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fix typos

time-shift
Houtan Bastani 2011-09-13 13:56:04 -04:00
parent d9ede95ffa
commit 176afda1bc
1 changed files with 11 additions and 11 deletions
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doc/dynare.texi
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@ -4838,21 +4838,21 @@ need of user intervention.
The RMSE analysis can be performed with different types of sampling options:
@enumerate
@item
When @code{pprior=1} and @code{ppost=0}, the toolbox analyzes the RMSE’s for
When @code{pprior=1} and @code{ppost=0}, the toolbox analyzes the RMSEs for
the Monte-Carlo sample obtained by sampling parameters from their prior distributions
(or prior ranges): this analysis provides some hints about
what parameter drives the fit of which observed series, prior to the full
estimation;
@item
When @code{pprior=0} and @code{ppost=0}, the toolbox analyzes the RMSE’s for
When @code{pprior=0} and @code{ppost=0}, the toolbox analyzes the RMSEs for
a multivariate normal Monte-Carlo sample, with covariance matrix based on
the inverse Hessian at the optimum: this analysis is useful when maximum likelihood
estimation is done (@i{i.e.} no Bayesian estimation);
@item
When @code{ppost=1} the toolbox analyzes the RMSE’s for the posterior sample
obtained by Dynare’s Metropolis procedure.
When @code{ppost=1} the toolbox analyzes the RMSEs for the posterior sample
obtained by Dynare's Metropolis procedure.
@end enumerate
The use of cases 2 and 3 requires an estimation step beforehand. To
@ -4904,34 +4904,34 @@ but the same conventions are used for multivariate normal and posterior):
@itemize
@item
@code{<mod_file>_rmse_prior_*.fig}: for each parameter, plots the cdf’s
corresponding to the best 10% RMES’s of each observed series;
@code{<mod_file>_rmse_prior_*.fig}: for each parameter, plots the cdfs
corresponding to the best 10% RMSEs of each observed series;
@item
@code{<mod_file>_rmse_prior_dens_*.fig}: for each parameter, plots the
pdf’s corresponding to the best 10% RMES’s of each observed series;
pdfs corresponding to the best 10% RMESs of each observed series;
@item
@code{<mod_file>_rmse_prior_<name of observedseries>_corr_*.fig}: for
each observed series plots the bi-dimensional projections of samples
with the best 10% RMSE’s, when the correlation is significant;
with the best 10% RMSEs, when the correlation is significant;
@item
@code{<mod_file>_rmse_prior_lnlik*.fig}: for each observed series, plots
in red the cdf of the log-likelihood corresponding to the best 10%
RMSE’s, in green the cdf of the rest of the sample and in blue the
RMSEs, in green the cdf of the rest of the sample and in blue the
cdf of the full sample; this allows one to see the presence of some
idiosyncratic behavior;
@item
@code{<mod_file>_rmse_prior_lnpost*.fig}: for each observed series, plots
in red the cdf of the log-posterior corresponding to the best 10% RMSE’s,
in red the cdf of the log-posterior corresponding to the best 10% RMSEs,
in green the cdf of the rest of the sample and in blue the cdf of the full
sample; this allows one to see idiosyncratic behavior;
@item
@code{<mod_file>_rmse_prior_lnprior*.fig}: for each observed series, plots
in red the cdf of the log-prior corresponding to the best 10% RMSE’s,
in red the cdf of the log-prior corresponding to the best 10% RMSEs,
in green the cdf of the rest of the sample and in blue the cdf of the full
sample; this allows one to see idiosyncratic behavior;