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doc: fix markups of i.e. and e.g.

time-shift
Houtan Bastani 2017-03-21 14:15:51 +01:00
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@ -2303,7 +2303,7 @@ necessary for lagged/leaded variables, while feasible starting values are requir
It is important to be aware that if some variables, endogenous or exogenous, are not mentioned in the
@code{initval} block, a zero value is assumed. It is particularly important to keep
this in mind when specifying exogenous variables using @code{varexo} that are not allowed
to take on the value of zero, like e.g. TFP.
to take on the value of zero, like @i{e.g.} TFP.
Note that if the @code{initval} block is immediately followed by a
@code{steady} command, its semantics are slightly changed.
@ -2549,7 +2549,7 @@ equilibrium values.
The fact that @code{c} at @math{t=0} and @code{k} at @math{t=201} specified in
@code{initval} and @code{endval} are taken as given has an important
implication for plotting the simulated vector for the endogenous
variables, i.e. the rows of @code{oo_.endo_simul}: this vector will
variables, @i{i.e.} the rows of @code{oo_.endo_simul}: this vector will
also contain the initial and terminal
conditions and thus is 202 periods long in the example. When you specify
arbitrary values for the initial and terminal conditions for forward- and
@ -4002,7 +4002,7 @@ Default: no filter.
@item bandpass_filter = @var{[HIGHEST_PERIODICITY LOWEST_PERIODICITY]}
Uses a bandpass filter before computing moments. The passband is set to a periodicity of @code{HIGHEST_PERIODICITY}
to @code{LOWEST_PERIODICITY}, e.g. 6 to 32 quarters if the model frequency is quarterly.
to @code{LOWEST_PERIODICITY}, @i{e.g.} @math{6} to @math{32} quarters if the model frequency is quarterly.
Default: @code{[6,32]}.
@item irf = @var{INTEGER}
@ -4144,7 +4144,7 @@ period(s). The periods must be strictly positive. Conditional variances are give
decomposition provides the decomposition of the effects of shocks upon
impact. The results are stored in
@code{oo_.conditional_variance_decomposition}
(@pxref{oo_.conditional_variance_decomposition}). The variance decomposition is only conducted, if theoretical moments are requested, i.e. using the @code{periods=0}-option. In case of @code{order=2}, Dynare provides a second-order accurate approximation to the true second moments based on the linear terms of the second-order solution (see @cite{Kim, Kim, Schaumburg and Sims (2008)}). Note that the unconditional variance decomposition (i.e. at horizon infinity) is automatically conducted if theoretical moments are requested and if @code{nodecomposition} is not set (@pxref{oo_.variance_decomposition})
(@pxref{oo_.conditional_variance_decomposition}). The variance decomposition is only conducted, if theoretical moments are requested, @i{i.e.} using the @code{periods=0}-option. In case of @code{order=2}, Dynare provides a second-order accurate approximation to the true second moments based on the linear terms of the second-order solution (see @cite{Kim, Kim, Schaumburg and Sims (2008)}). Note that the unconditional variance decomposition (@i{i.e.} at horizon infinity) is automatically conducted if theoretical moments are requested and if @code{nodecomposition} is not set (@pxref{oo_.variance_decomposition})
@item pruning
Discard higher order terms when iteratively computing simulations of
@ -4372,7 +4372,7 @@ accurate approximation of the true second moments, see @code{conditional_varianc
@anchor{oo_.variance_decomposition}
@defvr {MATLAB/Octave variable} oo_.variance_decomposition
After a run of @code{stoch_simul} when requesting theoretical moments (@code{periods=0}),
contains a matrix with the result of the unconditional variance decomposition (i.e. at horizon infinity).
contains a matrix with the result of the unconditional variance decomposition (@i{i.e.} at horizon infinity).
The first dimension corresponds to the endogenous variables (in the order of declaration) and
the second dimension corresponds to exogenous variables (in the order of declaration).
Numbers are in percent and sum up to 100 across columns.
@ -4760,7 +4760,7 @@ varobs
This block specifies @emph{linear} trends for observed variables as
functions of model parameters. In case the @code{loglinear}-option is used,
this corresponds to a linear trend in the logged observables, i.e. an exponential
this corresponds to a linear trend in the logged observables, @i{i.e.} an exponential
trend in the level of the observables.
Each line inside of the block should be of the form:
@ -5096,7 +5096,7 @@ convergence is then checked using the @cite{Brooks and Gelman (1998)}
univariate convergence diagnostic.
The inefficiency factors are computed as in @cite{Giordano et al. (2011)} based on
Parzen windows as in e.g. @cite{Andrews (1991)}.
Parzen windows as in @i{e.g.} @cite{Andrews (1991)}.
@optionshead
@ -5311,11 +5311,11 @@ The scale to be used for drawing the initial value of the
Metropolis-Hastings chain. Generally, the starting points should be overdispersed
for the @cite{Brooks and Gelman (1998)}-convergence diagnostics to be meaningful. Default: 2*@code{mh_jscale}.
It is important to keep in mind that @code{mh_init_scale} is set at the beginning of
Dynare execution, i.e. the default will not take into account potential changes in
Dynare execution, @i{i.e.} the default will not take into account potential changes in
@ref{mh_jscale} introduced by either @code{mode_compute=6} or the
@code{posterior_sampler_options}-option @ref{scale_file}.
If @code{mh_init_scale} is too wide during initalization of the posterior sampler so that 100 tested draws
are inadmissible (e.g. Blanchard-Kahn conditions are always violated), Dynare will request user input
are inadmissible (@i{e.g.} Blanchard-Kahn conditions are always violated), Dynare will request user input
of a new @code{mh_init_scale} value with which the next 100 draws will be drawn and tested.
If the @ref{nointeractive}-option has been invoked, the program will instead automatically decrease
@code{mh_init_scale} by 10 percent after 100 futile draws and try another 100 draws. This iterative
@ -5513,7 +5513,7 @@ generator state of the already present draws is currently not supported.
@item load_results_after_load_mh
@anchor{load_results_after_load_mh} This option is available when loading a previous MCMC run without
adding additional draws, i.e. when @code{load_mh_file} is specified with @code{mh_replic=0}. It tells Dynare
adding additional draws, @i{i.e.} when @code{load_mh_file} is specified with @code{mh_replic=0}. It tells Dynare
to load the previously computed convergence diagnostics, marginal data density, and posterior statistics from an
existing @code{_results}-file instead of recomputing them.
@ -5840,7 +5840,7 @@ Note that @code{'slice'} is incompatible with
@anchor{posterior_sampler_options}
A list of @var{NAME} and @var{VALUE} pairs. Can be used to set options for the posterior sampling methods.
The set of available options depends on the selected posterior sampling routine
(i.e. on the value of option @ref{posterior_sampling_method}):
(@i{i.e.} on the value of option @ref{posterior_sampling_method}):
@table @code
@ -5910,7 +5910,7 @@ mode to perform rotated slice iterations. Default: 0
@item 'initial_step_size'
Sets the initial size of the interval in the stepping-out procedure as fraction of the prior support
i.e. the size will be initial_step_size*(UB-LB). @code{initial_step_size} must be a real number in the interval [0, 1].
@i{i.e.} the size will be initial_step_size*(UB-LB). @code{initial_step_size} must be a real number in the interval [0, 1].
Default: 0.8
@item 'use_mh_covariance_matrix'
@ -5982,13 +5982,13 @@ option @code{moments_varendo} to be specified.
@item filtered_vars
@anchor{filtered_vars} Triggers the computation of the posterior
distribution of filtered endogenous variables/one-step ahead forecasts, i.e. @math{E_{t}{y_{t+1}}}. Results are
distribution of filtered endogenous variables/one-step ahead forecasts, @i{i.e.} @math{E_{t}{y_{t+1}}}. Results are
stored in @code{oo_.FilteredVariables} (see below for a description of
this variable)
@item smoother
@anchor{smoother} Triggers the computation of the posterior distribution
of smoothed endogenous variables and shocks, i.e. the expected value of variables and shocks given the information available in all observations up to the @emph{final} date (@math{E_{T}{y_t}}). Results are stored in
of smoothed endogenous variables and shocks, @i{i.e.} the expected value of variables and shocks given the information available in all observations up to the @emph{final} date (@math{E_{T}{y_t}}). Results are stored in
@code{oo_.SmoothedVariables}, @code{oo_.SmoothedShocks} and
@code{oo_.SmoothedMeasurementErrors}. Also triggers the computation of
@code{oo_.UpdatedVariables}, which contains the estimation of the expected value of variables given the information available at the @emph{current} date (@math{E_{t}{y_t}}). See below for a description of all these
@ -6030,9 +6030,9 @@ Use the Univariate Diffuse Kalman Filter
@end table
@noindent
Default value is @code{0}. In case of missing observations of single or all series, Dynare treats those missing values as unobserved states and uses the Kalman filter to infer their value (see e.g. @cite{Durbin and Koopman (2012), Ch. 4.10})
Default value is @code{0}. In case of missing observations of single or all series, Dynare treats those missing values as unobserved states and uses the Kalman filter to infer their value (see @i{e.g.} @cite{Durbin and Koopman (2012), Ch. 4.10})
This procedure has the advantage of being capable of dealing with observations where the forecast error variance matrix becomes singular for some variable(s).
If this happens, the respective observation enters with a weight of zero in the log-likelihood, i.e. this observation for the respective variable(s) is dropped
If this happens, the respective observation enters with a weight of zero in the log-likelihood, @i{i.e.} this observation for the respective variable(s) is dropped
from the likelihood computations (for details see @cite{Durbin and Koopman (2012), Ch. 6.4 and 7.2.5} and @cite{Koopman and Durbin (2000)}). If the use of a multivariate Kalman filter is specified and a
singularity is encountered, Dynare by default automatically switches to the univariate Kalman filter for this parameter draw. This behavior can be changed via the
@ref{use_univariate_filters_if_singularity_is_detected} option.
@ -6061,7 +6061,7 @@ See below.
@item filter_step_ahead = [@var{INTEGER1} @var{INTEGER2} @dots{}]
@anchor{filter_step_ahead}
Triggers the computation k-step ahead filtered values, i.e. @math{E_{t}{y_{t+k}}}. Stores results in
Triggers the computation k-step ahead filtered values, @i{i.e.} @math{E_{t}{y_{t+k}}}. Stores results in
@code{oo_.FilteredVariablesKStepAhead}. Also stores 1-step ahead values in @code{oo_.FilteredVariables}.
@code{oo_.FilteredVariablesKStepAheadVariances} is stored if @code{filter_covariance}.
@ -6071,7 +6071,7 @@ Triggers the computation k-step ahead filtered values, i.e. @math{E_{t}{y_{t+k}}
decomposition of the above k-step ahead filtered values. Stores results in @code{oo_.FilteredVariablesShockDecomposition}.
@item smoothed_state_uncertainty
@anchor{smoothed_state_uncertainty} Triggers the computation of the variance of smoothed estimates, i.e.
@anchor{smoothed_state_uncertainty} Triggers the computation of the variance of smoothed estimates, @i{i.e.}
@code{Var_T(y_t)}. Stores results in @code{oo_.Smoother.State_uncertainty}.
@item diffuse_filter
@ -6221,7 +6221,7 @@ such a singularity is encountered. Default: @code{1}.
With the default @ref{use_univariate_filters_if_singularity_is_detected}=1, Dynare will switch
to the univariate Kalman filter when it encounters a singular forecast error variance
matrix during Kalman filtering. Upon encountering such a singularity for the first time, all subsequent
parameter draws and computations will automatically rely on univariate filter, i.e. Dynare will never try
parameter draws and computations will automatically rely on univariate filter, @i{i.e.} Dynare will never try
the multivariate filter again. Use the @code{keep_kalman_algo_if_singularity_is_detected} option to have the
@code{use_univariate_filters_if_singularity_is_detected} only affect the behavior for the current draw/computation.
@ -6351,7 +6351,7 @@ It is also possible to impose implicit ``endogenous'' priors about IRFs and mome
estimation. For example, one can specify that all valid parameter draws for the model must generate fiscal multipliers that are
bigger than 1 by specifying how the IRF to a government spending shock must look like. The prior restrictions can be imposed
via @code{irf_calibration} and @code{moment_calibration} blocks (@pxref{IRF/Moment calibration}). The way it works internally is that
any parameter draw that is inconsistent with the ``calibration'' provided in these blocks is discarded, i.e. assigned a prior density of 0.
any parameter draw that is inconsistent with the ``calibration'' provided in these blocks is discarded, @i{i.e.} assigned a prior density of 0.
When specifying these blocks, it is important to keep in mind that one won't be able to easily do @code{model_comparison} in this case,
because the prior density will not integrate to 1.
@ -6395,7 +6395,7 @@ Upper bound of a 90% HPD interval
@item HPDinf_ME
Lower bound of a 90% HPD interval@footnote{See option @ref{conf_sig}
to change the size of the HPD interval} for observables when taking
measurement error into account (see e.g. @cite{Christoffel et al. (2010), p.17}).
measurement error into account (see @i{e.g.} @cite{Christoffel et al. (2010), p.17}).
@item HPDsup_ME
Upper bound of a 90% HPD interval for observables when taking
@ -6528,7 +6528,7 @@ indicate the respective variables. The third dimension of the array provides the
observation for which the forecast has been made. For example, if @code{filter_step_ahead=[1 2 4]}
and @code{nobs=200}, the element (3,5,204) stores the four period ahead filtered
value of variable 5 computed at time t=200 for time t=204. The periods at the beginning
and end of the sample for which no forecasts can be made, e.g. entries (1,5,1) and
and end of the sample for which no forecasts can be made, @i{e.g.} entries (1,5,1) and
(1,5,204) in the example, are set to zero. Note that in case of Bayesian estimation
the variables will be ordered in the order of declaration after the estimation
command (or in general declaration order if no variables are specified here). In case
@ -6564,7 +6564,7 @@ The fourth dimension of the array provides the
observation for which the forecast has been made. For example, if @code{filter_step_ahead=[1 2 4]}
and @code{nobs=200}, the element (3,5,2,204) stores the contribution of the second shock to the
four period ahead filtered value of variable 5 (in deviations from the mean) computed at time t=200 for time t=204. The periods at the beginning
and end of the sample for which no forecasts can be made, e.g. entries (1,5,1) and
and end of the sample for which no forecasts can be made, @i{e.g.} entries (1,5,1) and
(1,5,204) in the example, are set to zero. Padding with zeros and variable ordering is analogous to
@code{oo_.FilteredVariablesKStepAhead}.
@end defvr
@ -6714,7 +6714,7 @@ Fields are of the form:
Variable set by the @code{estimation} command (if used with the
@code{smoother} option), or by the @code{calib_smoother} command.
Contains the constant part of the endogenous variables used in the
smoother, accounting e.g. for the data mean when using the @code{prefilter}
smoother, accounting @i{e.g.} for the data mean when using the @code{prefilter}
option.
Fields are of the form:
@ -6750,7 +6750,7 @@ Auto- and cross-correlation of endogenous variables. Fields are vectors with cor
@item VarianceDecomposition
Decomposition of variance (unconditional variance, i.e. at horizon infinity)@footnote{When the shocks are correlated, it
Decomposition of variance (unconditional variance, @i{i.e.} at horizon infinity)@footnote{When the shocks are correlated, it
is the decomposition of orthogonalized shocks via Cholesky
decomposition according to the order of declaration of shocks
(@pxref{Variable declarations})}
@ -6916,7 +6916,7 @@ Upper/lower bound of the 90% HPD interval taking into account both parameter and
@end table
@var{VARIABLE_NAME} contains a matrix of the following size: number of time periods for which forecasts are requested using the nobs = [@var{INTEGER1}:@var{INTEGER2}] option times the number of forecast horizons requested by the @code{forecast} option. I.e., the row indicates the period at which the forecast is performed and the column the respective k-step ahead forecast. The starting periods are sorted in ascending order, not in declaration order.
@var{VARIABLE_NAME} contains a matrix of the following size: number of time periods for which forecasts are requested using the nobs = [@var{INTEGER1}:@var{INTEGER2}] option times the number of forecast horizons requested by the @code{forecast} option. @i{i.e.}, the row indicates the period at which the forecast is performed and the column the respective k-step ahead forecast. The starting periods are sorted in ascending order, not in declaration order.
@end defvr
@ -6976,7 +6976,7 @@ estimates using a higher tapering are usually more reliable.
@descriptionhead
This command computes odds ratios and estimate a posterior density over a
collection of models (see e.g. @cite{Koop (2003), Ch. 1}). The priors over
collection of models (see @i{e.g.} @cite{Koop (2003), Ch. 1}). The priors over
models can be specified as the @var{DOUBLE} values, otherwise a uniform prior
over all models is assumed. In contrast to frequentist econometrics, the
models to be compared do not need to be nested. However, as the computation of
@ -7053,7 +7053,7 @@ Posterior probability of the respective model
@descriptionhead
This command computes the historical shock decomposition for a given sample based on
the Kalman smoother, i.e. it decomposes the historical deviations of the endogenous
the Kalman smoother, @i{i.e.} it decomposes the historical deviations of the endogenous
variables from their respective steady state values into the contribution coming
from the various shocks. The @code{variable_names} provided govern for which
variables the decomposition is plotted.
@ -7118,9 +7118,9 @@ The second dimension stores
in the first @code{M_.exo_nbr} columns the contribution of the respective shocks.
Column @code{M_.exo_nbr+1} stores the contribution of the initial conditions,
while column @code{M_.exo_nbr+2} stores the smoothed value of the respective
endogenous variable in deviations from their steady state, i.e. the mean and trends are
endogenous variable in deviations from their steady state, @i{i.e.} the mean and trends are
subtracted. The third dimension stores the time periods. Both the variables
and shocks are stored in the order of declaration, i.e. @code{M_.endo_names} and
and shocks are stored in the order of declaration, @i{i.e.} @code{M_.endo_names} and
@code{M_.exo_names}, respectively.
@end deffn
@ -8016,7 +8016,7 @@ where:
@item
@math{\gamma} are parameters to be optimized. They must be elements
of the matrices @math{A_1}, @math{A_2}, @math{A_3}, i.e. be specified as
of the matrices @math{A_1}, @math{A_2}, @math{A_3}, @i{i.e.} be specified as
parameters in the @code{params}-command and be entered in the
@code{model}-block;
@ -8038,7 +8038,7 @@ parameters to minimize the weighted (co)-variance of a specified subset
of endogenous variables, subject to a linear law of motion implied by the
first order conditions of the model. A few things are worth mentioning.
First, @math{y} denotes the selected endogenous variables' deviations
from their steady state, i.e. in case they are not already mean 0 the
from their steady state, @i{i.e.} in case they are not already mean 0 the
variables entering the loss function are automatically demeaned so that
the centered second moments are minimized. Second, @code{osr} only solves
linear quadratic problems of the type resulting from combining the
@ -8075,7 +8075,7 @@ by listing them after the command, as @code{stoch_simul}
Specifies the optimizer for minimizing the objective function. The same solvers as for @code{mode_compute} (@pxref{mode_compute}) are available, except for 5,6, and 10.
@item optim = (@var{NAME}, @var{VALUE}, ...)
A list of @var{NAME} and @var{VALUE} pairs. Can be used to set options for the optimization routines. The set of available options depends on the selected optimization routine (i.e. on the value of option @ref{opt_algo}). @xref{optim}.
A list of @var{NAME} and @var{VALUE} pairs. Can be used to set options for the optimization routines. The set of available options depends on the selected optimization routine (@i{i.e.} on the value of option @ref{opt_algo}). @xref{optim}.
@item maxit = @var{INTEGER}
Determines the maximum number of iterations used in @code{opt_algo=4}. This option is now deprecated and will be
@ -8187,7 +8187,7 @@ Each line has the following syntax:
PARAMETER_NAME, LOWER_BOUND, UPPER_BOUND;
@end example
Note that the use of this block requires the use of a constrained optimizer, i.e. setting @ref{opt_algo} to
Note that the use of this block requires the use of a constrained optimizer, @i{i.e.} setting @ref{opt_algo} to
1,2,5, or 9.
@examplehead
@ -8332,7 +8332,7 @@ maximizes the policy maker's objective function subject to the
constraints provided by the equilibrium path of the private economy and under
commitment to this optimal policy. The Ramsey policy is computed
by approximating the equilibrium system around the perturbation point where the
Lagrange multipliers are at their steady state, i.e. where the Ramsey planner acts
Lagrange multipliers are at their steady state, @i{i.e.} where the Ramsey planner acts
as if the initial multipliers had
been set to 0 in the distant past, giving them time to converge to their steady
state value. Consequently, the optimal decision rules are computed around this steady state
@ -8395,7 +8395,7 @@ multipliers associated with the planner's problem are set to their steady state
values (@pxref{ramsey_policy}).
In contrast, the second entry stores the value of the planner objective with
initial Lagrange multipliers of the planner's problem set to 0, i.e. it is assumed
initial Lagrange multipliers of the planner's problem set to 0, @i{i.e.} it is assumed
that the planner exploits its ability to surprise private agents in the first
period of implementing Ramsey policy. This is the value of implementating
optimal policy for the first time and committing not to re-optimize in the future.
@ -8738,7 +8738,7 @@ Maximum number of lags for moments in identification analysis. Default: @code{1}
The @code{irf_calibration} and @code{moment_calibration} blocks allow imposing implicit ``endogenous'' priors
about IRFs and moments on the model. The way it works internally is that
any parameter draw that is inconsistent with the ``calibration'' provided in these blocks is discarded, i.e. assigned a prior density of 0.
any parameter draw that is inconsistent with the ``calibration'' provided in these blocks is discarded, @i{i.e.} assigned a prior density of @math{0}.
In the context of @code{dynare_sensitivity}, these restrictions allow tracing out which parameters are driving the model to
satisfy or violate the given restrictions.
@ -11197,7 +11197,7 @@ This section outlines the steps necessary on most Windows systems to set up Dyna
@enumerate
@item Write a configuration file containing the options you want. A mimimum working
example setting up a cluster consisting of two local CPU cores that allows for e.g. running
example setting up a cluster consisting of two local CPU cores that allows for @i{e.g.} running
two Monte Carlo Markov Chains in parallel is shown below.
@item Save the configuration file somwhere. The name and file ending do not matter
if you are providing it with the @code{conffile} command line option. The only restrictions are that the
@ -11205,8 +11205,8 @@ This section outlines the steps necessary on most Windows systems to set up Dyna
For the configuration file to be accessible without providing an explicit path at the command line, you must save it
under the name @file{dynare.ini} into your user account's @code{Application Data} folder.
@item Install the @file{PSTools} from @uref{https://technet.microsoft.com/sysinternals/pstools.aspx}
to your system, e.g. into @file{C:\PSTools}.
@item Set the Windows System Path to the @file{PSTools}-folder (e.g. using something along the line of pressing Windows Key+Pause to
to your system, @i{e.g.} into @file{C:\PSTools}.
@item Set the Windows System Path to the @file{PSTools}-folder (@i{e.g.} using something along the line of pressing Windows Key+Pause to
open the System Configuration, then go to Advanced -> Environment Variables -> Path, see also @uref{https://technet.microsoft.com/sysinternals/pstools.aspx}).
@item Restart your computer to make the path change effective.
@item Open Matlab and type into the command window