From 69daebf441fcfb73590db7a8a1d31ad0aa414e67 Mon Sep 17 00:00:00 2001 From: sebastien Date: Tue, 13 Oct 2009 13:08:05 +0000 Subject: [PATCH] Reference manual: fixed problems of conformance to DocBook 4.5 spec git-svn-id: https://www.dynare.org/svn/dynare/trunk@3038 ac1d8469-bf42-47a9-8791-bf33cf982152 --- doc/manual.xml | 258 ++++++++++++++++++++++++++++++------------------- 1 file changed, 160 insertions(+), 98 deletions(-) diff --git a/doc/manual.xml b/doc/manual.xml index 568b4392a..91e6586c4 100644 --- a/doc/manual.xml +++ b/doc/manual.xml @@ -85,8 +85,8 @@ A copy of the license can be found at: Dynare is a pre-processor and a collection of Matlab and GNU Octave routines which solve, simulate and estimate non-linear models with forward looking variables. It is the result of research carried at -CEPREMAP by several people (see , -, , and ). +CEPREMAP by several people (see , +, , and ). When the framework is deterministic, Dynare can be used for models with the assumption of perfect @@ -103,17 +103,17 @@ transition path to a new equilibrium following a permanent shock. For deterministic simulations, Dynare uses a Newton-type algorithm, first -proposed by , instead of a first order technique like -the one proposed by , and used in earlier generation simulation programs. We believe +proposed by , instead of a first order technique like +the one proposed by , 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 used in Dynare can be found in . +details of the algorithm used in Dynare can be found in . -In a stochastic context, Dynare computes one or several simulations corresponding to a random draw of the shocks. Starting with version 2.3, Dynare uses a second order Taylor approximation of the expectation functions (see , -, , and ). +In a stochastic context, Dynare computes one or several simulations corresponding to a random draw of the shocks. Starting with version 2.3, Dynare uses a second order Taylor approximation of the expectation functions (see , +, , and ). -Starting with version 3.0, it is possible to use Dynare to estimate model parameters either by maximum likelihood as in or using a Bayesian approach as in , or . +Starting with version 3.0, it is possible to use Dynare to estimate model parameters either by maximum likelihood as in or using a Bayesian approach as in , or . @@ -415,7 +415,7 @@ In the description of Dynare commands, the following conventions are observed: , VARIABLE_NAME - ; + ; @@ -454,7 +454,7 @@ var c gnp q1 q2; , VARIABLE_NAME - ; + ; @@ -496,7 +496,7 @@ varexo m gov; , VARIABLE_NAME - ; + ; @@ -538,7 +538,7 @@ varexo_det tau; , PARAMETER_NAME - ; + ; @@ -581,7 +581,7 @@ parameters alpha, bet; 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 for more details and concrete examples. + 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 for more details and concrete examples. Leads and lags are primarily used for endogenous variables. They can be used for exogenous variables under some conditions (TO BE EXPLICITED). They are forbidden for parameters and for local model variables (see ). @@ -627,13 +627,16 @@ parameters alpha, bet; When using Dynare for computing simulations, it is necessary to calibrate the parameters of the model. This is done through parameter initialization. Syntax - + + PARAMETER_NAME = EXPRESSION ; - + + Example + parameters alpha, bet; @@ -643,6 +646,7 @@ alpha = 0.36; A = 1-alpha*beta; + @@ -665,7 +669,7 @@ A = 1-alpha*beta; model (OPTION, OPTION) - ; + ; @@ -674,7 +678,7 @@ A = 1-alpha*beta; # VARIABLE_NAME = MODEL_EXPRESSION ; - end; + end; @@ -696,7 +700,7 @@ Inside the model block, Dynare allows the creation of model-local vari Options - + Declares the model as being linear. It spares oneself from having to declare initial values for computing the steady state, and it sets automatically =1 in . @@ -727,7 +731,7 @@ end; Example 2: use of model local variables -The following program: +The following program: model; # gamma = 1 - 1/sigma; @@ -743,6 +747,7 @@ u1 = c1^(1-1/sigma)/(1-1/sigma); u2 = c2^(1-1/sigma)/(1-1/sigma); end; + @@ -776,7 +781,7 @@ end; 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 and (see . +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 and (see . Another one is to study, how an economy, starting from arbitrary initial conditions converges toward equilibrium. To do that, one needs and ; @@ -797,11 +802,11 @@ For models with lags on more than one period, the command for x=1 - histval; + histval; VARIABLE_NAME = EXPRESSION ; - end; + end; @@ -997,7 +1006,7 @@ end; -Shocks on exogenous variables +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 and . @@ -1028,7 +1037,7 @@ If the variance of an exogenous variable is set to zero, this variable will appe - shocks; + shocks; @@ -1039,13 +1048,12 @@ If the variance of an exogenous variable is set to zero, this variable will appe - end; + end; - var VARIABLE_NAME; - periods INTEGER:INTEGER - , INTEGER:INTEGER; - values EXPRESSION , EXPRESSION; + var VARIABLE_NAME; + periods INTEGER:INTEGER, INTEGER:INTEGER; + values EXPRESSION , EXPRESSION; @@ -1154,7 +1162,7 @@ forecast; - mshocks; + mshocks; @@ -1165,13 +1173,13 @@ forecast; - end; + end; - var VARIABLE_NAME; - periods INTEGER:INTEGER - , INTEGER:INTEGER; - values EXPRESSION , EXPRESSION; + var VARIABLE_NAME; + periods INTEGER:INTEGER + , INTEGER:INTEGER; + values EXPRESSION , EXPRESSION; @@ -1185,6 +1193,9 @@ forecast; + Description + ... + @@ -1200,8 +1211,8 @@ forecast; Sigma_e - = [ EXPRESSION , EXPRESSION - ; EXPRESSION , EXPRESSION ]; + = [ EXPRESSION , EXPRESSION + ; EXPRESSION , EXPRESSION ]; The matrix elements are actually written between square brackets ([]). Here, the initial [ and final ] don't have the meaning of optional element as elsewhere. @@ -1251,10 +1262,14 @@ where the variance of u is 0.81, the variance of e dsample - INTEGER - INTEGER; + INTEGER + INTEGER; + + Description + ... + @@ -1321,12 +1336,12 @@ Dynare has special commands for the computation of the static equilibrium of the steady - (OPTION, OPTION); + (OPTION, OPTION); Options - + = INTEGER Determines the non-linear solver to use. Possible values for the option are: @@ -1393,11 +1408,15 @@ See and . - homotopy_setup; + homotopy_setup; VARIABLE_NAME, EXPRESSION, EXPRESSION; - end; + end; + + Description + ... + @@ -1413,12 +1432,12 @@ See and . check - ( = INTEGER); + ( = INTEGER); Options - + = INTEGER See there for the possible values and their meaning @@ -1457,12 +1476,12 @@ A necessary condition for the uniqueness of a stable equilibrium in the neighbor forecast (OPTION, OPTION) VARIABLE_NAME - , VARIABLE_NAME; + , VARIABLE_NAME; Options - + = INTEGER Number of periods of the forecast. Default: 40 @@ -1532,13 +1551,13 @@ forecast; simul (=INTEGER) - ; + ; Description - Triggers the computation of a deterministic simulation of the model for the number of periods set in the option . simul uses a Newton method to solve simultaneously all the equations for every period (see ). + Triggers the computation of a deterministic simulation of the model for the number of periods set in the option . simul uses a Newton method to solve simultaneously all the equations for every period (see ). Output variables @@ -1563,12 +1582,12 @@ The simulated endogenous variables are available in global matrix oo_.e stoch_simul (OPTION, OPTION) - VARIABLE_NAME; + VARIABLE_NAME; Options - + = INTEGER Order of autocorrelation coefficients to compute and to print. Default: 5 @@ -1821,7 +1840,7 @@ Note that in order to avoid stochastic singularity, you must have at least as ma VARIABLE_NAME - ; + ; @@ -1853,11 +1872,11 @@ varobs C y rr; - observation_trends; + observation_trends; VARIABLE_NAME(EXPRESSION); - end; + end; @@ -1893,7 +1912,7 @@ end; Syntax I (Maximum likelihood estimation) - estimated_params; + estimated_params; @@ -1916,13 +1935,13 @@ end; ; - end; + end; Syntax II (Bayesian estimation) - estimated_params; + estimated_params; @@ -1944,7 +1963,7 @@ end; - , PRIOR_SHAPE + , PRIOR_SHAPE , PRIOR_MEAN @@ -1963,7 +1982,7 @@ end; ; - end; + end; @@ -2095,7 +2114,7 @@ end; - estimated_params_init; + estimated_params_init; @@ -2114,7 +2133,7 @@ end; ; - end; + end; @@ -2141,7 +2160,7 @@ end; - estimated_params_bounds; + estimated_params_bounds; @@ -2164,7 +2183,7 @@ end; ; - end; + end; @@ -2192,7 +2211,7 @@ end; estimation (OPTION, OPTION) - VARIABLE_NAME; + VARIABLE_NAME; @@ -2534,7 +2553,7 @@ oo_.posterior_hpdsup.measurement_errors_corr.gdp_conso prior_analysis - (OPTION, OPTION); + (OPTION, OPTION); @@ -2610,7 +2629,7 @@ oo_.posterior_hpdsup.measurement_errors_corr.gdp_conso posterior_analysis - (OPTION, OPTION); + (OPTION, OPTION); @@ -2638,13 +2657,13 @@ oo_.posterior_hpdsup.measurement_errors_corr.gdp_conso , VARIABLE_NAME - ; + ; Description -unit_root_vars is now deprecated and will result in no action, It was used to declare unit-root variables of a model so that a diffuse prior can be used in the initialization of the Kalman filter for these variables only. For stationary variables, the unconditional covariance matrix of these variables is used for initialization. The algorithm to compute a true diffuse prior is taken from and . +unit_root_vars is now deprecated and will result in no action, It was used to declare unit-root variables of a model so that a diffuse prior can be used in the initialization of the Kalman filter for these variables only. For stationary variables, the unconditional covariance matrix of these variables is used for initialization. The algorithm to compute a true diffuse prior is taken from and . When unit_root_vars is used the lik_init option of has no effect. @@ -2682,14 +2701,14 @@ oo_.posterior_hpdsup.measurement_errors_corr.gdp_conso - optim_weights; + optim_weights; VARIABLE_NAME EXPRESSION; VARIABLE_NAME, VARIABLE_NAME EXPRESSION; - end; + end; @@ -2715,7 +2734,7 @@ oo_.posterior_hpdsup.measurement_errors_corr.gdp_conso osr (OPTION, OPTION) - VARIABLE_NAME; + VARIABLE_NAME; @@ -2766,7 +2785,7 @@ This problem is solved using a numerical optimizer. osr_params PARAMETER_NAME PARAMETER_NAME - ; + ; @@ -2792,10 +2811,14 @@ This problem is solved using a numerical optimizer. planner_objective MODEL_EXPRESSION - ; + ; + Description + ... + + @@ -2812,7 +2835,7 @@ This problem is solved using a numerical optimizer. ramsey_policy (OPTION, OPTION) - VARIABLE_NAME; + VARIABLE_NAME; @@ -2847,7 +2870,7 @@ This problem is solved using a numerical optimizer. dynare_sensitivity (OPTION, OPTION) - ; + ; @@ -2888,7 +2911,7 @@ Dynare has comments to plot the results of a simulation and to save the results. rplot VARIABLE_NAME VARIABLE_NAME - ; + ; Description @@ -2913,7 +2936,7 @@ Plots the simulated path of one or several variables. dynatype (FILENAME) VARIABLE_NAME - ; + ; @@ -2940,7 +2963,7 @@ Plots the simulated path of one or several variables. dynasave (FILENAME) VARIABLE_NAME - ; + ; @@ -2978,6 +3001,11 @@ In Matlab, variables saved with the @#include includes another file + + Description + ... + + @@ -2989,6 +3017,11 @@ In Matlab, variables saved with the @#define defines a macro-variable + + Description + ... + + @@ -3000,6 +3033,11 @@ In Matlab, variables saved with the @#if ... @#else ... @#endif conditional inclusion of some part of the .mod file + + Description + ... + + @@ -3011,6 +3049,10 @@ In Matlab, variables saved with the @#for ... @#endfor loop for replications of portions of the .mod file + + Description + ... + @@ -3022,6 +3064,11 @@ In Matlab, variables saved with the @#echo asks the preprocessor to display some message on standard output + + Description + ... + + @@ -3033,6 +3080,11 @@ In Matlab, variables saved with the @#error asks the preprocessor to display some error message on standard output and to abort + + Description + ... + + @@ -3135,6 +3187,11 @@ In Matlab, variables saved with the bvar_density computes the marginal density of an estimated BVAR model, using Minnesota priors + + Description + ... + + @@ -3146,6 +3203,11 @@ In Matlab, variables saved with the bvar_forecast computes in-sample or out-sample forecasts for an estimated BVAR model, using Minnesota priors + + Description + ... + + @@ -3160,7 +3222,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + Boucekkine @@ -3176,7 +3238,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + @@ -3198,7 +3260,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + @@ -3220,7 +3282,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + @@ -3238,7 +3300,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + @@ -3282,7 +3344,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + Ireland @@ -3298,7 +3360,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + Judd @@ -3330,7 +3392,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + Juillard @@ -3346,7 +3408,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + @@ -3369,7 +3431,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + Laffargue @@ -3408,7 +3470,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + Mancini-Griffoli Tommaso @@ -3418,7 +3480,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast An introduction to the solution and estimation of DSGE models - + @@ -3440,7 +3502,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + Schorfheide @@ -3456,7 +3518,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - + @@ -3477,7 +3539,7 @@ Fabrice Collard (GREMAQ, University of Toulouse) has written a guide to stochast - +