the k-th variable in the DR-order corresponds to the endogenous variable numbered oo_.dr_order_var(k) in declaration order. Conversely, k-th declared variable is numbered oo_.dr.inv_order_var(k) in DR-order.

Finally, the state variables of the model are the purely backward variables and the mixed variables. They are orderer in DR-order when they appear in decision rules elements. There are oo_.dr.npred such variables.

First order approximation
-------------------------

The approximation has the form:

y_t = y^s + A y^h_t-1 + B u_t

where y^s is the steady state value of y and y^h_t=y_t-y^s.

The coefficients of the decision rules are stored as follows:

  * y^s is stored in oo_.dr.ys. The vector rows correspond to all endogenous in the declaration order.

  * A is stored in oo_.dr.ghx. The matrix rows correspond to all endogenous in DR-order. The matrix columns correspond to state variables in DR-order.

  * B is stored oo_.dr.ghu. The matrix rows correspond to all endogenous in DR-order. The matrix columns correspond to exogenous variables in declaration order.

Second order approximation
--------------------------

The approximation has the form:

y_t = y^s + 0.5 \Delta^2 + A y^h_t-1 + B u_t + 0.5 C (y^h_t-1\otimes y^h_t-1) + 0.5 D (u_t \otimes u_t) + E (y^h_t-1 \otimes u_t)

where y^s is the steady state value of y, y^h_t=y_t-y^s, and \Delta^2 is the shift effect of the variance of future shocks.

The coefficients of the decision rules are stored in the variables described for first order approximation, plus the following variables:

  * \Delta^2 is stored in oo_.dr.ghs2. The vector rows correspond to all endogenous in DR-order.

  * C is stored in oo_.dr.ghxx. The matrix rows correspond to all endogenous in DR-order. The matrix columns correspond to the Kronecker product of the vector of state variables in DR-order.

  * D is stored in oo_.dr.ghuu. The matrix rows correspond to all endogenous in DR-order. The matrix columns correspond to the Kronecker product of exogenous variables in declaration order.

  * E is stored in oo_.dr.ghxu. The matrix rows correspond to all endogenous in DR-order. The matrix columns correspond to the Kronecker product of the vector of state variables (in DR-order) by the vector of exogenous variables (in declaration order).

Third order approximation
-------------------------

The approximation has the form:

y_t = y^s + G_0 + G_1 z_t + G_2 (z_t \otimes z_t) + G_3 (z_t \otimes z_t \otimes z_t)

where y^s is the steady state value of y, and z_t is a vector consisting of the deviation from the steady state of the state variables (in DR-order) at date t-1 followed by the exogenous variables at date t (in declaration order). The vector z_t is therefore of size n_z = oo_.dr.npred + M_.exo_nbr.

The coefficients of the decision rules are stored as follows:

  * y^s is stored in oo_.dr.ys. The vector rows correspond to all endogenous in the declaration order.

  * G_0 is stored in oo_.dr.g_0. The vector rows correspond to all endogenous in DR-order.

  * G_1 is stored in oo_.dr.g_1. The matrix rows correspond to all endogenous in DR-order. The matrix columns correspond to state variables in DR-order, followed by exogenous in declaration order.

  * G_2 is stored in oo_.dr.g_2. The matrix rows correspond to all endogenous in DR-order. The matrix columns correspond to the Kronecker product of state variables (in DR-order), followed by exogenous (in declaration order). Note that the Kronecker product is stored in a folded way, i.e. symmetric elements are stored only once, which implies that the matrix has n_z(n_z+1)/2 columns.  More precisely, each column of this matrix corresponds to a pair (i_1, i_2) where each index represents an element of z_t and is therefore between 1 and n_z. Only non-decreasing pairs are stored, i.e. those for which i_1 \leq i_2. The columns are arranged in the lexicographical order of non-decreasing pairs. Also note that for those pairs where i_1 \neq i_2, since the element is stored only once but appears two times in the unfolded G_2 matrix, it must be multiplied by 2 when computing the decision rules.

  * G_3 is stored in oo_.dr.g_3. The matrix rows correspond to all endogenous in DR-order. The matrix columns correspond to the third Kronecker power of state variables (in DR-order), followed by exogenous (in declaration order). Note that the third Kronecker power is stored in a folded way, i.e. symmetric elements are stored only once, which implies that the matrix has n_z(n_z+1)(n_z+2)/6 columns.  More precisely, each column of this matrix corresponds to a tuple (i_1, i_2, i_3) where each index represents an element of z_t and is therefore between 1 and n_z. Only non-decreasing tuples are stored, i.e. those for which i_1 \leq i_2 \leq i_3. The columns are arranged in the lexicographical order of non-decreasing tuples. Also note that for tuples that have three distinct indices (i.e. i_1 \neq i_2 and i_1 \neq i_3 and i_2 \neq i_3, since these elements are stored only once but appears six times in the unfolded G_3 matrix, they must be multiplied by 6 when computing the decision rules. Similarly, for those tuples that have two equal indices (i.e. of the form (a,a,b) or (a,b,a) or (b,a,a)), since these elements are stored only once but appears three times in the unfolded G_3 matrix, they must be multiplied by 3 when computing the decision rules.

 -- MATLAB/Octave variable: oo_.conditional_variance_decomposition
     After a run of stoch_simul with the conditional_variance_decomposition option, contains a three-dimensional array with the result of the decomposition. The first dimension corresponds to forecast horizons (as declared with the option), the second dimension corresponds to endogenous variables (in the order of declaration), the third dimension corresponds to exogenous variables (in the order of declaration).

Estimation
==========

Provided that you have observations on some endogenous variables, it is possible to use Dynare to estimate some or all parameters. Both maximum likelihood (as in Ireland (2004)) and Bayesian techniques (as in Rabanal and Rubio-Ramirez (2003), Schorfheide (2000) or Smets and Wouters (2003)) are available. Using Bayesian methods, it is possible to estimate DSGE models, VAR models, or a combination of the two techniques called DSGE-VAR.

Note that in order to avoid stochastic singularity, you must have at least as many shocks or measurement errors in your model as you have observed variables.

 -- Command: varobs VARIABLE_NAME...;

     Description

     This command lists the name of observed endogenous variables for the estimation procedure. These variables must be available in the data file (see estimation_cmd).

     Alternatively, this command is also used in conjunction with the partial_information option of stoch_simul, for declaring the set of observed variables when solving the model under partial information.

     Only one instance of varobs is allowed in a model file. If one needs to declare observed variables in a loop, the macroprocessor can be used as shown in the second example below.

     Simple example

          varobs C y rr;

     Example with a loop

          varobs
          @#for co in countries
            GDP_@{co}
          @#endfor
          ;

 -- Block: observation_trends ;

     Description

     This block specifies linear trends for observed variables as functions of model parameters.

     Each line inside of the block should be of the form:

          VARIABLE_NAME(EXPRESSION);

     In most cases, variables shouldn't be centered when observation_trends is used.

     Example

          observation_trends;
          Y (eta);
          P (mu/eta);
          end;

 -- Block: estimated_params ;

     Description

     This block lists all parameters to be estimated and specifies bounds and priors as necessary.

     Each line corresponds to an estimated parameter.

     In a maximum likelihood estimation, each line follows this syntax:

          stderr VARIABLE_NAME | corr VARIABLE_NAME_1, VARIABLE_NAME_2 | PARAMETER_NAME
          , INITIAL_VALUE [, LOWER_BOUND, UPPER_BOUND ];

     In a Bayesian estimation, each line follows this syntax:

          stderr VARIABLE_NAME | corr VARIABLE_NAME_1, VARIABLE_NAME_2 |
          PARAMETER_NAME | DSGE_PRIOR_WEIGHT
          [, INITIAL_VALUE [, LOWER_BOUND, UPPER_BOUND]], PRIOR_SHAPE,
          PRIOR_MEAN, PRIOR_STANDARD_ERROR [, PRIOR_3RD_PARAMETER [,
          PRIOR_4TH_PARAMETER [, SCALE_PARAMETER ] ] ];

     The first part of the line consists of one of the three following alternatives:

     stderr VARIABLE_NAME
          Indicates that the standard error of the exogenous variable VARIABLE_NAME, or of the observation error associated with endogenous observed variable VARIABLE_NAME, is to be estimated

     corr VARIABLE_NAME1, VARIABLE_NAME2
          Indicates that the correlation between the exogenous variables VARIABLE_NAME1 and VARIABLE_NAME2, or the correlation of the observation errors associated with endogenous observed variables VARIABLE_NAME1 and VARIABLE_NAME2, is to be estimated

     PARAMETER_NAME
          The name of a model parameter to be estimated

     DSGE_PRIOR_WEIGHT
          ...

     The rest of the line consists of the following fields, some of them being optional:

     INITIAL_VALUE
          Specifies a starting value for maximum likelihood estimation

     LOWER_BOUND
          Specifies a lower bound for the parameter value in maximum likelihood estimation

     UPPER_BOUND
          Specifies an upper bound for the parameter value in maximum likelihood estimation

     PRIOR_SHAPE
          A keyword specifying the shape of the prior density. The possible values are: beta_pdf, gamma_pdf, normal_pdf, uniform_pdf, inv_gamma_pdf, inv_gamma1_pdf, inv_gamma2_pdf. Note that inv_gamma_pdf is equivalent to inv_gamma1_pdf

     PRIOR_MEAN
          The mean of the prior distribution

     PRIOR_STANDARD_ERROR
          The standard error of the prior distribution

     PRIOR_3RD_PARAMETER
          A third parameter of the prior used for generalized beta distribution, generalized gamma and for the uniform distribution. Default: 0

     PRIOR_4TH_PARAMETER
          A fourth parameter of the prior used for generalized beta distribution and for the uniform distribution. Default: 1

     SCALE_PARAMETER
          The scale parameter to be used for the jump distribution of the Metropolis-Hasting algorithm

     Note that INITIAL_VALUE, LOWER_BOUND, UPPER_BOUND, PRIOR_MEAN, PRIOR_STANDARD_ERROR, PRIOR_3RD_PARAMETER, PRIOR_4TH_PARAMETER and SCALE_PARAMETER can be any valid EXPRESSION. Some of them can be empty, in which Dynare will select a default value depending on the context and the prior shape.

     As one uses options more towards the end of the list, all previous options must be filled: for example, if you want to specify SCALE_PARAMETER, you must specify PRIOR_3RD_PARAMETER and PRIOR_4TH_PARAMETER. Use empty values, if these parameters don't apply.

     Example

     The following line:

          corr eps_1, eps_2, 0.5,  ,  , beta_pdf, 0, 0.3, -1, 1;

     sets a generalized beta prior for the correlation between eps_1 and eps_2 with mean 0 and variance 0.3. By setting PRIOR_3RD_PARAMETER to -1 and PRIOR_4TH_PARAMETER to 1 the standard beta distribution with support [0,1] is changed to a generalized beta with support [-1,1]. Note that LOWER_BOUND and UPPER_BOUND are left empty and thus default to -1 and 1, respectively. The initial value is set to 0.5.

     Similarly, the following line:

          corr eps_1, eps_2, 0.5,  -0.5,  1, beta_pdf, 0, 0.3, -1, 1;

     sets the same generalized beta distribution as before, but now truncates this distribution to [-0.5,1] through the use of LOWER_BOUND and UPPER_BOUND. Hence, the prior does not integrate to 1 anymore.

     Parameter transformation

     Sometimes, it is desirable to estimate a transformation of a parameter appearing in the model, rather than the parameter itself. It is of course possible to replace the original parameter by a function of the estimated parameter everywhere is the model, but it is often unpractical.

     In such a case, it is possible to declare the parameter to be estimated in the parameters statement and to define the transformation, using a pound sign (#) expression (see Model declaration).

     Example

          parameters bet;

          model;
          # sig = 1/bet;
          c = sig*c(+1)*mpk;
          end;

          estimated_params;
          bet, normal_pdf, 1, 0.05;
          end;

 -- Block: estimated_params_init ;

     This block declares numerical initial values for the optimizer when these ones are different from the prior mean.

     Each line has the following syntax:

          stderr VARIABLE_NAME | corr VARIABLE_NAME_1, VARIABLE_NAME_2 | PARAMETER_NAME
          , INITIAL_VALUE;

     See estimated_params, for the meaning and syntax of the various components.

 -- Block: estimated_params_bounds ;

     This block declares lower and upper bounds for parameters in maximum likelihood estimation.

     Each line has the following syntax:

          stderr VARIABLE_NAME | corr VARIABLE_NAME_1, VARIABLE_NAME_2 | PARAMETER_NAME
          , LOWER_BOUND, UPPER_BOUND;

     See estimated_params, for the meaning and syntax of the various components.

 -- Command: estimation [VARIABLE_NAME...];
 -- Command: estimation (OPTIONS...) [VARIABLE_NAME...];

     Description

     This command runs Bayesian or maximum likelihood estimation.

     The following information will be displayed by the command:

       * results from posterior optimization (also for maximum likelihood)

       * marginal log density

       * mean and shortest confidence interval from posterior simulation

       * Metropolis-Hastings convergence graphs that still need to be documented

       * graphs with prior, posterior and mode

       * graphs of smoothed shocks, smoothed observation errors, smoothed and historical variables

     Options

     datafile = FILENAME
          The datafile: a .m file, a .mat file or, a .xls file (the latter format is supported under Octave if the io (http://octave.sourceforge.net/io/) and java (http://octave.sourceforge.net/java/) packages from Octave-Forge are installed, along with a Java Runtime Environment (http://www.java.com/download))

     xls_sheet = NAME
          The name of the sheet with the data in an Excel file

     xls_range = RANGE
          The range with the data in an Excel file

     nobs = INTEGER
          The number of observations to be used. Default: all observations in the file

     nobs = [INTEGER1:INTEGER2]
          Runs a recursive estimation and forecast for samples of size ranging of INTEGER1 to INTEGER2. Option forecast must also be specified

     first_obs = INTEGER
          The number of the first observation to be used. Default: 1

     prefilter = INTEGER
          A value of 1 means that the estimation procedure will demean the data. Default: 0, i.e. no prefiltering

     presample = INTEGER
          The number of observations to be skipped before evaluating the likelihood. Default: 0

     loglinear
          Computes a log-linear approximation of the model instead of a linear approximation. The data must correspond to the definition of the variables used in the model. Default: computes a linear approximation

     plot_priors = INTEGER
          Control the plotting of priors:

          0
               No prior plot

          1
               Prior density for each estimated parameter is plotted. It is important to check that the actual shape of prior densities matches what you have in mind. Ill choosen values for the prior standard density can result in absurd prior densities.

          Default value is 1.

     nograph
          No graphs should be plotted

     lik_init = INTEGER
          Type of initialization of Kalman filter:

          1
               For stationary models, the initial matrix of variance of the error of forecast is set equal to the unconditional variance of the state variables

          2
               For nonstationary models: a wide prior is used with an initial matrix of variance of the error of forecast diagonal with 10 on the diagonal

          3
               For nonstationary models: use a diffuse filter (use rather the diffuse_filter option)

          4
               The filter is initialized with the fixed point of the Riccati equation

          Default value is 1. For advanced use only.

     lik_algo = INTEGER
          For internal use and testing only.

     conf_sig = DOUBLE
          See conf_sig.

     mh_replic = INTEGER
           Number of replications for Metropolis-Hastings algorithm. For the time being, mh_replic should be larger than 1200. Default: 20000

     sub_draws = INTEGER
           number of draws from the Metropolis iterations that are used to compute posterior distribution of various objects (smoothed variable, smoothed shocks, forecast, moments, IRF). sub_draws should be smaller than the total number of Metropolis draws available. Default: min(1200,0.25*Total number of draws)

     mh_nblocks = INTEGER
          Number of parallel chains for Metropolis-Hastings algorithm. Default: 2

     mh_drop = DOUBLE
          The fraction of initially generated parameter vectors to be dropped before using posterior simulations. Default: 0.5

     mh_jscale = DOUBLE
          The scale to be used for the jumping distribution in Metropolis-Hastings algorithm. The default value is rarely satisfactory. This option must be tuned to obtain, ideally, an acceptation rate of 25% in the Metropolis-Hastings algorithm. Default: 0.2

     mh_init_scale = DOUBLE
          The scale to be used for drawing the initial value of the Metropolis-Hastings chain. Default: 2*mh_scale

     mh_recover
           Attempts to recover a Metropolis-Hastings simulation that crashed prematurely. Shouldn't be used together with load_mh_file

     mh_mode = INTEGER
          ...

     mode_file = FILENAME
          Name of the file containing previous value for the mode. When computing the mode, Dynare stores the mode (xparam1) and the hessian (hh) in a file called MODEL_FILENAME_mode.mat

     mode_compute = INTEGER | FUNCTION_NAME
          Specifies the optimizer for the mode computation:

          0
               The mode isn't computed. When mode_file option is specified, the mode is simply read from that file.

               When mode_file option is not specified, Dynare reports the value of the log posterior (log likelihood) evaluated at the initial value of the parameters.

               When mode_file option is not specified and there is no estimated_params block, but the smoother option is used, it is a roundabout way to compute the smoothed value of the variables of a model with calibrated parameters.

          1
               Uses fmincon optimization routine (not available under Octave)

          2
               Value no longer used

          3
               Uses fminunc optimization routine

          4
               Uses Chris Sims's csminwel

          5
               Uses Marco Ratto's newrat. This value is not compatible with non linear filters or DSGE-VAR models

          6
               Uses a Monte-Carlo based optimization routine (see Dynare wiki (http://www.dynare.org/DynareWiki/MonteCarloOptimization) for more details)

          7
               Uses fminsearch, a simplex based optimization routine (available under MATLAB if the optimization toolbox is installed; available under Octave if the optim (http://octave.sourceforge.net/optim/) package from Octave-Forge is installed)

          8
               Uses Dynare implementation of the Nelder-Mead simplex based optimization routine (generally more efficient than the MATLAB or Octave implementation available with mode_compute=7)

          9
               Uses the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, an evolutionary algorithm for difficult non-linear non-convex optimization

          FUNCTION_NAME
               It is also possible to give a FUNCTION_NAME to this option, instead of an INTEGER. In that case, Dynare takes the return value of that function as the posterior mode.

          Default value is 4.

     mode_check
          Tells Dynare to plot the posterior density for values around the computed mode for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer

     prior_trunc = DOUBLE
           Probability of extreme values of the prior density that is ignored when computing bounds for the parameters. Default: 1e-32

     load_mh_file
           Tells Dynare to add to previous Metropolis-Hastings simulations instead of starting from scratch. Shouldn't be used together with mh_recover

     optim = (FMINCON OPTIONS)
          Can be used to set options for fmincon, the optimizing function of MATLAB Optimization toolbox. Use MATLAB's syntax for these options. Default: ('display','iter','LargeScale','off','MaxFunEvals',100000,'TolFun',1e-8,'TolX',1e-6)

     nodiagnostic
          Doesn't compute the convergence diagnostics for Metropolis-Hastings. Default: diagnostics are computed and displayed

     bayesian_irf
           Triggers the computation of the posterior distribution of IRFs. The length of the IRFs are controlled by the irf option. Results are stored in oo_.PosteriorIRF.dsge (see below for a description of this variable)

     dsge_var
          Triggers the estimation of a DSGE-VAR model, where the weight of the DSGE prior of the VAR model will be estimated.  The prior on the weight of the DSGE prior, dsge_prior_weight, must be defined in the estimated_params section.  NB: The previous method of declaring dsge_prior_weight as a parameter and then placing it in estimated_params is now deprecated and will be removed in a future release of Dynare.

     dsge_var = DOUBLE
          Triggers the estimation of a DSGE-VAR model, where the weight of the DSGE prior of the VAR model is calibrated to the value passed. NB: The previous method of declaring dsge_prior_weight as a parameter and then calibrating it is now deprecated and will be removed in a future release of Dynare.

     dsge_varlag = INTEGER
           The number of lags used to estimate a DSGE-VAR model. Default: 4.

     moments_varendo
           Triggers the computation of the posterior distribution of the theoretical moments of the endogenous variables. Results are stored in oo_.PosteriorTheoreticalMoments (see below for a description of this variable)

     conditional_variance_decomposition = INTEGER
          See below.

     conditional_variance_decomposition = [INTEGER1:INTEGER2]
          See below.

     conditional_variance_decomposition = [INTEGER1 INTEGER2 ...]
          Computes the posterior distribution of the conditional variance decomposition for the specified period(s). The periods must be strictly positive. Conditional variances are given by var(y_t+k|t). For period 1, the conditional variance decomposition provides the decomposition of the effects of shocks upon impact. The results are stored in oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition, but currently there is not output. Note that this option requires the option moments_varendo to be specified.

     filtered_vars
           Triggers the computation of the posterior distribution of filtered endogenous variables and shocks. Results are stored in oo_.FilteredVariables (see below for a description of this variable)

     smoother
           Triggers the computation of the posterior distribution of smoothered endogenous variables and shocks. Results are stored in oo_.SmoothedVariables, oo_.SmoothedShocks and oo_.SmoothedMeasurementErrors.  Also triggers the computation of oo_.UpdatedVariables, which contains the estimation of the expected value of variables given the information available at the current date.  See below for a description of all these variables.

     forecast = INTEGER
           Computes the posterior distribution of a forecast on INTEGER periods after the end of the sample used in estimation. The result is stored in variable oo_.forecast (see Forecasting)

     tex
           Requests the printing of results and graphs in TeX tables and graphics that can be later directly included in LaTeX files (not yet implemented)

     kalman_algo = INTEGER
          ...

     kalman_tol = DOUBLE
          ...

     filter_covariance
           Saves the series of one step ahead error of forecast covariance matrices.

     filter_step_ahead = [INTEGER1:INTEGER2]
          filter_step_ahead Triggers the computation k-step ahead filtered values. Stores results in oo_.FilteredVariablesKStepAhead and oo_.FilteredVariablesKStepAheadVariances.

     filter_decomposition
           Triggers the computation of the shock decomposition of the above k-step ahead filtered values.

     constant
          ...

     noconstant
          ...

     diffuse_filter
          Uses the diffuse Kalman filter (as described in Durbin and Koopman (2001) and Koopman and Durbin (2003)) to estimate models with non-stationary observed variables.

          When diffused_filter is used the lik_init option of estimation has no effect.

          When there are nonstationary variables in a model, there is no unique deterministic steady state. The user must supply a MATLAB/Octave function that computes the steady state values of the stationary variables in the model and returns dummy values for the nonstationary ones. The function should be called with the name of the .mod file followed by _steadystate. See fs2000_steadystate.m in examples directory for an example.

          Note that the nonstationary variables in the model must be integrated processes (their first difference or k-difference must be stationary).

     selected_variables_only
          Only run the smoother on the variables listed just after the estimation command. Default: run the smoother on all the declared endogenous variables.

     cova_compute = INTEGER
          When 0, the covariance matrix of estimated parameters is not computed after the computation of posterior mode (or maximum likelihood). This increases speed of computation in large models during development, when this information is not always necessary. Of course, it will break all successive computations that would require this covariance matrix. Default is 1.

     solve_algo = INTEGER
          See solve_algo.

     order = INTEGER
          See order.

     irf = INTEGER
          See irf. Only used if bayesian_irf is passed.

     irf_shocks = ( VARIABLE_NAME [[,] VARIABLE_NAME ...] )
          See irf_shocks. Only used if bayesian_irf is passed. Cannot be used with dsge_var.

     aim_solver
          See aim_solver.

     sylvester = OPTION
          See sylvester.

     sylvester_fixed_point_tol = DOUBLE
          See sylvester_fixed_point_tol.

     lyapunov = OPTION
          Determines the algorithm used to solve the Laypunov equation to initialized the variance-covariance matrix of the Kalman filter using the steady-state value of state variables. Possible values for OPTION are:

          default
               Uses the default solver for Lyapunov equations based on Bartels-Stewart algorithm.

          fixed_point
               Uses a fixed point algorithm to solve the Lyapunov equation. This method is faster than the default one for large scale models, but it could require a large amount of iterations.

          doubling
               Uses a doubling algorithm to solve the Lyapunov equation (disclyap_fast). This method is faster than the two previous one for large scale models.

          square_root_solver
               Uses a square-root solver for Lyapunov equations (dlyapchol). This method is fast for large scale models (available under MATLAB if the control system toolbox is installed; available under Octave if the control (http://octave.sourceforge.net/control/) package from Octave-Forge is installed)

          Default value is default

     lyapunov_fixed_point_tol = DOUBLE
          This is the convergence criterion used in the fixed point lyapunov solver. Its default value is 1e-10.

     lyapunov_doubling_tol = DOUBLE
          This is the convergence criterion used in the doubling algorithm to solve the lyapunov equation. Its default value is 1e-16.

     Note

     If no mh_jscale parameter is used in estimated_params, the procedure uses mh_jscale for all parameters. If mh_jscale option isn't set, the procedure uses 0.2 for all parameters.

     Output

     After running estimation, the parameters M_.params and the variance matrix M_.Sigma_e of the shocks are set to the mode for maximum likelihood estimation or posterior mode computation without Metropolis iterations.

     After estimation with Metropolis iterations (option mh_replic > 0 or option load_mh_file set) the parameters M_.params and the variance matrix M_.Sigma_e of the shocks are set to the posterior mean.

     Depending on the options, estimation stores results in various fields of the oo_ structure, described below.

     Running the smoother with calibrated parameters

     It is possible to compute smoothed value of the endogenous variables and the shocks with calibrated parameters, without estimation proper. For this usage, there should be no estimated_params block. Observed variables must be declared. A dataset must be specified in the estimation instruction. In addition, use the following options: mode_compute=0,mh_replic=0,smoother. Currently, there is no specific output for this usage of the estimation command. The results are made available in fields of oo_ structure. An example is available in ./tests/smoother/calibrated_model.mod.

In the following variables, we will adopt the following shortcuts for specific field names:

MOMENT_NAME

     This field can take the following values:

     HPDinf
          Lower bound of a 90% HPD interval (See option conf_sig to change the size of the HPD interval)

     HPDsup
          Upper bound of a 90% HPD interval

     Mean
          Mean of the posterior distribution

     Median
          Median of the posterior distribution

     Std
          Standard deviation of the posterior distribution

     deciles
          Deciles of the distribution.

     density
          Non parametric estimate of the posterior density. First and second columns are respectively abscissa and ordinate coordinates.

ESTIMATED_OBJECT

     This field can take the following values:

     measurement_errors_corr
          Correlation between two measurement errors

     measurement_errors_std
          Standard deviation of measurement errors

     parameters
          Parameters

     shocks_corr
          Correlation between two structural shocks

     shocks_std
          Standard deviation of structural shocks

 -- MATLAB/Octave variable: oo_.MarginalDensity.LaplaceApproximation
     Variable set by the estimation command.

 -- MATLAB/Octave variable: oo_.MarginalDensity.ModifiedHarmonicMean
     Variable set by the estimation command, if it is used with mh_replic > 0 or load_mh_file option.

 -- MATLAB/Octave variable: oo_.FilteredVariables
     Variable set by the estimation command, if it is used with the filtered_vars option. Fields are of the form:

          oo_.FilteredVariables.VARIABLE_NAME

 -- MATLAB/Octave variable: oo_.FilteredVariablesKStepAhead
     Variable set by the estimation command, if it is used with the filter_step_ahead option.

 -- MATLAB/Octave variable: oo_.FilteredVariablesKStepAheadVariances
     Variable set by the estimation command, if it is used with the filter_step_ahead option.

 -- MATLAB/Octave variable: oo_.PosteriorIRF.dsge
     Variable set by the estimation command, if it is used with the bayesian_irf option. Fields are of the form:

          oo_.PosteriorIRF.dsge.MOMENT_NAME.VARIABLE_NAME_SHOCK_NAME

 -- MATLAB/Octave variable: oo_.SmoothedMeasurementErrors
     Variable set by the estimation command, if it is used with the smoother option. Fields are of the form:

          oo_.SmoothedMeasurementErrors.VARIABLE_NAME

 -- MATLAB/Octave variable: oo_.SmoothedShocks
     Variable set by the estimation command, if it is used with the smoother option. Fields are of the form:

          oo_.SmoothedShocks.VARIABLE_NAME

 -- MATLAB/Octave variable: oo_.SmoothedVariables
     Variable set by the estimation command, if it is used with the smoother option. Fields are of the form:

          oo_.SmoothedVariables.VARIABLE_NAME

 -- MATLAB/Octave variable: oo_.UpdatedVariables
     Variable set by the estimation command, if it is used with the smoother option. Contains the estimation of the expected value of variables given the information available at the current date. Fields are of the form:

          oo_.UpdatedVariables.VARIABLE_NAME

 -- MATLAB/Octave variable: oo_.PosteriorTheoreticalMoments
     Variable set by the estimation command, if it is used with the moments_varendo option. Fields are of the form:

          oo_.PosteriorTheoreticalMoments.dsge.THEORETICAL_MOMENT.ESTIMATED_OBJECT.MOMENT_NAME.VARIABLE_NAME

     where THEORETICAL_MOMENT is one of the following:

     covariance
          Variance-covariance of endogenous variables

     correlation
          Correlation between endogenous variables

     VarianceDecomposition
          Decomposition of variance (When the shocks are correlated, it is the decomposition of orthogonalized shocks via Cholesky decompostion according to the order of declaration of shocks (see Variable declarations))

     ConditionalVarianceDecomposition
          Only if the conditional_variance_decomposition option has been specified

 -- MATLAB/Octave variable: oo_.posterior_density
     Variable set by the estimation command, if it is used with mh_replic > 0 or load_mh_file option. Fields are of the form:

          oo_.posterior_density.PARAMETER_NAME

 -- MATLAB/Octave variable: oo_.posterior_hpdinf
     Variable set by the estimation command, if it is used with mh_replic > 0 or load_mh_file option. Fields are of the form:

          oo_.posterior_hpdinf.ESTIMATED_OBJECT.VARIABLE_NAME

 -- MATLAB/Octave variable: oo_.posterior_hpdsup
     Variable set by the estimation command, if it is used with mh_replic > 0 or load_mh_file option. Fields are of the form:

          oo_.posterior_hpdsup.ESTIMATED_OBJECT.VARIABLE_NAME

 -- MATLAB/Octave variable: oo_.posterior_mean
     Variable set by the estimation command, if it is used with mh_replic > 0 or load_mh_file option. Fields are of the form:

          oo_.posterior_mean.ESTIMATED_OBJECT.VARIABLE_NAME

 -- MATLAB/Octave variable: oo_.posterior_mode
     Variable set by the estimation command, if it is used with mh_replic > 0 or load_mh_file option. Fields are of the form:

          oo_.posterior_mode.ESTIMATED_OBJECT.VARIABLE_NAME

 -- MATLAB/Octave variable: oo_.posterior_std
     Variable set by the estimation command, if it is used with mh_replic > 0 or load_mh_file option. Fields are of the form:

          oo_.posterior_std.ESTIMATED_OBJECT.VARIABLE_NAME

Here are some examples of generated variables:

     oo_.posterior_mode.parameters.alp
     oo_.posterior_mean.shocks_std.ex
     oo_.posterior_hpdsup.measurement_errors_corr.gdp_conso

 -- Command: model_comparison FILENAME[(DOUBLE)]...;
 -- Command: model_comparison (marginal_density = laplace | modifiedharmonicmean) FILENAME[(DOUBLE)]...;

     Description

     This command computes odds ratios and estimate a posterior density over a collection of models. The priors over models can be specified as the DOUBLE values, otherwise a uniform prior is assumed.

     Example

          model_comparison my_model(0.7) alt_model(0.3);

     This example attributes a 70% prior over my_model and 30% prior over alt_model.

 -- Command: shock_decomposition [VARIABLE_NAME]...;
 -- Command: shock_decomposition (OPTIONS...) [VARIABLE_NAME]...;

     Description

     This command computes and displays shock decomposition according to the model for a given sample.

     Note that this command must come after either estimation (in case of an estimated model) or stoch_simul (in case of a calibrated model).

     Options

     parameter_set = PARAMETER_SET
          Specify the parameter set to use for running the smoother. The PARAMETER_SET can take one of the following five values: calibration, prior_mode, prior_mean, posterior_mode, posterior_mean, posterior_median. Default value: posterior_mean if Metropolis has been run, else posterior_mode.

     shocks = (VARIABLE_NAME [VARIABLE_NAME ...]  [ ; VARIABLE_NAME [VARIABLE_NAME ...] ...] )
          ...

     labels = ( VARIABLE_NAME [VARIABLE_NAME ...] )
          ...

     datafile = FILENAME
          See datafile. Useful when computing the shock decomposition on a calibrated model.

 -- Command: unit_root_vars VARIABLE_NAME...;

     This command is deprecated. Use estimation option diffuse_filter instead for estimating a model with non-stationary observed variables or steady option nocheck to prevent steady to check the steady state returned by your steady state file.

Dynare also has the ability to estimate Bayesian VARs:

 -- Command: bvar_density ;
     Computes the marginal density of an estimated BVAR model, using Minnesota priors.

     See bvar-a-la-sims.pdf, which comes with Dynare distribution, for more information on this command.

 -- Command: calib_smoother [VARIABLE_NAME]...;
 -- Command: calib_smoother (OPTIONS...) [VARIABLE_NAME]...;

     Description

     This command computes the smoothed variables (and possible the filtered variables) on a calibrated model.

     A datafile must be provided, and the observable variables declared with varobs.

     By default, the command computes the smoothed variables and shocks and stores the results in oo_.SmoothedVariables and oo_.SmoothedShocks. It also fills oo_.UpdatedVariables.

     Options

     datafile = FILENAME
          See datafile.

     filtered_vars
          Triggers the computation of filtered variables. See filtered_vars for more details.

     filter_step_ahead = [INTEGER1:INTEGER2]
          See filter_step_ahead.

Forecasting
===========

On a calibrated model, forecasting is done using the forecast command. On an estimated command, use the forecast option of estimation command.

It is also possible to compute forecasts on a calibrated or estimated model for a given constrained path of the future endogenous variables. This is done, from the reduced form representation of the DSGE model, by finding the structural shocks that are needed to match the restricted paths. Use conditional_forecast, conditional_forecast_paths and plot_conditional_forecast for that purpose.

Finally, it is possible to do forecasting with a Bayesian VAR using the bvar_forecast command.

 -- Command: forecast [VARIABLE_NAME...];
 -- Command: forecast (OPTIONS...) [VARIABLE_NAME...];

     Description

     This command computes a simulation of a stochastic model from an arbitrary initial point.

     When the model also contains deterministic exogenous shocks, the simulation is computed conditionaly to the agents knowing the future values of the deterministic exogenous variables.

     forecast must be called after stoch_simul.

     forecast plots the trajectory of endogenous variables. When a list of variable names follows the command, only those variables are plotted. A 90% confidence interval is plotted around the mean trajectory. Use option conf_sig to change the level of the confidence interval.

     Options

     periods = INTEGER
          Number of periods of the forecast. Default: 40

     conf_sig = DOUBLE
           Level of significance for confidence interval. Default: 0.90

     nograph
          Don't display graphics.

     Initial Values

     forecast computes the forecast taking as initial values the values specified in histval (see histval). When no histval block is present, the initial values are the one stated in initval. When initval is followed by command steady, the initial values are the steady state (see steady).

     Output

     The results are stored in oo_.forecast, which is described below.

     Example

          varexo_det tau;
          varexo e;

          ...

          shocks;
          var e; stderr 0.01;
          var tau;
          periods 1:9;
          values -0.15;
          end;

          stoch_simul(irf=0);

          forecast;

 -- MATLAB/Octave variable: oo_.forecast
     Variable set by the forecast command, or by the estimation command if used with the forecast option. Fields are of the form:

          oo_.forecast.FORECAST_MOMENT.VARIABLE_NAME

     where FORECAST_MOMENT is one of the following:

     HPDinf
          Lower bound of a 90% HPD interval (See option conf_sig to change the size of the HPD interval) of forecast due to parameter uncertainty

     HPDsup
          Lower bound of a 90% HPD interval due to parameter uncertainty

     HPDTotalinf
          Lower bound of a 90% HPD interval of forecast due to parameter uncertainty and future shocks (only with the estimation command)

     HPDTotalsup
          Lower bound of a 90% HPD interval due to parameter uncertainty and future shocks (only with the estimation command)

     Mean
          Mean of the posterior distribution of forecasts

     Median
          Median of the posterior distribution of forecasts

     Std
          Standard deviation of the posterior distribution of forecasts

 -- Command: conditional_forecast (OPTIONS...) [VARIABLE_NAME...];

     Description

     This command computes forecasts on an estimated model for a given constrained path of some future endogenous variables. This is done, from the reduced form representation of the DSGE model, by finding the structural shocks that are needed to match the restricted paths. This command has to be called after estimation.

     Use conditional_forecast_paths block to give the list of constrained endogenous, and their constrained future path. Option controlled_varexo is used to specify the structural shocks which will be matched to generate the constrained path.

     Use plot_conditional_forecast to graph the results.

     Options

     parameter_set = calibration | prior_mode | prior_mean | posterior_mode | posterior_mean | posterior_median
          Specify the parameter set to use for the forecasting. No default value, mandatory option.

     controlled_varexo = (VARIABLE_NAME...)
          Specify the exogenous variables to use as control variables. No default value, mandatory option.

     periods = INTEGER
          Number of periods of the forecast. Default: 40.  periods cannot be less than the number of constrained periods.

     replic = INTEGER
          Number of simulations. Default: 5000.

     conf_sig = DOUBLE
          Level of significance for confidence interval. Default: 0.80

     Example

          var y a
          varexo e u;

          ...

          estimation(...);

          conditional_forecast_paths;
          var y;
          periods 1:3, 4:5;
          values 2, 5;
          var a;
          periods 1:5;
          values 3;
          end;

          conditional_forecast(parameter_set = calibration, controlled_varexo = (e, u), replic = 3000);

          plot_conditional_forecast(periods = 10) a y;

 -- Block: conditional_forecast_paths ;

     Describes the path of constrained endogenous, before calling conditional_forecast. The syntax is similar to deterministic shocks in shocks, see conditional_forecast for an example.

     The syntax of the block is the same than the deterministic shocks in the shocks blocks (see Shocks on exogenous variables).

 -- Command: plot_conditional_forecast [VARIABLE_NAME...];
 -- Command: plot_conditional_forecast (periods = INTEGER) [VARIABLE_NAME...];

     Description

     Plots the conditional (plain lines) and unconditional (dashed lines) forecasts.

     To be used after conditional_forecast.

     Options

     periods = INTEGER
          Number of periods to be plotted. Default: equal to periods in conditional_forecast. The number of periods declared in plot_conditional_forecast cannot be greater than the one declared in conditional_forecast.

 -- Command: bvar_forecast ;
     This command computes in-sample or out-sample forecasts for an estimated BVAR model, using Minnesota priors.

     See bvar-a-la-sims.pdf, which comes with Dynare distribution, for more information on this command.

Optimal policy
==============

Dynare has tools to compute optimal policies for various types of objectives. You can either solve for optimal policy under commitment with ramsey_policy, for optimal policy under discretion with discretionary_policy or for optimal simple rule with osr.

 -- Command: osr [VARIABLE_NAME...];
 -- Command: osr (OPTIONS...) [VARIABLE_NAME...];

     Description

     This command computes optimal simple policy rules for linear-quadratic problems of the form:

          \max_\gamma E(y'_tWy_t)

     such that:

          A_1 E_ty_t+1+A_2 y_t+ A_3 y_t-1+C e_t=0

     where:

       * \gamma are parameters to be optimized. They must be elements of matrices A_1, A_2, A_3;

       * y are the endogenous variables;

       * e are the exogenous stochastic shocks;

     The parameters to be optimized must be listed with osr_params.

     The quadratic objectives must be listed with optim_weights.

     This problem is solved using a numerical optimizer.

     Options

     This command accept the same options than stoch_simul (see Computing the stochastic solution).

     The value of the objective is stored in the variable oo_.osr.objective_function, which is described below.

 -- Command: osr_params PARAMETER_NAME...;
     This command declares parameters to be optimized by osr.

 -- Block: optim_weights ;

     This block specifies quadratic objectives for optimal policy problems

     More precisely, this block specifies the nonzero elements of the quadratic weight matrices for the objectives in osr.

     A element of the diagonal of the weight matrix is given by a line of the form:

          VARIABLE_NAME EXPRESSION;

     An off-the-diagonal element of the weight matrix is given by a line of the form:

          VARIABLE_NAME,  VARIABLE_NAME EXPRESSION;

 -- MATLAB/Octave variable: oo_.osr.objective_function
     After an execution of the osr command, this variable contains the value of the objective under optimal policy.

 -- Command: ramsey_policy [VARIABLE_NAME...];
 -- Command: ramsey_policy (OPTIONS...) [VARIABLE_NAME...];

     Description

     This command computes the first order approximation of the policy that maximizes the policy maker objective function submitted to the constraints provided by the equilibrium path of the economy.

     The planner objective must be declared with the planner_objective command.

     See Auxiliary variables, for an explanation of how this operator is handled internally and how this affects the output.

     Options

     This command accepts all options of stoch_simul, plus:

     planner_discount = EXPRESSION
          Declares the discount factor of the central planner. Default: 1.0

     instruments = (VARIABLE_NAME,...)
          Declares instrument variables for the computation of the steady state under optimal policy. Requires a steady_state_model block or a ..._steadystate.m file. See below.

     Note that only first order approximation is available (i.e. order=1 must be specified).

     Output

     This command generates all the output variables of stoch_simul.

     In addition, it stores the value of planner objective function under Ramsey policy in oo_.planner_objective_value.

     Steay state

     Dynare takes advantage of the fact that the Lagrange multipliers appear linearly in the equations of the steady state of the model under optimal policy. Nevertheless, it is in general very difficult to compute the steady state with simply a numerical guess in initval for the endogenous variables.

     It greatly facilitates the computation, if the user provides an analytical solution for the steady state (in steady_state_model block or in a ..._steadystate.m file). In this case, it is necessary to provide a steady state solution CONDITIONAL on the value of the instruments in the optimal policy problem and declared with option instruments. Note that choosing the instruments is partly a matter of interpretation and you can choose instruments that are handy from a mathematical point of view but different from the instruments you would refer to in the analysis of the paper. Typical example is choosing inflation or nominal interest rate as an instrument.

 -- Command: discretionary_policy [VARIABLE_NAME...];
 -- Command: discretionary_policy (OPTIONS...) [VARIABLE_NAME...];

     Description

     This command computes an approximation of the optimal policy under discretion

     Options

     This command accepts the same options than ramsey_policy, plus:

     discretionary_tol = NON-NEGATIVE DOUBLE
          Sets the tolerance level used to assess convergence of the solution algorithm. Default: 1e-7.

 -- Command: planner_objective MODEL_EXPRESSION;

     This command declares the policy maker objective, for use with ramsey_policy or discretionary_policy.

     You need to give the one-period objective, not the discounted lifetime objective. The discount factor is given by the planner_discount option of ramsey_policy and discretionary_policy.

     Note that with this command you are not limited to quadratic objectives: you can give any arbitrary nonlinear expression.

Sensitivity and identification analysis
=======================================

Dynare provides an interface to the global sensitivity analysis (GSA) toolbox (developed by the Joint Research Center (JRC) of the European Commission), which is now part of the official Dynare distribution. The GSA toolbox can be used to answer the following questions:

 1. What is the domain of structural coefficients assuring the stability and determinacy of a DSGE model?

 2. Which parameters mostly drive the fit of, e.g., GDP and which the fit of inflation? Is there any conflict between the optimal fit of one observed series versus another?

 3. How to represent in a direct, albeit approximated, form the relationship between structural parameters and the reduced form of a rational expectations model?

The discussion of the methodologies and their application is described in Ratto (2008).

With respect to the previous version of the toolbox, in order to work properly, the GSA toolbox no longer requires that the Dynare estimation environment is setup.

Sensitivity analysis results are saved locally in <mod_file>/GSA, where <mod_file>.mod is the name of the DYNARE model file.

Sampling
--------

The following binary files are produced:

  * <mod_file>_prior.mat: this file stores information about the analyses performed sampling from the prior ranges, i.e. pprior=1 and ppost=0;

  * <mod_file>_mc.mat: this file stores information about the analyses performed sampling from multivariate normal, i.e. pprior=0 and ppost=0;

  * <mod_file>_post.mat: this file stores information about analyses performed using the Metropolis posterior sample, i.e. ppost=1.

Stability Mapping
-----------------

Figure files produced are of the form <mod_file>_prior_*.fig and store results for stability mapping from prior Monte-Carlo samples:

  * <mod_file>_prior_stab_SA_*.fig: plots of the Smirnov test analyses confronting the cdf of the sample fulfilling Blanchard-Kahn conditions with the cdf of the rest of the sample;

  * <mod_file>_prior_stab_indet_SA_*.fig: plots of the Smirnov test analyses confronting the cdf of the sample producing indeterminacy with the cdf of the original prior sample;

  * <mod_file>_prior_stab_unst_SA_*.fig: plots of the Smirnov test analyses confronting the cdf of the sample producing unstable (explosive roots) behavior with the cdf of the original prior sample;

  * <mod_file>_prior_stable_corr_*.fig: plots of bivariate projections of the sample fulfilling Blanchard-Kahn conditions;

  * <mod_file>_prior_indeterm_corr_*.fig: plots of bivariate projections of the sample producing indeterminacy;

  * <mod_file>_prior_unstable_corr_*.fig: plots of bivariate projections of the sample producing instability;

  * <mod_file>_prior_unacceptable_corr_*.fig: plots of bivariate projections of the sample producing unacceptable solutions, i.e. either instability or indeterminacy or the solution could not be found (e.g. the steady state solution could not be found by the solver).

Similar conventions apply for <mod_file>_mc_*.fig files, obtained when samples from multivariate normal are used.

Reduced Form Mapping
--------------------

The mapping of the reduced form solution forces the use of samples from prior ranges or prior distributions, i.e.: pprior=1 and ppost=0. It uses 250 samples to optimize smoothing parameters and 1000 samples to compute the fit. The rest of the sample is used for out-of-sample validation. One can also load a previously estimated mapping with a new Monte-Carlo sample, to look at the forecast for the new Monte-Carlo sample.

The following synthetic figures are produced:

  * <mod_file>_redform_<endo name>_vs_lags_*.fig: shows bar charts of the sensitivity indices for the ten most important parameters driving the reduced form coefficients of the selected endogenous variables (namendo) versus lagged endogenous variables (namlagendo); suffix log indicates the results for log-transformed entries;

  * <mod_file>_redform_<endo name>_vs_shocks_*.fig: shows bar charts of the sensitivity indices for the ten most important parameters driving the reduced form coefficients of the selected endogenous variables (namendo) versus exogenous variables (namexo); suffix log indicates the results for log-transformed entries;

  * <mod_file>_redform_GSA(_log).fig: shows bar chart of all sensitivity indices for each parameter: this allows one to notice parameters that have a minor effect for any of the reduced form coefficients.

Detailed results of the analyses are shown in the subfolder <mod_file>/GSA/redform_stab, where the detailed results of the estimation of the single functional relationships between parameters \theta and reduced form coefficient are stored in separate directories named as:

  * <namendo>_vs_<namlagendo>: for the entries of the transition matrix;

  * <namendo>_vs_<namexo>: for entries of the matrix of the shocks.

Moreover, analyses for log-transformed entries are denoted with the following suffixes (y denotes the generic reduced form coefficient):

  * log: y^* = \log(y);

  * minuslog: y^* = \log(-y);

  * logsquared: y^* = \log(y^2) for symmetric fat tails;

  * logskew: y^* = \log(|y + \lambda|) for asymmetric fat tails.

The optimal type of transformation is automatically selected without the need of user intervention.

RMSE
----

The RMSE analysis can be performed with different types of sampling options:

 1. When pprior=1 and ppost=0, the toolbox analyzes the RMSEs for the Monte-Carlo sample obtained by sampling parameters from their prior distributions (or prior ranges): this analysis provides some hints about what parameter drives the fit of which observed series, prior to the full estimation;

 2. When pprior=0 and ppost=0, the toolbox analyzes the RMSEs for a multivariate normal Monte-Carlo sample, with covariance matrix based on the inverse Hessian at the optimum: this analysis is useful when maximum likelihood estimation is done (i.e. no Bayesian estimation);

 3. When ppost=1 the toolbox analyzes the RMSEs for the posterior sample obtained by Dynare's Metropolis procedure.

The use of cases 2 and 3 requires an estimation step beforehand. To facilitate the sensitivity analysis after estimation, the dynare_sensitivity command also allows you to indicate some options of the estimation command. These are:

  * datafile

  * nobs

  * first_obs

  * prefilter

  * presample

  * nograph

  * conf_sig

  * loglinear

  * mode_file

Binary files produced my RMSE analysis are:

  * <mod_file>_prior_*.mat: these files store the filtered and smoothed variables for the prior Monte-Carlo sample, generated when doing RMSE analysis (pprior=1 and ppost=0);

  * <mode_file>_mc_*.mat: these files store the filtered and smoothed variables for the multivariate normal Monte-Carlo sample, generated when doing RMSE analysis (pprior=0 and ppost=0).

Figure files <mod_file>_rmse_*.fig store results for the RMSE analysis.

  * <mod_file>_rmse_prior*.fig: save results for the analysis using prior Monte-Carlo samples;

  * <mod_file>_rmse_mc*.fig: save results for the analysis using multivariate normal Monte-Carlo samples;

  * <mod_file>_rmse_post*.fig: save results for the analysis using Metropolis posterior samples.

The following types of figures are saved (we show prior sample to fix ideas, but the same conventions are used for multivariate normal and posterior):

  * <mod_file>_rmse_prior_*.fig: for each parameter, plots the cdfs corresponding to the best 10% RMSEs of each observed series;

  * <mod_file>_rmse_prior_dens_*.fig: for each parameter, plots the pdfs corresponding to the best 10% RMESs of each observed series;

  * <mod_file>_rmse_prior_<name of observedseries>_corr_*.fig: for each observed series plots the bi-dimensional projections of samples with the best 10% RMSEs, when the correlation is significant;

  * <mod_file>_rmse_prior_lnlik*.fig: for each observed series, plots in red the cdf of the log-likelihood corresponding to the best 10% RMSEs, in green the cdf of the rest of the sample and in blue the cdf of the full sample; this allows one to see the presence of some idiosyncratic behavior;

  * <mod_file>_rmse_prior_lnpost*.fig: for each observed series, plots in red the cdf of the log-posterior corresponding to the best 10% RMSEs, in green the cdf of the rest of the sample and in blue the cdf of the full sample; this allows one to see idiosyncratic behavior;

  * <mod_file>_rmse_prior_lnprior*.fig: for each observed series, plots in red the cdf of the log-prior corresponding to the best 10% RMSEs, in green the cdf of the rest of the sample and in blue the cdf of the full sample; this allows one to see idiosyncratic behavior;

  * <mod_file>_rmse_prior_lik_SA_*.fig: when lik_only=1, this shows the Smirnov tests for the filtering of the best 10% log-likelihood values;

  * <mod_file>_rmse_prior_post_SA_*.fig: when lik_only=1, this shows the Smirnov test for the filtering of the best 10% log-posterior values.

Screening Analysis
------------------

Screening analysis does not require any additional options with respect to those listed in Sampling Options. The toolbox performs all the analyses required and displays results.

The results of the screening analysis with Morris sampling design are stored in the subfolder <mod_file>/GSA/SCREEN. The data file <mod_file>_prior stores all the information of the analysis (Morris sample, reduced form coefficients, etc.).

Screening analysis merely concerns reduced form coefficients. Similar synthetic bar charts as for the reduced form analysis with Monte-Carlo samples are saved:

  * <mod_file>_redform_<endo name>_vs_lags_*.fig: shows bar charts of the elementary effect tests for the ten most important parameters driving the reduced form coefficients of the selected endogenous variables (namendo) versus lagged endogenous variables (namlagendo);

  * <mod_file>_redform_<endo name>_vs_shocks_*.fig: shows bar charts of the elementary effect tests for the ten most important parameters driving the reduced form coefficients of the selected endogenous variables (namendo) versus exogenous variables (namexo);

  * <mod_file>_redform_screen.fig: shows bar chart of all elementary effect tests for each parameter: this allows one to identify parameters that have a minor effect for any of the reduced form coefficients.

Identification Analysis
-----------------------

Setting the option identification=1, an identification analysis based on theoretical moments is performed. Sensitivity plots are provided that allow to infer which parameters are most likely to be less identifiable.

Prerequisite for properly running all the identification routines, is the keyword identification; in the Dynare model file. This keyword triggers the computation of analytic derivatives of the model with respect to estimated parameters and shocks. This is required for option morris=2, which implements Iskrev (2010) identification analysis.

For example, the placing identification; dynare_sensitivity(identification=1, morris=2); in the Dynare model file trigger identification analysis using analytic derivatives Iskrev (2010), jointly with the mapping of the acceptable region.

The identification analysis with derivatives can also be triggered by the commands identification; This does not do the mapping of acceptable regions for the model and uses the standard random sampler of Dynare. It completely offsets any use of the sensitivity analysis toolbox.

Performing Sensitivity and Identification Analysis
--------------------------------------------------

 -- Command: dynare_sensitivity ;
 -- Command: dynare_sensitivity (OPTIONS...);

     Description

     This command triggers sensitivity analysis on a DSGE model.

     Options Sampling Options

     nsam = INTEGER
          Size of the Monte-Carlo sample. Default: 2048

     ilptau = INTEGER
          If equal to 1, use LP_\tau quasi-Monte-Carlo. If equal to 0, use LHS Monte-Carlo. Default: 1

     pprior = INTEGER
          If equal to 1, sample from the prior distributions. If equal to 0, sample from the multivariate normal N(\bar\theta,\Sigma), where \bar\theta is the posterior mode and \Sigma=H^-1, H is the Hessian at the mode. Default: 1

     prior_range = INTEGER
          If equal to 1, sample uniformly from prior ranges. If equal to 0, sample from prior distributions. Default: 1

     morris = INTEGER
          If equal to 0, ANOVA mapping (Type I error) If equal to 1, Screening analysis (Type II error) If equal to 2, Analytic derivatives (similar to Type II error, only valid when identification=1).Default: 1 when identification=1, 0 otherwise

     morris_nliv = INTEGER
          Number of levels in Morris design. Default: 6

     morris_ntra = INTEGER
          Number trajectories in Morris design. Default: 20

     ppost = INTEGER
          If equal to 1, use Metropolis posterior sample. If equal to 0, do not use Metropolis posterior sample. NB: This overrides any other sampling option. Default: 0

     neighborhood_width = DOUBLE
          When pprior=0 and ppost=0, allows for the sampling of parameters around the value specified in the mode_file, in the range xparam1\pm\left|xparam1\timesneighborhood_width\right|. Default: 0

     Stability Mapping Options

     stab = INTEGER
          If equal to 1, perform stability mapping. If equal to 0, do not perform stability mapping. Default: 1

     load_stab = INTEGER
          If equal to 1, load a previously created sample. If equal to 0, generate a new sample. Default: 0

     alpha2_stab = DOUBLE
          Critical value for correlations \rho in filtered samples: plot couples of parmaters with \left|\rho\right|> alpha2_stab. Default: 0.3

     ksstat = DOUBLE
          Critical value for Smirnov statistics d: plot parameters with d> ksstat. Default: 0.1

     pvalue_ks = DOUBLE
          The threshold pvalue for significant Kolmogorov-Smirnov test (i.e. plot parameters with pvalue< pvalue_ks). Default: 0.001

     pvalue_corr = DOUBLE
          The threshold pvalue for significant correlation in filtered samples (i.e. plot bivariate samples when pvalue< pvalue_corr). Default: 0.001

     Reduced Form Mapping Options

     redform = INTEGER
          If equal to 1, prepare Monte-Carlo sample of reduced form matrices. If equal to 0, do not prepare Monte-Carlo sample of reduced form matrices. Default: 0

     load_redform = INTEGER
          If equal to 1, load previously estimated mapping. If equal to 0, estimate the mapping of the reduced form model. Default: 0

     logtrans_redform = INTEGER
          If equal to 1, use log-transformed entries. If equal to 0, use raw entries. Default: 0

     threshold_redform = [DOUBLE DOUBLE]
          The range over which the filtered Monte-Carlo entries of the reduced form coefficients should be analyzed. The first number is the lower bound and the second is the upper bound. An empty vector indicates that these entries will not be filtered. Default: empty

     ksstat_redform = DOUBLE
          Critical value for Smirnov statistics d when reduced form entries are filtered. Default: 0.1

     alpha2_redform = DOUBLE
          Critical value for correlations \rho  when reduced form entries are filtered. Default: 0.3

     namendo = (VARIABLE_NAME...)
          List of endogenous variables. `:' indicates all endogenous variables. Default: empty

     namlagendo = (VARIABLE_NAME...)
          List of lagged endogenous variables. `:' indicates all lagged endogenous variables. Analyze entries [namendo\timesnamlagendo] Default: empty

     namexo = (VARIABLE_NAME...)
          List of exogenous variables. `:' indicates all exogenous variables. Analyze entries [namendo\timesnamexo]. Default: empty

     RMSE Options

     rmse = INTEGER
          If equal to 1, perform RMSE analysis. If equal to 0, do not perform RMSE analysis. Default: 0

     load_rmse = INTEGER
          If equal to 1, load previous RMSE analysis. If equal to 0, make a new RMSE analysis. Default: 0

     lik_only = INTEGER
          If equal to 1, compute only likelihood and posterior. If equal to 0, compute RMSE's for all observed series. Default: 0

     var_rmse = (VARIABLE_NAME...)
          List of observed series to be considered. `:' indicates all observed variables. Default: varobs

     pfilt_rmse = DOUBLE
          Filtering threshold for RMSE's. Default: 0.1

     istart_rmse = INTEGER
          Value at which to start computing RMSE's (use 2 to avoid big intitial error). Default: presample+1

     alpha_rmse = DOUBLE
          Critical value for Smirnov statistics d: plot parameters with d> alpha_rmse. Default: 0.002

     alpha2_rmse = DOUBLE
          Critical value for correlation \rho: plot couples of parmaters with \left|\rho\right|= alpha2_rmse. Default: 1.0

     datafile = FILENAME
          See datafile.

     nobs = INTEGER

     nobs = [INTEGER1:INTEGER2]
          See nobs.

     first_obs = INTEGER
          See first_obs.

     prefilter = INTEGER
          See prefilter.

     presample = INTEGER
          See presample.

     nograph
          See nograph.

     conf_sig = DOUBLE
          See conf_sig.

     loglinear
          See loglinear.

     mode_file = FILENAME
          See mode_file.

     kalman_algo = INTEGER
          See kalman_algo.

     Identification Analysis Options

     identification = INTEGER
          If equal to 1, performs identification anlysis (forcing redform=0 and morris=1) If equal to 0, no identification analysis. Default: 0

     morris = INTEGER
          See morris.

     morris_nliv = INTEGER
          See morris_nliv.

     morris_ntra = INTEGER
          See morris_ntra.

     load_ident_files = INTEGER
          Loads previously performed identification analysis. Default: 0

     useautocorr = INTEGER
          Use autocorrelation matrices in place of autocovariance matrices in moments for identification analysis. Default: 0

     ar = INTEGER
          Maximum number of lags for moments in identification analysis. Default: 1

     lik_init = INTEGER
          See lik_init.

 -- Command: identification ;
 -- Command: identification (OPTIONS...);

     Description

     This command triggers identification analysis.

     Options

     ar = INTEGER
          Number of lags of computed autocorrelations (theoretical moments). Default: 1

     useautocorr = INTEGER
          If equal to 1, compute derivatives of autocorrelation. If equal to 0, compute derivatives of autocovariances. Default: 0

     load_ident_files = INTEGER
          If equal to 1, allow Dynare to load previously computed analyzes. Default: 0

     prior_mc = INTEGER
          Size of Monte-Carlo sample. Default: 1

     prior_range = INTEGER
          Triggers uniform sample within the range implied by the prior specifications (when prior_mc>1). Default: 0

     advanced = INTEGER
          Shows a more detailed analysis, comprised of an analysis for the linearized rational expectation model as well as the associated reduced form solution. Further performs a brute force search of the groups of parameters best reproducing the behavior of each single parameter. The maximum dimension of the group searched is triggered by max_dim_cova_group. Default: 0

     max_dim_cova_group = INTEGER
          In the brute force search (performed when advanced=1) this option sets the maximum dimension of groups of parameters that best reproduce the behavior of each single model parameter. Default: 2

     periods = INTEGER
          When the analytic Hessian is not available (i.e. with missing values or diffuse Kalman filter or univariate Kalman filter), this triggers the length of stochastic simulation to compute Simulated Moments Uncertainty. Default: 300

     replic = INTEGER
          When the analytic Hessian is not available, this triggers the number of replicas to compute Simulated Moments Uncertainty. Default: 100

     gsa_sample_file = INTEGER
          If equal to 0, do not use sample file. If equal to 1, triggers gsa prior sample. If equal to 2, triggers gsa Monte-Carlo sample (i.e. loads a sample corresponding to pprior=0 and ppost=0 in the dynare_sensitivity options). Default: 0

     gsa_sample_file = FILENAME
          Uses the provided path to a specific user defined sample file. Default: 0

     parameter_set = calibration | prior_mode | prior_mean | posterior_mode | posterior_mean | posterior_median
          Specify the parameter set to use. Default: prior_mean

     lik_init = INTEGER
          See lik_init.

     kalman_algo = INTEGER
          See kalman_algo.

Markov-switching SBVAR
======================

Given a list of variables, observed variables and a data file, Dynare can be used to solve a Markov-switching SBVAR model according to Sims, Waggoner and Zha (2008). Having done this, you can create forecasts and compute the marginal data density, regime probabilities, IRFs, and variance decomposition of the model.

The commands have been modularized, allowing for multiple calls to the same command within a <mod_file>.mod file. The default is to use <mod_file> to tag the input (output) files used (produced) by the program. Thus, to call any command more than once within a <mod_file>.mod file, you must use the *_tag options described below.

 -- Command: markov_switching (OPTIONS...);

     Description

     Declares the Markov state variable information of a Markov-switching SBVAR model.

     Options

     chain = INTEGER
           The Markov chain. Default: none

     state = INTEGER
          This state has duration equal to duration. Exactly one of state and number_of_states must be passed. Default: none

     number_of_states = INTEGER
          Total number of states. Implies that all states have the same duration. Exactly one of state and number_of_states must be passed. Default: none

     duration = DOUBLE | inf
          The duration of the state or states. Default: none

 -- Command: svar (OPTIONS...);

     Description

     Each Makov chain can control the switching of a set of parameters. We allow the parameters to be divided equation by equation and by variance or slope and intercept.

     Options

     coefficients
          Specifies that only the slope and intercept in the given equations are controlled by the given chain.  One, but not both, of coefficients or variances must appear. Default: none

     variances
          Specifies that only variances in the given equations are controlled by the given chain. One, but not both, of coefficients or variances must appear. Default: none

     equations
          Defines the equation controlled by the given chain. If not specificed, then all equations are controlled by chain. Default: none

     chain = INTEGER
          Specifies a Markov chain defined by markov_switching. Default: none

 -- Command: ms_estimation (OPTIONS...);

     Description

     Triggers the creation of an initialization file for, and the estimation of, a Markov-switching SBVAR model. At the end of the run, the A^0, A^+, Q and \zeta matrices are contained in the oo_.ms structure.

     Options

     General Options

     file_tag = FILENAME
          The portion of the filename associated with this run. This will create the model initialization file, init_<file_tag>.dat. Default: <mod_file>

     output_file_tag = FILENAME
          The portion of the output filename that will be assigned to this run. This will create, among other files, est_final_<output_file_tag>.out, est_intermediate_<output_file_tag>.out. Default: <file_tag>

     no_create_init
          Do not create an initialization file for the model. Passing this option will cause the Initialization Options to be ignored. Further, the model will be generated from the output files associated with the previous estimation run (i.e. est_final_<file_tag>.out, est_intermediate_<file_tag>.out or init_<file_tag>.dat, searched for in sequential order). This functionality can be useful for continuing a previous estimation run to ensure convergence was reached or for reusing an initialization file. NB: If this option is not passed, the files from the previous estimation run will be overwritten. Default: off (i.e. create initialization file)

     Initialization Options

     coefficients_prior_hyperparameters = [DOUBLE1 DOUBLE2 DOUBLE3 DOUBLE4 DOUBLE5 DOUBLE6]
          Sets the hyper parameters for the model. The six elements of the argument vector have the following interpretations:

          Position
               Interpretation

          1
               Overall tightness for A^0 and A^+

          2
               Relative tightness for A^+

          3
               Relative tightness for the constant term

          4
               Tightness on lag decay (range: 1.2 - 1.5); a faster decay produces better inflation process

          5
               Weight on nvar sums of coeffs dummy observations (unit roots)

          6
               Weight on single dummy initial observation including constant

          Default: [1.0 1.0 0.1 1.2 1.0 1.0]

     freq = INTEGER | monthly | quarterly | yearly
          Frequency of the data (e.g. monthly, 12). Default: 4

     initial_year = INTEGER
          The first year of data. Default: none

     initial_subperiod = INTEGER
          The first period of data (i.e. for quarterly data, an integer in [1,4]). Default: 1

     final_year = INTEGER
          The last year of data. Default: none

     final_subperiod = INTEGER
          The final period of data (i.e. for monthly data, an integer in [1,12]. Default: 4

     datafile = FILENAME
          See datafile.

     xls_sheet = NAME
          See xls_sheet.

     xls_range = RANGE
          See xls_range.

     nlags = INTEGER
          The number of lags in the model. Default: 1

     cross_restrictions
          Use cross A^0 and A^+ restrictions. Default: off

     contemp_reduced_form
          Use contemporaneous recursive reduced form. Default: off

     no_bayesian_prior
          Do not use bayesian prior. Default: off (i.e. use bayesian prior)

     alpha = INTEGER
          Alpha value for squared time-varying structural shock lambda. Default: 1

     beta = INTEGER
          Beta value for squared time-varying structural shock lambda. Default: 1

     gsig2_lmdm = INTEGER
          The variance for each independent \lambda parameter under SimsZha restrictions. Default: 50^2

     specification = sims_zha | none
          This controls how restrictions are imposed to reduce the number of parameters. Default: Random Walk

     Estimation Options

     convergence_starting_value = DOUBLE
          This is the tolerance criterion for convergence and refers to changes in the objective function value. It should be rather loose since it will gradually be tighened during estimation. Default: 1e-3

     convergence_ending_value = DOUBLE
          The convergence criterion ending value. Values much smaller than square root machine epsilon are probably overkill. Default: 1e-6

     convergence_increment_value = DOUBLE
          Determines how quickly the convergence criterion moves from the starting value to the ending value. Default: 0.1

     max_iterations_starting_value = INTEGER
          This is the maximum number of iterations allowed in the hill-climbing optimization routine and should be rather small since it will gradually be increased during estimation. Default: 50

     max_iterations_increment_value = DOUBLE
          Determines how quickly the maximum number of iterations is increased. Default: 2

     max_block_iterations = INTEGER
           The parameters are divided into blocks and optimization proceeds over each block. After a set of blockwise optimizations are performed, the convergence criterion is checked and the blockwise optimizations are repeated if the criterion is violated. This controls the maximum number of times the blockwise optimization can be performed. Note that after the blockwise optimizations have converged, a single optimization over all the parameters is performed before updating the convergence value and maximum number of iterations. Default: 100

     max_repeated_optimization_runs = INTEGER
          The entire process described by max_block_iterations is repeated until improvement has stopped. This is the maximum number of times the process is allowed to repeat. Set this to 0 to not allow repetitions. Default: 10

     function_convergence_criterion = DOUBLE
          The convergence criterion for the objective function when max_repeated_optimizations_runs is positive. Default: 0.1

     parameter_convergence_criterion = DOUBLE
          The convergence criterion for parameter values when max_repeated_optimizations_runs is positive. Default: 0.1

     number_of_large_perturbations = INTEGER
          The entire process described by max_block_iterations is repeated with random starting values drawn from the posterior. This specifies the number of random starting values used. Set this to 0 to not use random starting values. A larger number should be specified to ensure that the entire parameter space has been covererd. Default: 5

     number_of_small_perturbations = INTEGER
          The number of small perturbations to make after the large perturbations have stopped improving. Setting this number much above 10 is probably overkill. Default: 5

     number_of_posterior_draws_after_perturbation = INTEGER
          The number of consecutive posterior draws to make when producing a small perturbation. Because the posterior draws are serially correlated, a small number will result in a small perturbation. Default: 1

     max_number_of_stages = INTEGER
          The small and large perturbation are repeated until improvement has stopped. This specifices the maximum number of stages allowed. Default: 20

     random_function_convergence_criterion = DOUBLE
          The convergence criterion for the objective function when number_of_large_perturbations is positive. Default: 0.1

     random_parameter_convergence_criterion = DOUBLE
          The convergence criterion for parameter values when number_of_large_perturbations is positive. Default: 0.1

Example

     ms_estimation(datafile=data, initial_year=1959, final_year=2005,
     nlags=4, max_repeated_optimization_runs=1, max_number_of_stages=0);

     ms_estimation(file_tag=second_run, datafile=data, initial_year=1959,
     final_year=2005, nlags=4, max_repeated_optimization_runs=1,
     max_number_of_stages=0);

     ms_estimation(file_tag=second_run, output_file_tag=third_run,
     no_create_init, max_repeated_optimization_runs=5,
     number_of_large_perturbations=10);

 -- Command: ms_simulation ;
 -- Command: ms_simulation (OPTIONS...);

     Description

     Simulates a Markov-switching SBVAR model.

     Options

     file_tag = FILENAME
           The portion of the filename associated with the ms_estimation run. Default: <mod_file>

     output_file_tag = FILENAME
           The portion of the output filename that will be assigned to this run. Default: <file_tag>

     mh_replic = INTEGER
          The number of draws to save. Default: 10,000

     drop = INTEGER
          The number of burn-in draws. Default: 0.1*mh_replic*thinning_factor

     thinning_factor = INTEGER
          The total number of draws is equal to thinning_factor*mh_replic+drop.  Default: 1

     adaptive_mh_draws = INTEGER
          Tuning period for Metropolis-Hasting draws. Default: 30,000

Example

     ms_simulation(file_tag=second_run);

     ms_simulation(file_tag=third_run, mh_replic=5000, thinning_factor=3);

 -- Command: ms_compute_mdd ;
 -- Command: ms_compute_mdd (OPTIONS...);

     Description

     Computes the marginal data density of a Markov-switching SBVAR model from the posterior draws. At the end of the run, the Muller and Bridged log marginal densities are contained in the oo_.ms structure.

     Options

     file_tag = FILENAME
          See file_tag.

     output_file_tag = FILENAME
          See output_file_tag.

     simulation_file_tag = FILENAME
           The portion of the filename associated with the simulation run.  Defualt: <file_tag>

     proposal_type = INTEGER
          The proposal type:

          1
               Gaussian

          2
               Power

          3
               Truncated Power

          4
               Step

          5
               Truncated Gaussian

          Default: 3

     proposal_lower_bound = DOUBLE
          The lower cutoff in terms of probability. Not used for proposal_type in [1,2]. Required for all other proposal types. Default: 0.1

     proposal_upper_bound = DOUBLE
          The upper cutoff in terms of probability. Not used for proposal_type equal to 1. Required for all other proposal types. Default: 0.9

     mdd_proposal_draws = INTEGER
          The number of proposal draws. Default: 100,000

     mdd_use_mean_center
          Use the posterior mean as center. Default: off

 -- Command: ms_compute_probabilities ;
 -- Command: ms_compute_probabilities (OPTIONS...);

     Description

     Computes smoothed regime probabilities of a Markov-switching SBVAR model. Output .eps files are contained in <output_file_tag/Output/Probabilities>.

     Options

     file_tag = FILENAME
          See file_tag.

     output_file_tag = FILENAME
          See output_file_tag.

     filtered_probabilities
          Filtered probabilities are computed instead of smoothed. Default: off

     real_time_smoothed
          Smoothed probabilities are computed based on time t information for 0\le t\le nobs. Default: off

 -- Command: ms_irf ;
 -- Command: ms_irf (OPTIONS...);

     Description

     Computes impulse response functions for a Markov-switching SBVAR model. Output .eps files are contained in <output_file_tag/Output/IRF>, while data files are contained in <output_file_tag/IRF>.

     Options

     file_tag = FILENAME
          See file_tag.

     output_file_tag = FILENAME
          See output_file_tag.

     simulation_file_tag = FILENAME
          See simulation_file_tag.

     horizon = INTEGER
           The forecast horizon. Default: 12

     filtered_probabilities
           Uses filtered probabilities at the end of the sample as initial conditions for regime probabilities. Only one of filtered_probabilities, regime and regimes may be passed. Default: off

     error_band_percentiles = [DOUBLE1 ...]
           The percentiles to compute. Default: [0.16 0.50 0.84]. If median is passed, the default is [0.5]

     shock_draws = INTEGER
           The number of regime paths to draw. Default: 10,000

     shocks_per_parameter = INTEGER
           The number of regime paths to draw under parameter uncertainty. Default: 10

     thinning_factor = INTEGER
           Only 1/thinning_factor of the draws in posterior draws file are used. Default: 1

     free_parameters = NUMERICAL_VECTOR
           A vector of free parameters to initialize theta of the model. Default: use estimated parameters

     parameter_uncertainty
           Calculate IRFs under parameter uncertainty. Requires that ms_simulation has been run. Default: off

     regime = INTEGER
           Given the data and model parameters, what is the ergodic probability of being in the specified regime. Only one of filtered_probabilities, regime and regimes may be passed. Default: off

     regimes
           Describes the evolution of regimes. Only one of filtered_probabilities, regime and regimes may be passed. Default: off

     median
           A shortcut to setting error_band_percentiles=[0.5]. Default: off

 -- Command: ms_forecast ;
 -- Command: ms_forecast (OPTIONS...);

     Description

     Generates forecasts for a Markov-switching SBVAR model. Output .eps files are contained in <output_file_tag/Output/Forecast>, while data files are contained in <output_file_tag/Forecast>.

     Options

     file_tag = FILENAME
          See file_tag.

     output_file_tag = FILENAME
          See output_file_tag.

     simulation_file_tag = FILENAME
          See simulation_file_tag.

     data_obs_nbr = INTEGER
          The number of data points included in the output. Default: 0

     error_band_percentiles = [DOUBLE1 ...]
          See error_band_percentiles.

     shock_draws = INTEGER
          See shock_draws.

     shocks_per_parameter = INTEGER
          See shocks_per_parameter.

     thinning_factor = INTEGER
          See thinning_factor.

     free_parameters = NUMERICAL_VECTOR
          See free_parameters.

     parameter_uncertainty
          See parameter_uncertainty.

     regime = INTEGER
          See regime.

     regimes

          See regimes.

     median

          See median.

 -- Command: ms_variance_decomposition ;
 -- Command: ms_variance_decomposition (OPTIONS...);

     Description

     Computes the variance decomposition for a Markov-switching SBVAR model. Output .eps files are contained in <output_file_tag/Output/Variance_Decomposition>, while data files are contained in <output_file_tag/Variance_Decomposition>.

     Options

     file_tag = FILENAME
          See file_tag.

     output_file_tag = FILENAME
          See output_file_tag.

     simulation_file_tag = FILENAME
          See simulation_file_tag.

     horizon = INTEGER
          See horizon.

     filtered_probabilities
          See filtered_probabilities.

     no_error_bands
          Do not output percentile error bands (i.e. compute mean). Default: off (i.e. output error bands)

     error_band_percentiles = [DOUBLE1 ...]
          See error_band_percentiles.

     shock_draws = INTEGER
          See shock_draws.

     shocks_per_parameter = INTEGER
          See shocks_per_parameter.

     thinning_factor = INTEGER
          See thinning_factor.

     free_parameters = NUMERICAL_VECTOR
          See free_parameters.

     parameter_uncertainty
          See parameter_uncertainty.

     regime = INTEGER
          See regime.

     regimes

          See regimes.

Displaying and saving results
=============================

Dynare has comments to plot the results of a simulation and to save the results.

 -- Command: rplot VARIABLE_NAME...;

     Plots the simulated path of one or several variables, as stored in OO_.ENDO_SIMUL by either SIMUL (see Deterministic simulation) or STOCH_SIMUL with option PERIODS (see Computing the stochastic solution). The variables are plotted in levels.

 -- Command: dynatype (FILENAME) [VARIABLE_NAME...];
     This command prints the listed variables in a text file named FILENAME. If no VARIABLE_NAME is listed, all endogenous variables are printed.

 -- Command: dynasave (FILENAME) [VARIABLE_NAME...];

     This command saves the listed variables in a binary file named FILENAME. If no VARIABLE_NAME are listed, all endogenous variables are saved.

     In MATLAB or Octave, variables saved with the dynasave command can be retrieved by the command:

          load -mat FILENAME

Macro-processing language
=========================

It is possible to use ``macro'' commands in the .mod file for doing the following tasks: including modular source files, replicating blocks of equations through loops, conditionally executing some code, writing indexed sums or products inside equations...

The Dynare macro-language provides a new set of macro-commands which can be inserted inside .mod files. It features:

  * file inclusion

  * loops (for structure)

  * conditional inclusion (if/then/else structures)

  * expression substitution

Technically, this macro language is totally independent of the basic Dynare language, and is processed by a separate component of the Dynare pre-processor. The macro processor transforms a .mod file with macros into a .mod file without macros (doing expansions/inclusions), and then feeds it to the Dynare parser. The key point to understand is that the macro-processor only does text substitution (like the C preprocessor or the PHP language).  Note that it is possible to see the output of the macro-processor by using the savemacro option of the dynare command (see Dynare invocation).

The macro-processor is invoked by placing macro directives in the .mod file. Directives begin with an at-sign followed by a pound sign (@#). They produce no output, but give instructions to the macro-processor. In most cases, directives occupy exactly one line of text. In case of need, two anti-slashes (\\) at the end of the line indicates that the directive is continued on the next line. The main directives are:

  * @#include, for file inclusion,

  * @#define, for defining a macro-processor variable,

  * @#if, @#ifdef, @#else, @#endif for conditional statements,

  * @#for, @#endfor for constructing loops.

The macro-processor maintains its own list of variables (distinct of model variables and of MATLAB/Octave variables). These macro-variables are assigned using the @#define directive, and can be of four types: integer, character string, array of integers, array of strings.

Macro expressions
-----------------

It is possible to construct macro-expressions which can be assigned to macro-variables or used within a macro-directive. The expressions are constructed using literals of the four basic types (integers, strings, arrays of strings, arrays of integers), macro-variables names and standard operators.

String literals have to be enclosed between double quotes (like "name"). Arrays are enclosed within brackets, and their elements are separated by commas (like [1,2,3] or ["US", "EA"]).

Note that there is no boolean type: false is represented by integer zero and true is any non-null integer.

The following operators can be used on integers:

  * arithmetic operators: +, -, *, /

  * comparison operators: <, >, <=, >=, ==, !=

  * logical operators: &&, ||, !

  * integer ranges, using the following syntax: INTEGER1:INTEGER2 (for example, 1:4 is equivalent to integer array [1,2,3,4])

The following operators can be used on strings:

  * comparison operators: ==, !=

  * concatenation of two strings: +

  * extraction of substrings: if S is a string, then S[3] is a string containing only the third character of S, and S[4:6] contains the characters from 4th to 6th

The following operators can be used on arrays:

  * dereferencing: if V is an array, then V[2] is its 2nd element

  * concatenation of two arrays: +

  * difference -: returns the first operand from which the elements of the second operand have been removed

  * extraction of sub-arrays: e.g. V[4:6]

  * testing membership of an array: in operator (for example: "b" in ["a", "b", "c"] returns 1)

Macro-expressions can be used at two places:

  * inside macro directives, directly;

  * in the body of the .mod file, between an at-sign and curly braces (like @{EXPR}): the macro processor will substitute the expression with its value.

In the following, MACRO_EXPRESSION designates an expression constructed as explained above.

Macro directives
----------------

 -- Macro directive: @#include "FILENAME"
     This directive simply includes the content of another file at the place where it is inserted. It is exactly equivalent to a copy/paste of the content of the included file. Note that it is possible to nest includes (i.e. to include a file from an included file).

     Example

          @#include "modelcomponent.mod"

 -- Macro directive: @#define MACRO_VARIABLE = MACRO_EXPRESSION
     Defines a macro-variable.

     Example 1

          @#define x = 5              // Integer
          @#define y = "US"           // String
          @#define v = [ 1, 2, 4 ]    // Integer array
          @#define w = [ "US", "EA" ] // String array
          @#define z = 3 + v[2]       // Equals 5
          @#define t = ("US" in w)    // Equals 1 (true)

     Example 2

          @#define x = [ "B", "C" ]
          @#define i = 2

          model;
            A = @{x[i]};
          end;

     is strictly equivalent to:

          model;
            A = C;
          end;

 -- Macro directive: @#if MACRO_EXPRESSION
 -- Macro directive: @#ifdef MACRO_VARIABLE
 -- Macro directive: @#else
 -- Macro directive: @#endif
     Conditional inclusion of some part of the .mod file. The lines between @#if or @#ifdef and the next @#else or @#endif is executed only if the condition evaluates to a non-null integer. The @#else branch is optional and, if present, is only evaluated if the condition evaluates to 0.

     Example

     Choose between two alternative monetary policy rules using a macro-variable:

          @#define linear_mon_pol = 0 // or 1
          ...
          model;
          @#if linear_mon_pol
            i = w*i(-1) + (1-w)*i_ss + w2*(pie-piestar);
          @#else
            i = i(-1)^w * i_ss^(1-w) * (pie/piestar)^w2;
          @#endif
          ...
          end;

     Example

     Choose between two alternative monetary policy rules using a macro-variable. As linear_mon_pol was not previously defined in this example, the second equation will be chosen:

          model;
          @#ifdef linear_mon_pol
            i = w*i(-1) + (1-w)*i_ss + w2*(pie-piestar);
          @#else
            i = i(-1)^w * i_ss^(1-w) * (pie/piestar)^w2;
          @#endif
          ...
          end;

 -- Macro directive: @#for MACRO_VARIABLE in MACRO_EXPRESSION
 -- Macro directive: @#endfor
     Loop construction for replicating portions of the .mod file. Note that this construct can enclose variable/parameters declaration, computational tasks, but not a model declaration.

     Example

          model;
          @#for country in [ "home", "foreign" ]
            GDP_@{country} = A * K_@{country}^a * L_@{country}^(1-a);
          @#endfor
          end;

     is equivalent to:

          model;
            GDP_home = A * K_home^a * L_home^(1-a);
            GDP_foreign = A * K_foreign^a * L_foreign^(1-a);
          end;

 -- Macro directive: @#echo MACRO_EXPRESSION
     Asks the preprocessor to display some message on standard output. The argument must evaluate to a string.

 -- Macro directive: @#error MACRO_EXPRESSION
     Asks the preprocessor to display some error message on standard output and to abort. The argument must evaluate to a string.

Typical usages
--------------

Modularization
..............

The @#include directive can be used to split .mod files into several modular components.

Example setup:

modeldesc.mod
     Contains variable declarations, model equations and shocks declarations

simul.mod
     Includes modeldesc.mod, calibrates parameters and runs stochastic simulations

estim.mod
     Includes modeldesc.mod, declares priors on parameters and runs bayesian estimation

Dynare can be called on simul.mod and estim.mod, but it makes no sense to run it on modeldesc.mod.

The main advantage is that it is no longer needed to manually copy/paste the whole model (at the beginning) or changes to the model (during development).

Indexed sums or products
........................

The following example shows how to construct a moving average:

     @#define window = 2

     var x MA_x;
     ...
     model;
     ...
     MA_x = 1/@{2*window+1}*(
     @#for i in -window:window
             +x(@{i})
     @#endfor
            );
     ...
     end;

After macro-processing, this is equivalent to:

     var x MA_x;
     ...
     model;
     ...
     MA_x = 1/5*(
             +x(-2)
             +x(-1)
             +x(0)
             +x(1)
             +x(2)
            );
     ...
     end;

Multi-country models
....................

Here is a skeleton example for a multi-country model:

     @#define countries = [ "US", "EA", "AS", "JP", "RC" ]
     @#define nth_co = "US"

     @#for co in countries
     var Y_@{co} K_@{co} L_@{co} i_@{co} E_@{co} ...;
     parameters a_@{co} ...;
     varexo ...;
     @#endfor

     model;
     @#for co in countries
      Y_@{co} = K_@{co}^a_@{co} * L_@{co}^(1-a_@{co});
     ...
     @# if co != nth_co
      (1+i_@{co}) = (1+i_@{nth_co}) * E_@{co}(+1) / E_@{co}; // UIP relation
     @# else
      E_@{co} = 1;
     @# endif
     @#endfor
     end;

Endogeneizing parameters
........................

When doing the steady state calibration of the model, it may be useful to consider a parameter as an endogenous (and vice-versa).

For example, suppose production is defined by a CES function:

y = \left(\alpha^1/\xi \ell^1-1/\xi+(1-\alpha)^1/\xik^1-1/\xi\right)^\xi/(\xi-1)

The labor share in GDP is defined as:

lab_rat = (w \ell)/(p y)

In the model, \alpha is a (share) parameter, and lab_rat is an endogenous variable.

It is clear that calibrating \alpha is not straigthforward; but on the contrary, we have real world data for lab_rat, and it is clear that these two variables are economically linked.

The solution is to use a method called variable flipping, which consist in changing the way of computing the steady state. During this computation, \alpha will be made an endogenous variable and lab_rat will be made a parameter. An economically relevant value will be calibrated for lab_rat, and the solution algorithm will deduce the implied value for \alpha.

An implementation could consist of the following files:

modeqs.mod
     This file contains variable declarations and model equations. The code for the declaration of \alpha and lab_rat would look like:

          @#if steady
           var alpha;
           parameter lab_rat;
          @#else
           parameter alpha;
           var lab_rat;
          @#endif

steady.mod
     This file computes the steady state. It begins with:

          @#define steady = 1
          @#include "modeqs.mod"

     Then it initializes parameters (including lab_rat, excluding \alpha, computes the steady state (using guess values for endogenous, including \alpha, then saves values of parameters and endogenous at steady state in a file, using the save_params_and_steady_state command.

simul.mod
     This file computes the simulation. It begins with:

          @#define steady = 0
          @#include "modeqs.mod"

     Then it loads values of parameters and endogenous at steady state from file, using the load_params_and_steady_state command, and computes the simulations.

MATLAB/Octave loops versus macro-processor loops
------------------------------------------------

Suppose you have a model with a parameter \rho, and you want to make simulations for three values: \rho = 0.8, 0.9, 1. There are several ways of doing this:

With a MATLAB/Octave loop

          rhos = [ 0.8, 0.9, 1];
          for i = 1:length(rhos)
            rho = rhos(i);
            stoch_simul(order=1);
          end

     Here the loop is not unrolled, MATLAB/Octave manages the iterations. This is interesting when there are a lot of iterations.

With a macro-processor loop (case 1)

          rhos = [ 0.8, 0.9, 1];
          @#for i in 1:3
            rho = rhos(@{i});
            stoch_simul(order=1);
          @#endfor

     This is very similar to previous example, except that the loop is unrolled.  The macro-processor manages the loop index but not the data array (rhos).

With a macro-processor loop (case 2)

          @#for rho_val in [ "0.8", "0.9", "1"]
            rho = @{rho_val};
            stoch_simul(order=1);
          @#endfor

     The advantage of this method is that it uses a shorter syntax, since list of values directly given in the loop construct. Note that values are given as character strings (the macro-processor does not know floating point values. The inconvenient is that you can not reuse an array stored in a MATLAB/Octave variable.

Misc commands
=============

 -- Command: set_dynare_seed (INTEGER)
 -- Command: set_dynare_seed ('default')
 -- Command: set_dynare_seed ('reset')
 -- Command: set_dynare_seed ('ALGORITHM', INTEGER)

     Sets the seed used for random number generation.

 -- Command: save_params_and_steady_state (FILENAME);

     For all parameters, endogenous and exogenous variables, stores their value in a text file, using a simple name/value associative table.

       * for parameters, the value is taken from the last parameter initialization

       * for exogenous, the value is taken from the last initval block

       * for endogenous, the value is taken from the last steady state computation (or, if no steady state has been computed, from the last initval block)

     Note that no variable type is stored in the file, so that the values can be reloaded with load_params_and_steady_state in a setup where the variable types are different.

     The typical usage of this function is to compute the steady-state of a model by calibrating the steady-state value of some endogenous variables (which implies that some parameters must be endogeneized during the steady-state computation).

     You would then write a first .mod file which computes the steady state and saves the result of the computation at the end of the file, using save_params_and_steady_state.

     In a second file designed to perform the actual simulations, you would use load_params_and_steady_state just after your variable declarations, in order to load the steady state previously computed (including the parameters which had been endogeneized during the steady state computation).

     The need for two separate .mod files arises from the fact that the variable declarations differ between the files for steady state calibration and for simulation (the set of endogenous and parameters differ between the two); this leads to different var and parameters statements.

     Also note that you can take advantage of the @#include directive to share the model equations between the two files (see Macro-processing language).

 -- Command: load_params_and_steady_state (FILENAME);

     For all parameters, endogenous and exogenous variables, loads their value from a file created with save_params_and_steady_state.

       * for parameters, their value will be initialized as if they had been calibrated in the .mod file

       * for endogenous and exogenous, their value will be initialized as they would have been from an initval block

     This function is used in conjunction with save_params_and_steady_state; see the documentation of that function for more information.

The Configuration File
**********************

The configuration file is used to provide Dynare with information not related to the model (and hence not placed in the model file). At the moment, it is only used when using Dynare to run parallel computations.

On Linux and Mac OS X, the default location of the configuration file is $HOME/.dynare, while on Windows it is %APPDATA%\dynare.ini (typically C:\Documents and Settings\USERNAME\Application Data\dynare.ini under Windows XP, or C:\Users\USERNAME\AppData\dynare.ini under Windows Vista or Windows 7). You can specify a non standard location using the conffile option of the dynare command (see Dynare invocation).

The parsing of the configuration file is case-sensitive and it should take the following form, with each option/choice pair placed on a newline:

     [command0]
     option0 = choice0
     option1 = choice1

     [command1]
     option0 = choice0
     option1 = choice1

The configuration file follows a few conventions (self-explanatory conventions such as USER_NAME have been excluded for concision):

COMPUTER_NAME
     Indicates the valid name of a server (e.g. localhost, server.cepremap.org) or an IP address.

DRIVE_NAME
     Indicates a valid drive name in Windows, without the trailing colon (e.g. C).

PATH
     Indicates a valid path in the underlying operating system (e.g. /home/user/dynare/matlab/).

PATH_AND_FILE
     Indicates a valid path to a file in the underlying operating system (e.g. /usr/local/MATLAB/R2010b/bin/matlab).

BOOLEAN
     Is true or false.

Parallel Configuration
======================

This section explains how to configure Dynare for parallelizing some tasks which require very little inter-process communication.

The parallelization is done by running several MATLAB or Octave processes, either on local or on remote machines. Communication between master and slave processes are done through SMB on Windows and SSH on UNIX. Input and output data, and also some short status messages, are exchanged through network filesystems. Currently the system works only with homogenous grids: only Windows or only Unix machines.

The following routines are currently parallelized:

  * the Metropolis-Hastings algorithm;

  * the Metropolis-Hastings diagnostics;

  * the posterior IRFs;

  * the prior and posterior statistics;

  * some plotting routines.

Note that creating the configuration file is not enough in order to trigger parallelization of the computations: you also need to specify the parallel option to the dynare command. For more details, and for other options related to the parallelization engine, see Dynare invocation.

You also need to verify that the following requirements are met by your cluster (which is composed of a master and of one or more slaves):

For a Windows grid

       * a standard Windows network (SMB) must be in place;

       * PsTools (http://technet.microsoft.com/en-us/sysinternals/bb896649.aspx) must be installed in the path of the master Windows machine;

       * the Windows user on the master machine has to be user of any other slave machine in the cluster, and that user will be used for the remote computations.

For a UNIX grid

       * SSH must be installed on the master and on the slave machines;

       * SSH keys must be installed so that the SSH connection from the master to the slaves can be done without passwords, or using an SSH agent

We now turn to the description of the configuration directives:

 -- Configuration block: [cluster]

     Description

     When working in parallel, [cluster] is required to specify the group of computers that will be used. It is required even if you are only invoking multiple processes on one computer.

     Options

     Name = CLUSTER_NAME
          The reference name of this cluster.

     Members = NODE_NAME[(WEIGHT)] NODE_NAME[(WEIGHT)] ...
          A list of nodes that comprise the cluster with an optional computing weight specified for that node. The computing weight indicates how much more powerful one node is with respect to the others (e.g. n1(2) n2(1) n3(3), means that n1 is two times more powerful than n2 whereas n3 is three times more powerful than n2). Each node is separated by at least one space and the weights are in parenthesis with no spaces separating them from their node.

     Example

          [cluster]
          Name = c1
          Members = n1 n2 n3

          [cluster]
          Name = c2
          Members = n1(4) n2 n3

 -- Configuration block: [node]

     Description

     When working in parallel, [node] is required for every computer that will be used. The options that are required differ, depending on the underlying operating system and whether you are working locally or remotely.

     Options

     Name = NODE_NAME
          The reference name of this node.

     CPUnbr = INTEGER | [INTEGER:INTEGER]
          If just one integer is passed, the number of processors to use. If a range of integers is passed, the specific processors to use (processor counting is defined to begin at one as opposed to zero). Note that using specific processors is only possible under Windows; under Linux and Mac OS X, if a range is passed the same number of processors will be used but the range will be adjusted to begin at one.

     ComputerName = COMPUTER_NAME
          The name or IP address of the node. If you want to run locally, use localhost (case-sensitive).

     Port = INTEGER
          The port number to connect to on the node. The default is empty, meaning that the connection will be made to the default SSH port (22).

     UserName = USER_NAME
          The username used to log into a remote system. Required for remote runs on all platforms.

     Password = PASSWORD
          The password used to log into the remote system. Required for remote runs originating from Windows.

     RemoteDrive = DRIVE_NAME
          The drive to be used for remote computation. Required for remote runs originating from Windows.

     RemoteDirectory = PATH
          The directory to be used for remote computation. Required for remote runs on all platforms.

     DynarePath = PATH
          The path to the matlab subdirectory within the Dynare installation directory. The default is the empty string.

     MatlabOctavePath = PATH_AND_FILE
          The path to the MATLAB or Octave executable. The default value is matlab.

     SingleCompThread = BOOLEAN
          Whether or not to disable MATLAB's native multithreading. The default value is true. Option meaningless under Octave.

     OperatingSystem = OPERATING_SYSTEM
          The operating system associated with a node. Only necessary when creating a cluster with nodes from different operating systems. Possible values are unix or windows. There is no default value.

     Example

          [node]
          Name = n1
          ComputerName = localhost
          CPUnbr = 1

          [node]
          Name = n2
          ComputerName = dynserv.cepremap.org
          CPUnbr = 5
          UserName = usern
          RemoteDirectory = /home/usern/Remote
          DynarePath = /home/usern/dynare/matlab
          MatlabOctavePath = matlab

          [node]
          Name = n3
          ComputerName = dynserv.dynare.org
          Port = 3333
          CPUnbr = [2:4]
          UserName = usern
          RemoteDirectory = /home/usern/Remote
          DynarePath = /home/usern/dynare/matlab
          MatlabOctavePath = matlab

Examples
********

Dynare comes with a database of example .mod files, which are designed to show a broad range of Dynare features, and are taken from academic papers for most of them. You should have these files in the examples subdirectory of your distribution.

Here is a short list of the examples included. For a more complete description, please refer to the comments inside the files themselves.

ramst.mod
     An elementary real business cycle (RBC) model, simulated in a deterministic setup.

example1.mod
example2.mod
     Two examples of a small RBC model in a stochastic setup, presented in Collard (2001) (see the file guide.pdf which comes with Dynare).

fs2000.mod
     A cash in advance model, estimated by Schorfheide (2000).

fs2000_nonstationary.mod
     The same model than fs2000.mod, but written in non-stationary form. Detrending of the equations is done by Dynare.

bkk.mod
     Multi-country RBC model with time to build, presented in Backus, Kehoe and Kydland (1992).

agtrend.mod
     Small open economy RBC model with shocks to the growth trend, presented in Aguiar and Gopinath (2004).

Dynare internal documentation and unitary tests
***********************************************

One can obtain internal documentation of matlab/octave's routines or perform unitary tests using the internals command. This is a new feature, and, at this time, will work properly for a small number of routines. At the top of the (available) matlab/octave routines a commented block for the internal documentation is written in the GNU texinfo documentation format. This block is processed by calling texinfo from matlab. Consequently, texinfo has to be installed on your machine.

 -- MATLAB/Octave command: internals FLAG ROUTINENAME[.m]

     Description

     Depending on the FLAG this command prints internal documentation of a matlab/octave routine or triggers unitary tests associated to this routine.

     Flags

     --info
          Prints on screen the internal documentation of ROUTINENAME (if this routine exists and if this routine has texinfo internal documentation header). If the command is executed in the matlab directory of Dynare, then the path to ROUTINENAME has to be provided.

     --test
          Performs the unitary test associated to  ROUTINENAME (if this routine exists and if the matalab/octave m file has unitary test sections).

     Example

          internals --info particle/local_state_iteration
          internals --test particle/local_state_iteration

Bibliography
************

  * Aguiar, Mark and Gopinath, Gita (2004): ``Emerging Market Business Cycles: The Cycle is the Trend,'' NBER Working Paper, 10734

  * Backus, David K., Patrick J. Kehoe, and Finn E. Kydland (1992): ``International Real Business Cycles,'' Journal of Political Economy, 100(4), 745--775.

  * Boucekkine, Raouf (1995): ``An alternative methodology for solving nonlinear forward-looking models,'' Journal of Economic Dynamics and Control, 19, 711--734.

  * Collard, Fabrice (2001): ``Stochastic simulations with Dynare: A practical guide''.

  * Collard, Fabrice and Michel Juillard (2001a): ``Accuracy of stochastic perturbation methods: The case of asset pricing models,'' Journal of Economic Dynamics and Control, 25, 979--999.

  * Collard, Fabrice and Michel Juillard (2001b): ``A Higher-Order Taylor Expansion Approach to Simulation of Stochastic Forward-Looking Models with an Application to a Non-Linear Phillips Curve,'' Computational Economics, 17, 125--139.

  * Durbin, J. and S. J. Koopman (2001), Time Series Analysis by State Space Methods, Oxford University Press.

  * Fair, Ray and John Taylor (1983): ``Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectation Models,'' Econometrica, 51, 1169--1185.

  * Fernandez-Villaverde, Jesus and Juan Rubio-Ramirez (2004): ``Comparing Dynamic Equilibrium Economies to Data: A Bayesian Approach,'' Journal of Econometrics, 123, 153--187.

  * Ireland, Peter (2004): ``A Method for Taking Models to the Data,'' Journal of Economic Dynamics and Control, 28, 1205--26.

  * Iskrev, Nikolay (2010). ``Local identification in DSGE models,'' Journal of Monetary Economics, 57(2), 189--202.

  * Judd, Kenneth (1996): ``Approximation, Perturbation, and Projection Methods in Economic Analysis'', in Handbook of Computational Economics, ed. by Hans Amman, David Kendrick, and John Rust, North Holland Press, 511--585.

  * Juillard, Michel (1996): ``Dynare: A program for the resolution and simulation of dynamic models with forward variables through the use of a relaxation algorithm,'' CEPREMAP, Couverture Orange, 9602.

  * Kim, Jinill, Sunghyun Kim, Ernst Schaumburg, and Christopher A. Sims (2008): ``Calculating and using second-order accurate solutions of discrete time dynamic equilibrium models,'' Journal of Economic Dynamics and Control, 32(11), 3397--3414.

  * Koopman, S. J. and J. Durbin (2003): ``Filtering and Smoothing of State Vector for Diffuse State Space Models,'' Journal of Time Series Analysis, 24(1), 85--98.

  * Laffargue, Jean-Pierre (1990): ``Résolution d'un modèle macroéconomique avec anticipations rationnelles'', Annales d'Économie et Statistique, 17, 97--119.

  * Lubik, Thomas and Frank Schorfheide (2007): ``Do Central Banks Respond to Exchange Rate Movements? A Structural Investigation,'' Journal of Monetary Economics, 54(4), 1069--1087.

  * Mancini-Griffoli, Tommaso (2007): ``Dynare User Guide: An introduction to the solution and estimation of DSGE models''.

  * Pearlman, Joseph, David Currie, and Paul Levine (1986): ``Rational expectations models with partial information,'' Economic Modelling, 3(2), 90--105.

  * Rabanal, Pau and Juan Rubio-Ramirez (2003): ``Comparing New Keynesian Models of the Business Cycle: A Bayesian Approach,'' Federal Reserve of Atlanta, Working Paper Series, 2003-30.

  * Ratto, Marco (2008): ``Analysing dsge models with global sensitivity analysis''. Computational Economics, 31, 115--139.

  * Schorfheide, Frank (2000): ``Loss Function-based evaluation of DSGE models,'' Journal of Applied Econometrics, 15(6), 645--670.

  * Schmitt-Grohé, Stephanie and Martin Uríbe (2004): ``Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function,'' Journal of Economic Dynamics and Control, 28(4), 755--775.

  * Sims, Christopher A., Daniel F. Waggoner and Tao Zha (2008): ``Methods for inference in large multiple-equation Markov-switching models,'' Journal of Econometrics, 146, 255--274.

  * Smets, Frank and Rafael Wouters (2003): ``An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area,'' Journal of the European Economic Association, 1(5), 1123--1175.

  * Villemot, Sébastien (2011): ``Solving rational expectations models at first order: what Dynare does,'' Dynare Working Papers, 2, CEPREMAP