Commit ff7792a7 authored by Sébastien Villemot's avatar Sébastien Villemot
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The variance prior for BVAR "à la Sims" with only one lag is inconsistent; the

solution implemented consists of adding one extra observation in the presample
used to compute the prior; as a consequence, the numerical results for all
estimations will be slightly different in future releases (thanks to Marek
Jarociński for spotting this)
parent 1b4dcdd7
...@@ -102,7 +102,13 @@ Before constructing the dummy observations, one needs to choose values for the f ...@@ -102,7 +102,13 @@ Before constructing the dummy observations, one needs to choose values for the f
\item $d$: the decay factor for scaling down the coefficients of lagged values. Controlled by option \texttt{bvar\_prior\_decay}, with a default of 0.5 \item $d$: the decay factor for scaling down the coefficients of lagged values. Controlled by option \texttt{bvar\_prior\_decay}, with a default of 0.5
\item $\omega$ controls the tightness for the prior on $\Sigma$. Must be an integer. Controlled by option \texttt{bvar\_prior\_omega}, with a default of 1 \item $\omega$ controls the tightness for the prior on $\Sigma$. Must be an integer. Controlled by option \texttt{bvar\_prior\_omega}, with a default of 1
\item $\lambda$ and $\mu$: additional tuning parameters, respectively controlled by option \texttt{bvar\_prior\_lambda} (with a default of 5) and option \texttt{bvar\_prior\_mu} (with a default of 2) \item $\lambda$ and $\mu$: additional tuning parameters, respectively controlled by option \texttt{bvar\_prior\_lambda} (with a default of 5) and option \texttt{bvar\_prior\_mu} (with a default of 2)
\item based on a short presample $Y^0$ (in Dynare implementation, this presample consists of the $p$ observations used to initialize the VAR), one also calculates $\sigma = std(Y^0)$ and $\bar{y} = mean(Y^0)$ \item based on a short presample $Y^0$ (in Dynare implementation, this
presample consists of the $p$ observations used to initialize the VAR, plus
one extra observation at the beginning of the sample\footnote{In Dynare 4.2.1
and older versions, only $p$ observations where used. As a consequence the
case $p=1$ was buggy, since the standard error of a one observation sample
is undefined.}), one also calculates $\sigma = std(Y^0)$ and $\bar{y} =
mean(Y^0)$
\end{itemize} \end{itemize}
Below is a description of the different dummy observations. For the sake of simplicity, we should assume that $ny = 2$, $nx = 1$ and $p = 3$. The generalization is straigthforward. Below is a description of the different dummy observations. For the sake of simplicity, we should assume that $ny = 2$, $nx = 1$ and $p = 3$. The generalization is straigthforward.
......
...@@ -98,7 +98,7 @@ mnprior.tight = options_.bvar_prior_tau; ...@@ -98,7 +98,7 @@ mnprior.tight = options_.bvar_prior_tau;
mnprior.decay = options_.bvar_prior_decay; mnprior.decay = options_.bvar_prior_decay;
% Use only initializations lags for the variance prior % Use only initializations lags for the variance prior
vprior.sig = std(dataset(options_.first_obs+options_.presample-nlags:options_.first_obs+options_.presample-1,:))'; vprior.sig = std(dataset(options_.first_obs+options_.presample-nlags:options_.first_obs+options_.presample,:))'
vprior.w = options_.bvar_prior_omega; vprior.w = options_.bvar_prior_omega;
lambda = options_.bvar_prior_lambda; lambda = options_.bvar_prior_lambda;
......
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