manual updates
Marco Ratto
Stéphane Adjemian (Charybdis)
parent
d2d6e19d87
commit
06176caf64
187
doc/dynare.texi
187
doc/dynare.texi
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@ -7080,8 +7080,12 @@ graphs. See @code{colormap} in Matlab/Octave manual.
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@item nograph
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@xref{nograph}. Suppresses the display and creation only within the @code{shock_decomposition}-command
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but does not affect other commands.
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but does not affect other commands. See also @xref{plot_shock_decomposition} for plotting graphs.
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@item init_state = @var(INTEGER)
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@xref{init_state}. It can take values of 0 (=default) and 1.
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If @code{init_state= 1}, the shock decomposition is computed conditional on the smoothed state variables
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in period 1.
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@end table
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@vindex oo_.shock_decomposition
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@ -7141,6 +7145,163 @@ shocks_decomposition(use_shock_groups=group1);
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@end deffn
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@deffn Command realtime_shock_decomposition [@var{VARIABLE_NAME}]@dots{};
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@deffnx Command realtime_shock_decomposition (@var{OPTIONS}@dots{}) [@var{VARIABLE_NAME}]@dots{};
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@descriptionhead
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This command computes the recursive historical shock decomposition for a given sample based on
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the Kalman smoother, i.e. it decomposes the historical deviations of the endogenous
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variables from their respective steady state values into the contribution coming
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from the various shocks. The @code{variable_names} provided govern for which
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variables the decomposition is plotted.
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Note that this command must come after either @code{estimation} (in case
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of an estimated model) or @code{stoch_simul} (in case of a calibrated
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model).
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@optionshead
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@table @code
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@item parameter_set = @var{PARAMETER_SET}
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Specify the parameter set to use for running the smoother. The
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@var{PARAMETER_SET} can take one of the following seven values:
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@code{calibration}, @code{prior_mode}, @code{prior_mean},
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@code{posterior_mode}, @code{posterior_mean},
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@code{posterior_median}, @code{mle_mode}. Default value: @code{posterior_mean} if
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Metropolis has been run, @code{mle_mode} if MLE has been run.
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@item datafile = @var{FILENAME}
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@xref{datafile}. Useful when computing the shock decomposition on a
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calibrated model.
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@item first_obs = @var{INTEGER}
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@xref{first_obs}.
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@item nobs = @var{INTEGER}
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@xref{nobs}.
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@item use_shock_groups [= @var{SHOCK_GROUPS_NAME}]
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@anchor{use_shock_groups}. Uses groups of shocks instead of individual shocks in
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the decomposition. Groups of shocks are defined in @xref{shock_groups} block.
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@item colormap = @var{COLORMAP_NAME}
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@anchor{colormap}. Controls the colormap used for the shocks decomposition
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graphs. See @code{colormap} in Matlab/Octave manual.
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@item nograph
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@anchor{nograph}. Only shock decompositions are computed and stored in @code{oo_.shock_decomposition},
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but no plot is made (@xref{plot_shock_decomposition}).
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@item presample
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@anchor{presample}. First data point from which recursive realtime shock decompositions are computed, i.e. for T=(presample:nobs).
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@item forecast
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@anchor{forecast}. Compute shock decompositions up to T+k periods, i.e. get shock contributions to k-step ahead forecasts.
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@end table
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@vindex oo_.realtime_shock_decomposition
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The results of realtime decompositions are stored in the field @code{oo_.realtime_shock_decomposition}, which is a structure.
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Field @code{pool} stores the pooled decomposition @xref{plot_shock_decomposition}.
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Fields @code{time_*} store the vintages of realtime shock decompositions.
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@vindex oo_.conditional_shock_decomposition
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The results of realtime conditional decompositions are stored in the field @code{oo_.conditional_shock_decomposition}, which is a structure.
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Field @code{pool} stores the pooled decomposition @xref{plot_shock_decomposition}.
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Fields @code{time_*} store the vintages of conditional forecast shock decompositions.
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@vindex oo_.realtime_forecast_shock_decomposition
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The results of realtime forecast decompositions are stored in the field @code{oo_.realtime_forecast_shock_decomposition}, which is a structure.
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Field @code{pool} stores the pooled decomposition @xref{plot_shock_decomposition}.
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Fields @code{time_*} store the vintages of conditional forecast shock decompositions.
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@end deffn
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@deffn Command plot_shock_decomposition [@var{VARIABLE_NAME}]@dots{};
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@deffnx Command plot_shock_decomposition (@var{OPTIONS}@dots{}) [@var{VARIABLE_NAME}]@dots{};
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@descriptionhead
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This command plots the historical shock decomposition already computed by command
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@code{shock_decomposition}.
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The @code{variable_names} provided govern for which
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variables the decomposition is plotted.
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Note that this command must come after @code{shock_decomposition} or @code{realtime_shock_decomposition}.
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@optionshead
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@table @code
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@item use_shock_groups [= @var{SHOCK_GROUPS_NAME}]
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@anchor{use_shock_groups}. Uses groups of shocks instead of individual shocks in
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the decomposition. Groups of shocks are defined in @xref{shock_groups} block.
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@item colormap = @var{COLORMAP_NAME}
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@anchor{colormap}. Controls the colormap used for the shocks decomposition
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graphs. See @code{colormap} in Matlab/Octave manual.
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@item nodisplay
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@xref{nodisplay}.
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@item graph_format = (@{GRAPH_FORMAT_LIST})
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@xref{graph_format}.
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@item detailed
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@anchor{detailed}. Plots shock contributions using subplots, one per shock (or group of shocks).
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@item interactive
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@anchor{interactive}. Under MATLAB, add uimenu's for detailed group plots.
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@item screen
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@anchor{screen}. For large models [i.e. for models with more than 16 shocks], plots only the shocks
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that have the largest historical contribution for chosen selected @code{variable_names}.
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Historical contribution is ranked by the mean absolute value of all historical contributions.
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@item steadystate = @var{INTEGER}
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@anchor{steadystate}. 0=default.
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If =1, the the y-axis value of the zero line in the shock decomposition plot is translated to the steady state level.
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@item type = @var{TYPE_NAME}
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@anchor{type}. For quarterly data, the @var{TYPE_NAME} can take one of the following values:
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@code{qoq} for quarter-on-quarter plots,
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@code{yoy} for year-on-year plots of growth rates,
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@code{aoa} for annualized variables, i.e. the value in the last quarter for each year is plotted.
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Default value: @code{empty}, i.e. standard period-on-period plots [qoq for quarterly data].
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@item fig_name = @var{FIG_NAME}
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@anchor{fig_name}. Specifies a user-defined keyword to be appended to the default figure name set by @code{plot_shock_decomposition}.
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This can avoid to overwrite plots in case of sequential calls to @code{plot_shock_decomposition}.
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@item write_xls
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@anchor{write_xls}. Saves shock decompotions to excel.
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@item realtime = @var{INTEGER}
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@anchor{realtime}. Which kind of shock decomposition to plot. @var{INTEGER} can take following values:
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@code{0}: standard shock decomposition [DEFAULT]
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@code{1}: realtime shock decomposition: for T=1:nobs, realtime shock decompositions of Y(T|T)
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@code{2}: conditional shock decomposition: for T=1:nobs, realtime shock decompositions of Y(T|T) CONDITIONAL on Y(T|T-1)
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@code{3}: forecast shock decomposition: for T=1:nobs, realtime shock decompositions of Y(Y|T-1)
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@item vintage = @var{INTEGER}
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@anchor{vintage}. If @code{realtime}>0. @var{INTEGER} can take following values:
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@code{0}: plots 1step pooled shock decompositions [DEFAULT]
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@code{realtime=1}: pooled realtime shock decomposition. For T=1:nobs, plots last time point Y(T|T) of each vintage shock decomposition Y(1:T|T)
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@code{realtime=2}: pooled conditional shock decomposition. For T=1:nobs, realtime 1-step shock decomposition of Y(T|T) CONDITIONAL on Y(T|T-1)
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[i.e. decomposition of 1-step filter updates of each vintage T]
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@code{realtime=3}: pooled forecast shock decomposition. For T=1:nobs, realtime 1-step ahead shock decomposition of Y(T|T-1)
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[i.e. decomposition of shock contributions to 1-step ahead forecasts of each vintage T]
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@code{>0}: plots shock decompositions for vintage T=@code{vintage} under the following scenarios
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@code{realtime=1}: the full vintage shock decomposition Y(1:T|T)
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@code{realtime=2}: plots conditional forecast shock decomposition from T, i.e. plots Y(T+j|T+j) and the shock contributions needed to get to the data Y(T+j) CONDITIONAL on T=@code{vintage}, with j=(0:@code{forecast}).
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@code{realtime=3}: plots unconditional forecast shock decomposition from T, i.e. Y(T+j|T), where T=@code{vintage} and j=(0:@code{forecast}).
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@end table
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@end deffn
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@deffn Command unit_root_vars @var{VARIABLE_NAME}@dots{};
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This command is deprecated. Use @code{estimation} option @code{diffuse_filter} instead for estimating a model with non-stationary observed variables or @code{steady} option @code{nocheck} to prevent @code{steady} to check the steady state returned by your steady state file.
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@ -13943,7 +14104,7 @@ Small open economy RBC model with shocks to the growth trend, presented
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in @cite{Aguiar and Gopinath (2004)}.
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@item NK_baseline.mod
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Baseline New Keynesian Model estimated in @cite{Fernández-Villaverde (2010)}. It demonstrates how to use an explicit steady state file to update parameters and call a numerical solver.
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Baseline New Keynesian Model estimated in @cite{Fern??ndez-Villaverde (2010)}. It demonstrates how to use an explicit steady state file to update parameters and call a numerical solver.
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@end table
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@ -14128,7 +14289,7 @@ Plots the marginal prior density.
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Abramowitz, Milton and Irene A. Stegun (1964): ``Handbook of Mathematical Functions'', Courier Dover Publications
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@item
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Adjemian, Stéphane, Matthieu Darracq Parriès and Stéphane Moyen (2008): ``Towards a monetary policy evaluation framework'',
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Adjemian, St??phane, Matthieu Darracq Parri??s and St??phane Moyen (2008): ``Towards a monetary policy evaluation framework'',
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@i{European Central Bank Working Paper}, 942
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@item
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@ -14139,7 +14300,7 @@ Cycles: The Cycle is the Trend,'' @i{NBER Working Paper}, 10734
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Amisano, Gianni and Tristani, Oreste (2010): ``Euro area inflation persistence in an estimated nonlinear DSGE model'', @i{Journal of Economic Dynamics and Control}, 34(10), 1837--1858
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@item
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Andreasen, Martin M., Jesús Fernández-Villaverde, and Juan Rubio-Ramírez (2013): ``The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications,'' @i{NBER Working Paper}, 18983
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Andreasen, Martin M., Jes??s Fern??ndez-Villaverde, and Juan Rubio-Ram??rez (2013): ``The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications,'' @i{NBER Working Paper}, 18983
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@item
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Andrews, Donald W.K (1991): ``Heteroskedasticity and autocorrelation consistent covariance matrix estimation'',
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@ -14221,17 +14382,17 @@ Estimation of Dynamic Nonlinear Rational Expectation Models,''
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@i{Econometrica}, 51, 1169--1185
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@item
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Fernández-Villaverde, Jesús and Juan Rubio-Ramírez (2004): ``Comparing
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Fern??ndez-Villaverde, Jes??s and Juan Rubio-Ram??rez (2004): ``Comparing
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Dynamic Equilibrium Economies to Data: A Bayesian Approach,''
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@i{Journal of Econometrics}, 123, 153--187
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@item
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Fernández-Villaverde, Jesús and Juan Rubio-Ramírez (2005): ``Estimating
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Fern??ndez-Villaverde, Jes??s and Juan Rubio-Ram??rez (2005): ``Estimating
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Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood,''
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@i{Journal of Applied Econometrics}, 20, 891--910
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@item
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Fernández-Villaverde, Jesús (2010): ``The econometrics of DSGE models,''
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Fern??ndez-Villaverde, Jes??s (2010): ``The econometrics of DSGE models,''
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@i{SERIEs}, 1, 3--49
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@item
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@ -14326,9 +14487,9 @@ for local nonlinear optimization problems (version 1.1, Matlab, C, FORTRAN)'',
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University of Graz, Graz, Austria
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@item
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Laffargue, Jean-Pierre (1990): ``Résolution d'un modèle
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macroéconomique avec anticipations rationnelles'', @i{Annales
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d'Économie et Statistique}, 17, 97--119
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Laffargue, Jean-Pierre (1990): ``R??solution d'un mod??le
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macro??conomique avec anticipations rationnelles'', @i{Annales
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d'??conomie et Statistique}, 17, 97--119
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@item
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Liu, Jane and Mike West (2001): ``Combined parameter and state estimation in simulation-based filtering'', in @i{Sequential Monte Carlo Methods in Practice}, Eds. Doucet, Freitas and Gordon, Springer Verlag
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@ -14344,7 +14505,7 @@ to the solution and estimation of DSGE models''
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@item
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Murray, Lawrence M., Emlyn M. Jones and John Parslow (2013): ``On Disturbance State-Space Models and the Particle Marginal
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Metropolis-Hastings Sampler'', @i{SIAM/ASA Journal on Uncertainty Quantification}, 1, 494–521.
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Metropolis-Hastings Sampler'', @i{SIAM/ASA Journal on Uncertainty Quantification}, 1, 494???521.
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@item
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Pearlman, Joseph, David Currie, and Paul Levine (1986): ``Rational
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@ -14379,7 +14540,7 @@ Schorfheide, Frank (2000): ``Loss Function-based evaluation of DSGE
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models,'' @i{Journal of Applied Econometrics}, 15(6), 645--670
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@item
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Schmitt-Grohé, Stephanie and Martin Uríbe (2004): ``Solving Dynamic
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Schmitt-Groh??, Stephanie and Martin Ur??be (2004): ``Solving Dynamic
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General Equilibrium Models Using a Second-Order Approximation to the
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Policy Function,'' @i{Journal of Economic Dynamics and Control},
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28(4), 755--775
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@ -14408,7 +14569,7 @@ in @i{Computational Methods for the Study of Dynamic
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Economies}, Eds. Ramon Marimon and Andrew Scott, Oxford University Press, 30--61
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@item
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Villemot, Sébastien (2011): ``Solving rational expectations models at
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Villemot, S??bastien (2011): ``Solving rational expectations models at
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first order: what Dynare does,'' @i{Dynare Working Papers}, 2,
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CEPREMAP
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