From 06176caf64f8f574cce8aa0cf03f21fd4f31f731 Mon Sep 17 00:00:00 2001 From: Marco Ratto Date: Sun, 8 Jan 2017 23:03:50 +0100 Subject: [PATCH] manual updates --- doc/dynare.texi | 187 ++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 174 insertions(+), 13 deletions(-) diff --git a/doc/dynare.texi b/doc/dynare.texi index 0dc42dacf..6a3ee9ac3 100644 --- a/doc/dynare.texi +++ b/doc/dynare.texi @@ -7080,8 +7080,12 @@ graphs. See @code{colormap} in Matlab/Octave manual. @item nograph @xref{nograph}. Suppresses the display and creation only within the @code{shock_decomposition}-command -but does not affect other commands. +but does not affect other commands. See also @xref{plot_shock_decomposition} for plotting graphs. +@item init_state = @var(INTEGER) +@xref{init_state}. It can take values of 0 (=default) and 1. +If @code{init_state= 1}, the shock decomposition is computed conditional on the smoothed state variables +in period 1. @end table @vindex oo_.shock_decomposition @@ -7141,6 +7145,163 @@ shocks_decomposition(use_shock_groups=group1); @end deffn +@deffn Command realtime_shock_decomposition [@var{VARIABLE_NAME}]@dots{}; +@deffnx Command realtime_shock_decomposition (@var{OPTIONS}@dots{}) [@var{VARIABLE_NAME}]@dots{}; + +@descriptionhead + +This command computes the recursive historical shock decomposition for a given sample based on +the Kalman smoother, i.e. it decomposes the historical deviations of the endogenous +variables from their respective steady state values into the contribution coming +from the various shocks. The @code{variable_names} provided govern for which +variables the decomposition is plotted. + +Note that this command must come after either @code{estimation} (in case +of an estimated model) or @code{stoch_simul} (in case of a calibrated +model). + +@optionshead + +@table @code + +@item parameter_set = @var{PARAMETER_SET} +Specify the parameter set to use for running the smoother. The +@var{PARAMETER_SET} can take one of the following seven values: +@code{calibration}, @code{prior_mode}, @code{prior_mean}, +@code{posterior_mode}, @code{posterior_mean}, +@code{posterior_median}, @code{mle_mode}. Default value: @code{posterior_mean} if +Metropolis has been run, @code{mle_mode} if MLE has been run. + +@item datafile = @var{FILENAME} +@xref{datafile}. Useful when computing the shock decomposition on a +calibrated model. + +@item first_obs = @var{INTEGER} +@xref{first_obs}. + +@item nobs = @var{INTEGER} +@xref{nobs}. + +@item use_shock_groups [= @var{SHOCK_GROUPS_NAME}] +@anchor{use_shock_groups}. Uses groups of shocks instead of individual shocks in +the decomposition. Groups of shocks are defined in @xref{shock_groups} block. + +@item colormap = @var{COLORMAP_NAME} +@anchor{colormap}. Controls the colormap used for the shocks decomposition +graphs. See @code{colormap} in Matlab/Octave manual. + +@item nograph +@anchor{nograph}. Only shock decompositions are computed and stored in @code{oo_.shock_decomposition}, +but no plot is made (@xref{plot_shock_decomposition}). + +@item presample +@anchor{presample}. First data point from which recursive realtime shock decompositions are computed, i.e. for T=(presample:nobs). + +@item forecast +@anchor{forecast}. Compute shock decompositions up to T+k periods, i.e. get shock contributions to k-step ahead forecasts. +@end table + +@vindex oo_.realtime_shock_decomposition +The results of realtime decompositions are stored in the field @code{oo_.realtime_shock_decomposition}, which is a structure. +Field @code{pool} stores the pooled decomposition @xref{plot_shock_decomposition}. +Fields @code{time_*} store the vintages of realtime shock decompositions. + +@vindex oo_.conditional_shock_decomposition +The results of realtime conditional decompositions are stored in the field @code{oo_.conditional_shock_decomposition}, which is a structure. +Field @code{pool} stores the pooled decomposition @xref{plot_shock_decomposition}. +Fields @code{time_*} store the vintages of conditional forecast shock decompositions. + +@vindex oo_.realtime_forecast_shock_decomposition +The results of realtime forecast decompositions are stored in the field @code{oo_.realtime_forecast_shock_decomposition}, which is a structure. +Field @code{pool} stores the pooled decomposition @xref{plot_shock_decomposition}. +Fields @code{time_*} store the vintages of conditional forecast shock decompositions. + +@end deffn + +@deffn Command plot_shock_decomposition [@var{VARIABLE_NAME}]@dots{}; +@deffnx Command plot_shock_decomposition (@var{OPTIONS}@dots{}) [@var{VARIABLE_NAME}]@dots{}; + +@descriptionhead + +This command plots the historical shock decomposition already computed by command +@code{shock_decomposition}. +The @code{variable_names} provided govern for which +variables the decomposition is plotted. + +Note that this command must come after @code{shock_decomposition} or @code{realtime_shock_decomposition}. + +@optionshead + +@table @code + +@item use_shock_groups [= @var{SHOCK_GROUPS_NAME}] +@anchor{use_shock_groups}. Uses groups of shocks instead of individual shocks in +the decomposition. Groups of shocks are defined in @xref{shock_groups} block. + +@item colormap = @var{COLORMAP_NAME} +@anchor{colormap}. Controls the colormap used for the shocks decomposition +graphs. See @code{colormap} in Matlab/Octave manual. + +@item nodisplay +@xref{nodisplay}. + +@item graph_format = (@{GRAPH_FORMAT_LIST}) +@xref{graph_format}. + +@item detailed +@anchor{detailed}. Plots shock contributions using subplots, one per shock (or group of shocks). + +@item interactive +@anchor{interactive}. Under MATLAB, add uimenu's for detailed group plots. + +@item screen +@anchor{screen}. For large models [i.e. for models with more than 16 shocks], plots only the shocks +that have the largest historical contribution for chosen selected @code{variable_names}. +Historical contribution is ranked by the mean absolute value of all historical contributions. + +@item steadystate = @var{INTEGER} +@anchor{steadystate}. 0=default. +If =1, the the y-axis value of the zero line in the shock decomposition plot is translated to the steady state level. + +@item type = @var{TYPE_NAME} +@anchor{type}. For quarterly data, the @var{TYPE_NAME} can take one of the following values: +@code{qoq} for quarter-on-quarter plots, +@code{yoy} for year-on-year plots of growth rates, +@code{aoa} for annualized variables, i.e. the value in the last quarter for each year is plotted. +Default value: @code{empty}, i.e. standard period-on-period plots [qoq for quarterly data]. + +@item fig_name = @var{FIG_NAME} +@anchor{fig_name}. Specifies a user-defined keyword to be appended to the default figure name set by @code{plot_shock_decomposition}. +This can avoid to overwrite plots in case of sequential calls to @code{plot_shock_decomposition}. + +@item write_xls +@anchor{write_xls}. Saves shock decompotions to excel. + +@item realtime = @var{INTEGER} +@anchor{realtime}. Which kind of shock decomposition to plot. @var{INTEGER} can take following values: +@code{0}: standard shock decomposition [DEFAULT] +@code{1}: realtime shock decomposition: for T=1:nobs, realtime shock decompositions of Y(T|T) +@code{2}: conditional shock decomposition: for T=1:nobs, realtime shock decompositions of Y(T|T) CONDITIONAL on Y(T|T-1) +@code{3}: forecast shock decomposition: for T=1:nobs, realtime shock decompositions of Y(Y|T-1) + +@item vintage = @var{INTEGER} +@anchor{vintage}. If @code{realtime}>0. @var{INTEGER} can take following values: + +@code{0}: plots 1step pooled shock decompositions [DEFAULT] +@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) +@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) +[i.e. decomposition of 1-step filter updates of each vintage T] +@code{realtime=3}: pooled forecast shock decomposition. For T=1:nobs, realtime 1-step ahead shock decomposition of Y(T|T-1) +[i.e. decomposition of shock contributions to 1-step ahead forecasts of each vintage T] + +@code{>0}: plots shock decompositions for vintage T=@code{vintage} under the following scenarios +@code{realtime=1}: the full vintage shock decomposition Y(1:T|T) +@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}). +@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}). +@end table + +@end deffn + @deffn Command unit_root_vars @var{VARIABLE_NAME}@dots{}; 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. @@ -13943,7 +14104,7 @@ Small open economy RBC model with shocks to the growth trend, presented in @cite{Aguiar and Gopinath (2004)}. @item NK_baseline.mod -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. +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. @end table @@ -14128,7 +14289,7 @@ Plots the marginal prior density. Abramowitz, Milton and Irene A. Stegun (1964): ``Handbook of Mathematical Functions'', Courier Dover Publications @item -Adjemian, Stéphane, Matthieu Darracq Parriès and Stéphane Moyen (2008): ``Towards a monetary policy evaluation framework'', +Adjemian, St??phane, Matthieu Darracq Parri??s and St??phane Moyen (2008): ``Towards a monetary policy evaluation framework'', @i{European Central Bank Working Paper}, 942 @item @@ -14139,7 +14300,7 @@ Cycles: The Cycle is the Trend,'' @i{NBER Working Paper}, 10734 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 @item -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 +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 @item Andrews, Donald W.K (1991): ``Heteroskedasticity and autocorrelation consistent covariance matrix estimation'', @@ -14221,17 +14382,17 @@ Estimation of Dynamic Nonlinear Rational Expectation Models,'' @i{Econometrica}, 51, 1169--1185 @item -Fernández-Villaverde, Jesús and Juan Rubio-Ramírez (2004): ``Comparing +Fern??ndez-Villaverde, Jes??s and Juan Rubio-Ram??rez (2004): ``Comparing Dynamic Equilibrium Economies to Data: A Bayesian Approach,'' @i{Journal of Econometrics}, 123, 153--187 @item -Fernández-Villaverde, Jesús and Juan Rubio-Ramírez (2005): ``Estimating +Fern??ndez-Villaverde, Jes??s and Juan Rubio-Ram??rez (2005): ``Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood,'' @i{Journal of Applied Econometrics}, 20, 891--910 @item -Fernández-Villaverde, Jesús (2010): ``The econometrics of DSGE models,'' +Fern??ndez-Villaverde, Jes??s (2010): ``The econometrics of DSGE models,'' @i{SERIEs}, 1, 3--49 @item @@ -14326,9 +14487,9 @@ for local nonlinear optimization problems (version 1.1, Matlab, C, FORTRAN)'', University of Graz, Graz, Austria @item -Laffargue, Jean-Pierre (1990): ``Résolution d'un modèle -macroéconomique avec anticipations rationnelles'', @i{Annales -d'Économie et Statistique}, 17, 97--119 +Laffargue, Jean-Pierre (1990): ``R??solution d'un mod??le +macro??conomique avec anticipations rationnelles'', @i{Annales +d'??conomie et Statistique}, 17, 97--119 @item 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 @@ -14344,7 +14505,7 @@ to the solution and estimation of DSGE models'' @item Murray, Lawrence M., Emlyn M. Jones and John Parslow (2013): ``On Disturbance State-Space Models and the Particle Marginal -Metropolis-Hastings Sampler'', @i{SIAM/ASA Journal on Uncertainty Quantification}, 1, 494–521. +Metropolis-Hastings Sampler'', @i{SIAM/ASA Journal on Uncertainty Quantification}, 1, 494???521. @item Pearlman, Joseph, David Currie, and Paul Levine (1986): ``Rational @@ -14379,7 +14540,7 @@ Schorfheide, Frank (2000): ``Loss Function-based evaluation of DSGE models,'' @i{Journal of Applied Econometrics}, 15(6), 645--670 @item -Schmitt-Grohé, Stephanie and Martin Uríbe (2004): ``Solving Dynamic +Schmitt-Groh??, Stephanie and Martin Ur??be (2004): ``Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function,'' @i{Journal of Economic Dynamics and Control}, 28(4), 755--775 @@ -14408,7 +14569,7 @@ 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 +Villemot, S??bastien (2011): ``Solving rational expectations models at first order: what Dynare does,'' @i{Dynare Working Papers}, 2, CEPREMAP