9. Dynare misc commands¶

MATLAB/Octave command: send_endogenous_variables_to_workspace ;¶: Puts the simulation results for the endogenous variables stored in oo_.endo_simul into vectors with the same name as the respective variables into the base workspace.

MATLAB/Octave command: send_exogenous_variables_to_workspace ;¶: Puts the simulation results for the exogenous variables stored in oo_.exo_simul into vectors with the same name as the respective variables into the base workspace.

MATLAB/Octave command: send_irfs_to_workspace ;¶: Puts the IRFs stored in oo_.irfs into vectors with the same name into the base workspace.

Command: prior_function(OPTIONS);¶

Executes a user-defined function on parameter draws from the prior distribution. Dynare returns the results of the computations for all draws in an ndraws by \(n\) cell array named oo_.prior_function_results.

Options

function = FUNCTION_NAME¶: The function must have the following header output_cell = FILENAME(xparam1,M_,options_,oo_,estim_params_,bayestopt_,dataset_,dataset_info), providing read-only access to all Dynare structures. The only output argument allowed is a \(1 \times n\) cell array, which allows for storing any type of output/computations. This option is required.

sampling_draws = INTEGER¶: Number of draws used for sampling. Default: 500.

Command: posterior_function(OPTIONS);¶

Same as the prior_function command but for the posterior distribution. Results returned in oo_.posterior_function_results.

Options

function = FUNCTION_NAME: See prior_function_function.

sampling_draws = INTEGER: See prior_function_sampling_draws.

Command: generate_trace_plots(CHAIN_NUMBER);¶

Generates trace plots of the MCMC draws for all estimated parameters and the posterior density for the specified Markov Chain(s) CHAIN_NUMBER. If CHAIN_NUMBER is a vector of integers, the trace plots will plot contains separate lines for each chain.

MATLAB/Octave command: internals FLAG ROUTINENAME[.m]|MODFILENAME¶

Depending on the value of FLAG, the internals command can be used to run unitary tests specific to a MATLAB/Octave routine (if available), to display documentation about a MATLAB/Octave routine, or to extract some informations about the state of Dynare.

Flags

--test

Performs the unitary test associated to ROUTINENAME (if this routine exists and if the matlab/octave .m file has unitary test sections).

Example
>> internals --test ROUTINENAME
if routine.m is not in the current directory, the full path has to be given:
>> internals --test ../matlab/fr/ROUTINENAME

--display-mh-history

Displays information about the previously saved MCMC draws generated by a .mod file named MODFILENAME. This file must be in the current directory.

Example
>> internals --display-mh-history MODFILENAME

--load-mh-history

Loads into the MATLAB/Octave’s workspace informations about the previously saved MCMC draws generated by a .mod file named MODFILENAME.

Example
>> internals --load-mh-history MODFILENAME
This will create a structure called mcmc_informations (in the workspace) with the following fields:

Nblck

The number of MCMC chains.

InitialParameters

A Nblck*n, where n is the number of estimated parameters, array of doubles. Initial state of the MCMC.

LastParameters

A Nblck*n, where n is the number of estimated parameters, array of doubles. Current state of the MCMC.

InitialLogPost

A Nblck*1 array of doubles. Initial value of the posterior kernel.

LastLogPost

A Nblck*1 array of doubles. Current value of the posterior kernel.

InitialSeeds

A 1*Nblck structure array. Initial state of the random number generator.

LastSeeds

A 1*Nblck structure array. Current state of the random number generator.

AcceptanceRatio

A 1*Nblck array of doubles. Current acceptance ratios.

MATLAB/Octave command: prior [OPTIONS[ ...]];¶

Prints information about the prior distribution given the provided options. If no options are provided, the command returns the list of available options.

Options

table¶: Prints a table describing the marginal prior distributions (mean, mode, std., lower and upper bounds, HPD interval).

moments¶: Computes and displays first and second order moments of the endogenous variables at the prior mode (considering the linearized version of the model).

moments(distribution)¶: Computes and displays the prior mean and prior standard deviation of the first and second moments of the endogenous variables (considering the linearized version of the model) by randomly sampling from the prior. The results will also be stored in the prior subfolder in a _endogenous_variables_prior_draws.mat file.

optimize¶: Optimizes the prior density (starting from a random initial guess). The parameters such that the steady state does not exist or does not satisfy the Blanchard and Kahn conditions are penalized, as they would be when maximizing the posterior density. If a significant proportion of the prior mass is defined over such regions, the optimization algorithm may fail to converge to the true solution (the prior mode).

simulate¶: Computes the effective prior mass using a Monte-Carlo. Ideally the effective prior mass should be equal to 1, otherwise problems may arise when maximising the posterior density and model comparison based on marginal densities may be unfair. When comparing models, say \(A\) and \(B\), the marginal densities, \(m_A\) and \(m_B\), should be corrected for the estimated effective prior mass \(p_A\neq p_B \leq 1\) so that the prior mass of the compared models are identical.

plot¶: Plots the marginal prior density.

MATLAB/Octave command: search VARIABLENAME[ OPTION]¶

Searches all occurrences of a variable in a model, and prints the equations where the variable appear in the command line window. If OPTION is set to withparamvalues, the values of the parameters (if available) are displayed instead of the name of the parameters. Requires the json command line option to be set.

Example

Assuming that we already ran a .mod file and that the workspace has not been cleaned after, we can search for all the equations containing variable X
>> search X

Y = alpha*X/(1-X)+e;

diff(X) = beta*(X(-1)-mX) + gamma1*Z + gamma2*R + u;
To replace the parameters with estimated or calibrated parameters:
>> search X withparamvalues

Y = 1.254634*X/(1-X)+e;

diff(X) = -0.031459*(X(-1)-mX) + 0.1*Z - 0.2*R + u;

MATLAB/Octave command: dplot [OPTION VALUE[ ...]]¶

Plot expressions extracting data from different dseries objects.

Options

--expression EXPRESSION¶: EXPRESSION is a mathematical expression involving variables available in the dseries objects, dseries methods, numbers or parameters. All the referenced objects are supposed to be available in the calling workspace.

--dseries NAME¶: NAME is the name of a dseries object from which the variables involved in EXPRESSION will be extracted.

--range DATE1:DATE2¶: This option is not mandatory and allows to plot the expressions only over a sub-range. DATE1 and DATE2 must be dates as defined in Dates in a mod file.

--style MATLAB_SCRIPT_NAME¶: Name of a Matlab script (without extension) containing Matlab commands to customize the produced figure.

--title MATLAB_STRING¶: Adds a title to the figure.

--with-legend¶: Prints a legend below the produced plot.

Remarks

More than one –expression argument is allowed, and they must come first.

For each dseries object we plot all the expressions. We use two nested loops, the outer loop is over the dseries objects and the inner loop over the expressions. This determines the ordering of the plotted lines.

All dseries objects must be defined in the calling workspace, if a dseries object is missing the routine throws a warning (we only build the plots for the available dseries objects), if all dseries objects are missing the routine throws an error.

If the range is not provided, the expressions cannot involve leads or lags.

Example

>> toto = dseries(randn(100,3), dates('2000Q1'), {'x','y','z'});
>> noddy = dseries(randn(100,3), dates('2000Q1'), {'x','y','z'});
>> b = 3;
>> dplot --expression 2/b*cumsum(x/y(-1)-1) --dseries toto --dseries noddy --range 2001Q1:2024Q1 --title 'This is my plot'

will produce plots for 2/b*cumsum(x/y(-1)-1), where x and y are variables in dseries objects toto and noddy, in the same figure.