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Document new filter option

time-shift
Johannes Pfeifer 2015-10-12 17:13:50 +02:00
parent 40877685f2
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@ -3703,13 +3703,27 @@ Number of points (burnin) dropped at the beginning of simulation before computin
@item hp_filter = @var{DOUBLE}
Uses HP filter with @math{\lambda} = @var{DOUBLE} before computing
moments. Default: no filter.
moments. If theoretical moments are requested, the spectrum of the model solution is filtered
following the approach outlined in @cite{Uhlig (2001)}.
Default: no filter.
@item hp_ngrid = @var{INTEGER}
Number of points in the grid for the discrete Inverse Fast Fourier
Transform used in the HP filter computation. It may be necessary to
increase it for highly autocorrelated processes. Default: @code{512}.
@item bandpass_filter
Uses a bandpass filter with the default passband before computing moments. If theoretical moments are
requested, the spectrum of the model solution is filtered using an ideal bandpass
filter. If empirical moments are requested, the @cite{Baxter and King (1999)}-filter
is used.
Default: no filter.
@item bandpass_filter = @var{[HIGHEST_PERIODICITY LOWEST_PERIODICITY]}
Uses a bandpass filter before computing moments. The passband is set to a periodicity of @code{HIGHEST_PERIODICITY}
to @code{LOWEST_PERIODICITY}, e.g. 6 to 32 quarters if the model frequency is quarterly.
Default: @code{[6,32]}.
@item irf = @var{INTEGER}
@anchor{irf}
Number of periods on which to compute the IRFs. Setting @code{irf=0},
@ -10769,7 +10783,7 @@ ts1 is a dseries object:
@deftypefn {dseries} {@var{B} = } baxter_king_filter (@var{A}, @var{hf}, @var{lf}, @var{K})
Implementation of the Baxter and King (1999) band pass filter for @dseries objects. This filter isolates business cycle fluctuations with a period of length ranging between @var{hf} (high frequency) to @var{lf} (low frequency) using a symmetric moving average smoother with @math{2K+1} points, so that K observations at the beginning and at the end of the sample are lost in the computation of the filter. The default value for @var{hf} is @math{6}, for @var{lf} is @math{32}, and for @var{K} is 12.
Implementation of the @cite{Baxter and King (1999)} band pass filter for @dseries objects. This filter isolates business cycle fluctuations with a period of length ranging between @var{hf} (high frequency) to @var{lf} (low frequency) using a symmetric moving average smoother with @math{2K+1} points, so that K observations at the beginning and at the end of the sample are lost in the computation of the filter. The default value for @var{hf} is @math{6}, for @var{lf} is @math{32}, and for @var{K} is 12.
@examplehead
@example
@ -12933,6 +12947,11 @@ Backus, David K., Patrick J. Kehoe, and Finn E. Kydland (1992):
``International Real Business Cycles,'' @i{Journal of Political
Economy}, 100(4), 745--775
@item
Baxter, Marianne and Robert G. King (1999):
``Measuring Business Cycles: Approximate Band-pass Filters for Economic Time Series,''
@i{Review of Economics and Statistics}, 81(4), 575--593
@item
Boucekkine, Raouf (1995): ``An alternative methodology for solving
nonlinear forward-looking models,'' @i{Journal of Economic Dynamics
@ -13129,6 +13148,11 @@ Smets, Frank and Rafael Wouters (2003): ``An Estimated Dynamic
Stochastic General Equilibrium Model of the Euro Area,'' @i{Journal of
the European Economic Association}, 1(5), 1123--1175
@item
Uhlig, Harald (2001): ``A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily,''
in @i{Computational Methods for the Study of Dynamic
Economies}, Eds. Ramon Marimon and Andrew Scott, Oxford University Press, 30--61
@item
Villemot, Sébastien (2011): ``Solving rational expectations models at
first order: what Dynare does,'' @i{Dynare Working Papers}, 2,