Document new search matlab command.
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@ -10,7 +10,7 @@ Dynare misc commands
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.. command:: prior_function(OPTIONS);
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|br| Executes a user-defined function on parameter draws from the prior
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Executes a user-defined function on parameter draws from the prior
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distribution. Dynare returns the results of the computations for
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all draws in an $ndraws$ by $n$ cell array named
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``oo_.prior_function_results``.
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@ -30,9 +30,11 @@ Dynare misc commands
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Number of draws used for sampling. Default: 500.
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|br|
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.. command:: posterior_function(OPTIONS);
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|br| Same as the :comm:`prior_function` command but for the
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Same as the :comm:`prior_function` command but for the
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posterior distribution. Results returned in
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``oo_.posterior_function_results``.
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@ -46,23 +48,27 @@ Dynare misc commands
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See :opt:`prior_function_sampling_draws <sampling_draws = INTEGER>`.
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|br|
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.. command:: generate_trace_plots(CHAIN_NUMBER);
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|br| Generates trace plots of the MCMC draws for all estimated
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Generates trace plots of the MCMC draws for all estimated
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parameters and the posterior density in the specified Markov Chain
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``CHAIN_NUMBER``.
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|br|
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.. matcomm:: internals FLAG ROUTINENAME[.m]|MODFILENAME
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|br| Depending on the value of ``FLAG``, the ``internals`` command
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can be used to run unitary tests specific to a MATLAB/Octave
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routine (if available), to display documentation about a
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MATLAB/Octave routine, or to extract some informations about the
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state of Dynare.
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Depending on the value of ``FLAG``, the ``internals`` command
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can be used to run unitary tests specific to a MATLAB/Octave
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routine (if available), to display documentation about a
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MATLAB/Octave routine, or to extract some informations about the
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state of Dynare.
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*Flags*
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*Flags*
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``--test``
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``--test``
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Performs the unitary test associated to ROUTINENAME (if this
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routine exists and if the matlab/octave ``.m`` file has
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@ -79,7 +85,7 @@ Dynare misc commands
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>> internals --test ../matlab/fr/ROUTINENAME
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``--display-mh-history``
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``--display-mh-history``
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Displays information about the previously saved MCMC draws
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generated by a ``.mod`` file named MODFILENAME. This file must
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@ -91,9 +97,9 @@ Dynare misc commands
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>> internals --display-mh-history MODFILENAME
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``--load-mh-history``
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``--load-mh-history``
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|br| Loads into the MATLAB/Octave’s workspace informations
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Loads into the MATLAB/Octave’s workspace informations
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about the previously saved MCMC draws generated by a ``.mod``
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file named MODFILENAME.
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@ -146,56 +152,90 @@ Dynare misc commands
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A ``1*Nblck`` array of doubles. Current acceptance ratios.
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|br|
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.. matcomm:: prior [OPTIONS[, ...]];
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Prints information about the prior distribution given the provided
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options. If no options are provided, the command returns the list of
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available options.
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Prints information about the prior distribution given the provided
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options. If no options are provided, the command returns the list of
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available options.
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*Options*
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*Options*
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.. option:: table
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.. option:: table
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Prints a table describing the marginal prior distributions
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(mean, mode, std., lower and upper bounds, HPD interval).
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Prints a table describing the marginal prior distributions
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(mean, mode, std., lower and upper bounds, HPD interval).
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.. option:: moments
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.. option:: moments
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Computes and displays first and second order moments of the
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endogenous variables at the prior mode (considering the
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linearized version of the model).
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Computes and displays first and second order moments of the
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endogenous variables at the prior mode (considering the
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linearized version of the model).
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.. option:: moments(distribution)
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.. option:: moments(distribution)
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Computes and displays the prior mean and prior standard
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deviation of the first and second moments of the endogenous
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variables (considering the linearized version of the model) by
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randomly sampling from the prior. The results will also be
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stored in the ``prior`` subfolder in a
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``_endogenous_variables_prior_draws.mat`` file.
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Computes and displays the prior mean and prior standard
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deviation of the first and second moments of the endogenous
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variables (considering the linearized version of the model) by
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randomly sampling from the prior. The results will also be
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stored in the ``prior`` subfolder in a
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``_endogenous_variables_prior_draws.mat`` file.
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.. option:: optimize
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.. option:: optimize
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Optimizes the prior density (starting from a random initial
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guess). The parameters such that the steady state does not
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exist or does not satisfy the Blanchard and Kahn conditions
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are penalized, as they would be when maximizing the posterior
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density. If a significant proportion of the prior mass is
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defined over such regions, the optimization algorithm may fail
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to converge to the true solution (the prior mode).
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Optimizes the prior density (starting from a random initial
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guess). The parameters such that the steady state does not
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exist or does not satisfy the Blanchard and Kahn conditions
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are penalized, as they would be when maximizing the posterior
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density. If a significant proportion of the prior mass is
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defined over such regions, the optimization algorithm may fail
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to converge to the true solution (the prior mode).
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.. option:: simulate
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.. option:: simulate
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Computes the effective prior mass using a Monte-Carlo. Ideally
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the effective prior mass should be equal to 1, otherwise
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problems may arise when maximising the posterior density and
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model comparison based on marginal densities may be
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unfair. When comparing models, say :math:`A` and :math:`B`,
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the marginal densities, :math:`m_A` and :math:`m_B`, should be
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corrected for the estimated effective prior mass
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:math:`p_A\neq p_B \leq 1` so that the prior mass of the
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compared models are identical.
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Computes the effective prior mass using a Monte-Carlo. Ideally
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the effective prior mass should be equal to 1, otherwise
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problems may arise when maximising the posterior density and
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model comparison based on marginal densities may be
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unfair. When comparing models, say :math:`A` and :math:`B`,
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the marginal densities, :math:`m_A` and :math:`m_B`, should be
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corrected for the estimated effective prior mass
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:math:`p_A\neq p_B \leq 1` so that the prior mass of the
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compared models are identical.
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.. option:: plot
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.. option:: plot
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Plots the marginal prior density.
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Plots the marginal prior density.
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|br|
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.. matcomm:: search VARIABLENAME[ OPTION]
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Searches all occurrences of a variable in a model, and prints the
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equations where the variable appear in the command line window. If OPTION is
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set to `withparamvalues`, the values of the parameters (if available) are
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displayed instead of the name of the parameters.
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*Example*
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Assuming that we already ran a `.mod` file and that the workspace has not
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been cleaned after, we can search for all the equations containing variable `X`
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::
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>> search X
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Y = alpha*X/(1-X)+e;
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diff(X) = beta*(X(-1)-mX) + gamma1*Z + gamma2*R + u;
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To replace the parameters with estimated or calibrated parameters:
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::
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>> search X withparamvalues
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Y = 1.254634*X/(1-X)+e;
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diff(X) = -0.031459*(X(-1)-mX) + 0.1*Z - 0.2*R + u;
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