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<a name="Dynare-misc-commands"></a>
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<p>
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<a name="Dynare-misc-commands-1"></a>
<h2 class="chapter">9 Dynare misc commands</h2>

<a name="prior_005ffunction"></a><dl>
<dt><a name="index-prior_005ffunction_0028OPTIONS_0029"></a>Command: <strong>prior_function(<var>OPTIONS</var>)</strong> <em>;</em></dt>
<dd>
<p>Executes a user-defined function on parameter draws from the prior
distribution. Dynare returns the results of the computations for all draws in an
<SPAN CLASS="MATH"><IMG
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 SRC="dynare.html_181.png"
 ALT="$ndraws$"></SPAN> by <SPAN CLASS="MATH"><IMG
 WIDTH="14" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
 SRC="dynare.html_74.png"
 ALT="$n$"></SPAN> cell array named <var>oo_.prior_function_results</var>.
</p>
<p><em>Options</em>
</p>
<dl compact="compact">
<dd>
<a name="prior_005ffunction_005ffunction"></a></dd>
<dt><code>function = <var>FUNCTION_NAME</var></code></dt>
<dd><p>The function must have the following header <code>output_cell =
FILENAME(xparam1,M_,options_,oo_,estim_params_,bayestopt_,dataset_,dataset_info)</code>,
providing read-only access to all Dynare structures. The only output argument
allowed is a <SPAN CLASS="MATH"><IMG
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 SRC="dynare.html_74.png"
 ALT="$n$"></SPAN> cell array, which allows for storing any type of
output/computations. This option is required.
</p>
<a name="prior_005ffunction_005fsampling_005fdraws"></a></dd>
<dt><code>sampling_draws = <var>INTEGER</var></code></dt>
<dd><p>Number of draws used for sampling. Default: 500.
</p>
</dd>
</dl>

</dd></dl>

<dl>
<dt><a name="index-posterior_005ffunction_0028OPTIONS_0029"></a>Command: <strong>posterior_function(<var>OPTIONS</var>)</strong> <em>;</em></dt>
<dd>
<p>Same as the <a href="#prior_005ffunction">prior_function</a> command but for the posterior
distribution. Results returned in <var>oo_.posterior_function_results</var>
</p>
<p><em>Options</em>
</p>
<dl compact="compact">
<dt><code>function = <var>FUNCTION_NAME</var></code></dt>
<dd><p>See <a href="#prior_005ffunction_005ffunction">prior_function_function</a>.
</p>
</dd>
<dt><code>sampling_draws = <var>INTEGER</var></code></dt>
<dd><p>See <a href="#prior_005ffunction_005fsampling_005fdraws">prior_function_sampling_draws</a>.
</p>
</dd>
</dl>

</dd></dl>

<a name="generate_005ftrace_005fplots"></a><dl>
<dt><a name="index-generate_005ftrace_005fplots_0028CHAIN_005fNUMBER_0029"></a>Command: <strong>generate_trace_plots(<var>CHAIN_NUMBER</var>)</strong> <em>;</em></dt>
<dd>
<p>Generates trace plots of the MCMC draws for all estimated parameters and the posterior density in the specified Markov Chain <code>CHAIN_NUMBER</code>.
</p>
</dd></dl>

<a name="internals"></a><dl>
<dt><a name="index-internals"></a>MATLAB/Octave command: <strong>internals</strong> <em><var>FLAG</var> <var>ROUTINENAME</var>[.m]|<var>MODFILENAME</var></em></dt>
<dd>
<p>Depending on the value of <var>FLAG</var>, the <code>internals</code> 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.
</p>
<p><em>Flags</em>
</p>
<dl compact="compact">
<dt><code>--test</code></dt>
<dd><p>Performs the unitary test associated to  <var>ROUTINENAME</var> (if this routine exists and if the matalab/octave <code>m</code>
file has unitary test sections).
<em>Example</em>
</p><div class="example">
<pre class="example">&gt;&gt; internals --test ROUTINENAME
</pre></div>
<p>if <code>routine.m</code> is  not in the current directory, the  full path has
to be given:
</p><div class="example">
<pre class="example">&gt;&gt; internals --test ../matlab/fr/ROUTINENAME
</pre></div>

</dd>
<dt><code>--info</code></dt>
<dd><p>Prints  on screen  the internal  documentation of  <var>ROUTINENAME</var> (if
this  routine  exists  and  if  this  routine  has  a  texinfo  internal
documentation header). The path to <var>ROUTINENAME</var> has to be provided,
if the routine is not in the current directory.
<em>Example</em>
</p><div class="example">
<pre class="example">&gt;&gt; internals --doc ../matlab/fr/ROUTINENAME
</pre></div>
<p>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.
</p>
</dd>
<dt><code>--display-mh-history</code></dt>
<dd><p>Displays information about the previously saved MCMC draws generated by a mod file named <var>MODFILENAME</var>. This file must be in the current directory.
<em>Example</em>
</p><div class="example">
<pre class="example">&gt;&gt; internals --display-mh-history MODFILENAME
</pre></div>

</dd>
<dt><code>--load-mh-history</code></dt>
<dd><p>Loads into the Matlab/Octave&rsquo;s workspace informations  about the previously saved MCMC draws generated by a mod file named <var>MODFILENAME</var>.
<em>Example</em>
</p><div class="example">
<pre class="example">&gt;&gt; internals --load-mh-history MODFILENAME
</pre></div>
<p>This will create a structure called <code>mcmc_informations</code> (in the workspace) with the following fields:
</p><dl compact="compact">
<dt><code>Nblck</code></dt>
<dd><p>The number of MCMC chains.
</p></dd>
<dt><code>InitialParameters</code></dt>
<dd><p>A <code>Nblck*n</code>, where <code>n</code> is the number of estimated parameters, array of doubles. Initial state of the MCMC.
</p></dd>
<dt><code>LastParameters</code></dt>
<dd><p>A <code>Nblck*n</code>, where <code>n</code> is the number of estimated parameters, array of doubles. Current state of the MCMC.
</p></dd>
<dt><code>InitialLogPost</code></dt>
<dd><p>A <code>Nblck*1</code> array of doubles. Initial value of the posterior kernel.
</p></dd>
<dt><code>LastLogPost</code></dt>
<dd><p>A <code>Nblck*1</code> array of doubles. Current value of the posterior kernel.
</p></dd>
<dt><code>InitialSeeds</code></dt>
<dd><p>A <code>1*Nblck</code> structure array. Initial state of the random number generator.
</p></dd>
<dt><code>LastSeeds</code></dt>
<dd><p>A <code>1*Nblck</code> structure array. Current  state of the random number generator.
</p></dd>
<dt><code>AcceptanceRatio</code></dt>
<dd><p>A <code>1*Nblck</code> array of doubles. Current acceptance ratios.
</p></dd>
</dl>

</dd>
</dl>

</dd></dl>


<dl>
<dt><a name="index-prior"></a>MATLAB/Octave command line: <strong>prior</strong> <em>[options[, ...]];</em></dt>
<dd>
<p>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:
</p>
<dl compact="compact">
<dt><code>table</code></dt>
<dd><p>Prints a table describing the marginal prior distributions (mean, mode,
std., lower and upper bounds, HPD interval).
</p>
</dd>
<dt><code>moments</code></dt>
<dd><p>Computes and displays first and second order moments of the endogenous
variables at the prior mode (considering the linearized version of the
model).
</p>
</dd>
<dt><code>optimize</code></dt>
<dd><p>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). 
</p>
</dd>
<dt><code>simulate</code></dt>
<dd><p>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 <SPAN CLASS="MATH"><IMG
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and <SPAN CLASS="MATH"><IMG
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be corrected for the estimated effective prior mass <!-- MATH
 $p_A\neq p_B
\leq 1$
 -->
<SPAN CLASS="MATH"><IMG
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\leq 1$"></SPAN> so that the prior mass of the compared models are identical.
</p>
</dd>
<dt><code>plot</code></dt>
<dd><p>Plots the marginal prior density.
</p>
</dd>
</dl>

</dd></dl>

<hr>
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