9. Dynare misc commands

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 by 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 by 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 in the specified Markov Chain CHAIN_NUMBER.

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 matalab/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

--info

Prints on screen the internal documentation of ROUTINENAME (if this routine exists and if this routine has a texinfo internal documentation header). The path to ROUTINENAME has to be provided, if the routine is not in the current directory. Example

>> internals --doc ../matlab/fr/ROUTINENAME

At this time, will work properly for only 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.

--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 line: prior [options[, ...]];

Prints various informations about the prior distribution depending on the options. If no options are provided, the command returns the list of available options. Following options are available:

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).
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 and , the marginal densities, and , 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.

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