Prevents crashes during estimation. Instead of directly crashing, error handling is done via print_info.m so that penalizing the error during estimation is possible
* Second and third positional arguments after the name of the
estimated parameter in the estimated_params block are only
considered in the optimization stage (not in the MCMC)
* Do not store bounds in bayestopt_, because bounds do not always
reflect restrictions implied by prior shapes.
* prior_bounds routine returns a structure (with fields lb and ub)
instead of a matrix.
- the function was called with the wrong input argument for this case (Q instead of R*Q*R'), crashing with incompatible matrix dimensions
- the persistent variable X was not updated when the size of it changed, leading to crashes when estimation is followed by calls to DsgeSmoother.m where the state-space is different
- Also eliminates the printed output in lyapunov_symm.m that could not be turned off and clutters estimation
If a file <MOD_FILE_NAME>_prior_restrictions.m exists in current folder, the value returned by this routine is
substracted to fval (likelihood-lnprior) at the end of dsge_likelihood. The arguments of this routine are: M_,
oo_, options_, dataset_ and dataset_info. Routines for writing <MOD_FILE_NAME>_prior_restrictions.m will be
provided later.
Uses preprocessing capabilities introduced in 07137e804bFixes#392 and #494. Also fixes a bug in the checking for positive definiteness of covariance matrices in likelihood functions
Allows for calibrated covariances by reading them out and setting them after covariance matrix has been reconstructed from correlation and variances.
Adds unit test
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Lines 399-418 set the measurement covariance matrix and save it to H1.
If it is diagonal, it is not recomputed again as
correlated_errors_have_been_checked is 0. In that case, lines 654-675
are not entered and univariate_kalman_filter tries to use the old H, but
it was named H1 before, leading to a crash. Changing the name of the
matrix H in lines 654-682 to H1 assures that univariate_kalman_filter
uses the correctly updated matrix of the
~correlated_errors_have_been_checked and the previously computed H1 in
the other cases.
Add option and code for endogenous priors according to
Christiano/Trabandt/Walentin 2011, JEDC. Still needs to be integrated to
manual and pre-processor.
1) allow to compute derivatives starting from NUMERICAL derivatives of jacobian and steady state: this has a minor cost in accuracy and allow apply without errors identification and estimation with numerical derivatives;
2) added trap in dynare_estimation_init: if steadystate changes param values, automaticly shifts to numerical derivs of jacoban and steady state + analytic derivatives of all the rest;
3) bug fixes for 2nd order derivatives w.r.t. model parameters;
If options_.prior_trunc is set to zero (the default is strictly positive) then prior_correction is infinite because the prior density is zero (this is not true for the uniform prior)... This does not help the optimizer. Even if we do not fall in this case (because options_.prior_trunc>0 or becuase only uniform priors are used for the bounded parameters) the meaning of this correction is unclear.
Johannes Pfeifer
Stéphane Adjemian (Karaba)
Marco Ratto
Sébastien Villemot
Sébastien Villemot
Johannes Pfeifer
Michel Juillard
Sébastien Villemot
Ferhat Mihoubi