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Improve documentation on unconditional variance decomposition

time-shift
Johannes Pfeifer 2014-04-07 11:30:11 +02:00
parent 3afdbf8e47
commit 4a8e737c7a
1 changed files with 23 additions and 17 deletions
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doc/dynare.texi
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@ -3643,7 +3643,7 @@ period(s). The periods must be strictly positive. Conditional variances are give
decomposition provides the decomposition of the effects of shocks upon
impact. The results are stored in
@code{oo_.conditional_variance_decomposition}
(@pxref{oo_.conditional_variance_decomposition}). The variance decomposition is only conducted, if theoretical moments are requested, i.e. using the @code{periods=0}-option. In case of @code{order=2}, Dynare provides a second-order accurate approximation to the true second moments based on the linear terms of the second-order solution (see @cite{Kim, Kim, Schaumburg and Sims (2008)}).
(@pxref{oo_.conditional_variance_decomposition}). The variance decomposition is only conducted, if theoretical moments are requested, i.e. using the @code{periods=0}-option. In case of @code{order=2}, Dynare provides a second-order accurate approximation to the true second moments based on the linear terms of the second-order solution (see @cite{Kim, Kim, Schaumburg and Sims (2008)}). Note that the unconditional variance decomposition (i.e. at horizon infinity) is automatically conducted if theoretical moments are requested (@pxref {oo_.variance_decomposition})
@item pruning
Discard higher order terms when iteratively computing simulations of
@ -3792,7 +3792,7 @@ number of the matrix in the cell array corresponds to the order of
autocorrelation. The option @code{ar} specifies the number of
autocorrelation matrices available. Contains theoretical
autocorrelations if the @code{periods} option is not present (or an approximation thereof for @code{order=2}), and
empirical autocorrelations otherwise.
empirical autocorrelations otherwise. The field is only created if stationary variables are present.
The element @code{oo_.autocorr@{i@}(k,l)} is equal to the correlation
between @math{y^k_t} and @math{y^l_{t-i}}, where @math{y^k}
@ -3819,7 +3819,7 @@ details. Beware, this is the @i{autocorrelation} function, not the
@i{autocovariance} function.
@item oo_.gamma@{nar+2@}
Variance decomposition.
Unconditional variance decomposition @pxref{oo_.variance_decomposition}
@item oo_.gamma@{nar+3@}
If a second order approximation has been requested, contains the
@ -3830,6 +3830,22 @@ In case of @code{order=2}, the theoretical second moments are a second order acc
@end defvr
@anchor{oo_.variance_decomposition}
@defvr {MATLAB/Octave variable} oo_.variance_decomposition
After a run of @code{stoch_simul} when requesting theoretical moments (@code{periods=0}), contains a matrix with the result of the unconditional variance decomposition (i.e. at horizon infinity). The first dimension corresponds to the endogenous variables (in the order of declaration) and the second dimension corresponds to exogenous variables (in the order of declaration). Numbers are in percent and sum up to 100 across columns.
@end defvr
@anchor{oo_.conditional_variance_decomposition}
@defvr {MATLAB/Octave variable} oo_.conditional_variance_decomposition
After a run of @code{stoch_simul} with the
@code{conditional_variance_decomposition} option, contains a
three-dimensional array with the result of the decomposition. The
first dimension corresponds to forecast horizons (as declared with the
option), the second dimension corresponds to endogenous variables (in
the order of declaration), the third dimension corresponds to
exogenous variables (in the order of declaration).
@end defvr
@defvr {MATLAB/Octave variable} oo_.irfs
After a run of @code{stoch_simul} with option @code{irf} different
from zero, contains the impulse responses, with the following naming
@ -4123,16 +4139,6 @@ three times in the unfolded @math{G_3} matrix, they must be multiplied
by 3 when computing the decision rules.
@end itemize
@anchor{oo_.conditional_variance_decomposition}
@defvr {MATLAB/Octave variable} oo_.conditional_variance_decomposition
After a run of @code{stoch_simul} with the
@code{conditional_variance_decomposition} option, contains a
three-dimensional array with the result of the decomposition. The
first dimension corresponds to forecast horizons (as declared with the
option), the second dimension corresponds to endogenous variables (in
the order of declaration), the third dimension corresponds to
exogenous variables (in the order of declaration).
@end defvr
@node Estimation
@section Estimation
@ -4984,8 +4990,7 @@ model. Default: @code{4}.
@anchor{moments_varendo} Triggers the computation of the posterior
distribution of the theoretical moments of the endogenous
variables. Results are stored in
@code{oo_.PosteriorTheoreticalMoments} (see below for a description of
this variable). The number of lags in the autocorrelation function is
@code{oo_.PosteriorTheoreticalMoments} (@pxref{oo_.PosteriorTheoreticalMoments}). The number of lags in the autocorrelation function is
controlled by the @code{ar} option.
@item conditional_variance_decomposition = @var{INTEGER}
@ -5002,7 +5007,7 @@ period 1, the conditional variance decomposition provides the
decomposition of the effects of shocks upon impact. The results are
stored in
@code{oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition},
but currently there is no output. Note that this option requires the
but currently there is no displayed output. Note that this option requires the
option @code{moments_varendo} to be specified.
@item filtered_vars
@ -5429,6 +5434,7 @@ After an estimation with Metropolis, fields are of the form:
@end defvr
@defvr {MATLAB/Octave variable} oo_.PosteriorTheoreticalMoments
@anchor{oo_.PosteriorTheoreticalMoments}
Variable set by the @code{estimation} command, if it is used with the
@code{moments_varendo} option. Fields are of the form:
@example
@ -5446,7 +5452,7 @@ Auto- and cross-correlation of endogenous variables. Fields are vectors with cor
@item VarianceDecomposition
Decomposition of variance@footnote{When the shocks are correlated, it
Decomposition of variance (unconditional variance, i.e. at horizon infinity)@footnote{When the shocks are correlated, it
is the decomposition of orthogonalized shocks via Cholesky
decomposition according to the order of declaration of shocks
(@pxref{Variable declarations})}