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Merge branch 'bound_documentation'

time-shift
Stéphane Adjemian (Charybdis) 2014-09-19 23:39:40 +02:00
parent 2e08025cac 9eaf0a1ff5
commit 009f5f7efc
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doc/dynare.texi
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@ -4353,10 +4353,20 @@ Specifies a starting value for the posterior mode optimizer or the
maximum likelihood estimation. If unset, defaults to the prior mean.
@item @var{LOWER_BOUND}
Specifies a lower bound for the parameter value in maximum likelihood estimation
@anchor{lower_bound} Specifies a lower bound for the parameter value in maximum
likelihood estimation. In a Bayesian estimation context, sets a lower bound
only effective while maximizing the posterior kernel. This lower bound does not
modify the shape of the prior density, and is only aimed at helping the
optimizer in identifying the posterior mode (no consequences for the MCMC). For
some prior densities (namely inverse gamma, gamma, uniform or beta) it is
possible to shift the default lower bound (zero) on the left or the right using
@ref{prior_3rd_parameter}. In this case the prior density is effectively
modified (note that the truncated Gaussian density is not implemented in
Dynare). If unset, defaults to minus infinity (ML) or the natural lower bound
of the prior (Bayesian estimation).
@item @var{UPPER_BOUND}
Specifies an upper bound for the parameter value in maximum likelihood estimation
Same as @ref{lower_bound}, but specifying an upper bound instead.
@item @var{PRIOR_SHAPE}
A keyword specifying the shape of the prior density.
@ -4368,16 +4378,18 @@ that @code{inv_gamma_pdf} is equivalent to
@code{inv_gamma1_pdf}
@item @var{PRIOR_MEAN}
The mean of the prior distribution
@anchor{prior_mean} The mean of the prior distribution
@item @var{PRIOR_STANDARD_ERROR}
The standard error of the prior distribution
@anchor{prior_standard_error} The standard error of the prior distribution
@item @var{PRIOR_3RD_PARAMETER}
@anchor{prior_3rd_parameter}
A third parameter of the prior used for generalized beta distribution,
generalized gamma and for the uniform distribution. Default: @code{0}
@item @var{PRIOR_4TH_PARAMETER}
@anchor{prior_4th_parameter}
A fourth parameter of the prior used for generalized beta distribution
and for the uniform distribution. Default: @code{1}
@ -5731,16 +5743,29 @@ estimates using a higher tapering are usually more reliable.
@deffn Command model_comparison @var{FILENAME}[(@var{DOUBLE})]@dots{};
@deffnx Command model_comparison (marginal_density = laplace | modifiedharmonicmean) @var{FILENAME}[(@var{DOUBLE})]@dots{};
@anchor{model_comparison}
@descriptionhead
This command computes odds ratios and estimate a posterior density
over a collection of models
(see e.g. @cite{Koop (2003), Ch. 1}). The priors over models can be specified
as the @var{DOUBLE} values, otherwise a uniform prior over all models is assumed.
In contrast to frequentist econometrics, the models to be compared do not need to be nested.
However, as the computation of posterior odds ratios is a Bayesian technique,
the comparison of models estimated with maximum likelihood is not supported.
This command computes odds ratios and estimate a posterior density over a
collection of models (see e.g. @cite{Koop (2003), Ch. 1}). The priors over
models can be specified as the @var{DOUBLE} values, otherwise a uniform prior
over all models is assumed. In contrast to frequentist econometrics, the
models to be compared do not need to be nested. However, as the computation of
posterior odds ratios is a Bayesian technique, the comparison of models
estimated with maximum likelihood is not supported.
It is important to keep in mind that model comparison of this type is only
valid with proper priors. If the prior does not integrate to one for all
compared models, the comparison is not valid. This may be the case if part of
the prior mass is implicitly truncated because Blanchard and Kahn conditions
(instability or indeterminacy of the model) are not fulfilled, or because for
some regions of the parameters space the deterministic steady state is
undefined (or Dynare is unable to find it). The compared marginal densities
should be renormalized by the effective prior mass, but this not done by
Dynare: it is the user's responsibility to make sure that model comparison is
based on proper priors. Note that, for obvious reasons, this is not an issue if
the compared marginal densities are based on Laplace approximations.
@examplehead