9. Dynare misc commands¶

Command:
prior_function
(OPTIONS);
¶
Executes a userdefined 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 namedoo_.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 readonly 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 theprior_function
command but for the posterior distribution. Results returned inoo_.posterior_function_results
.Options

function = FUNCTION_NAME

sampling_draws = INTEGER


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 ChainCHAIN_NUMBER
.

MATLAB/Octave command:
internals
FLAG ROUTINENAME[.m]MODFILENAME
¶
Depending on the value ofFLAG
, theinternals
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
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.
displaymhhistory
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 displaymhhistory MODFILENAME
loadmhhistory
Loads into the Matlab/Octave’s workspace informations about the previously saved MCMC draws generated by a.mod
file named MODFILENAME.Example
>> internals loadmhhistory MODFILENAME
This will create a structure called
mcmc_informations
(in the workspace) with the following fields:Nblck
The number of MCMC chains.InitialParameters
ANblck*n
, wheren
is the number of estimated parameters, array of doubles. Initial state of the MCMC.LastParameters
ANblck*n
, wheren
is the number of estimated parameters, array of doubles. Current state of the MCMC.InitialLogPost
ANblck*1
array of doubles. Initial value of the posterior kernel.LastLogPost
ANblck*1
array of doubles. Current value of the posterior kernel.InitialSeeds
A1*Nblck
structure array. Initial state of the random number generator.LastSeeds
A1*Nblck
structure array. Current state of the random number generator.AcceptanceRatio
A1*Nblck
array of doubles. Current acceptance ratios.

MATLAB/Octave command:
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 MonteCarlo. 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.