Document extended_path and oo_.exo_simul in ref manual
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@ -2875,7 +2875,8 @@ The simulated endogenous variables are available in global matrix
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@defvr {MATLAB/Octave variable} oo_.endo_simul
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This variable stores the result of a deterministic simulation
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(computed by @code{simul}) or of a stochastic simulation (computed by
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@code{stoch_simul} with the @code{periods} option).
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@code{stoch_simul} with the @code{periods} option or by
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@code{extended_path}).
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The variables are arranged row by row, in order of declaration (as in
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@code{M_.endo_names}). Note that this variable also contains initial
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@ -2883,16 +2884,38 @@ and terminal conditions, so it has more columns than the value of
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@code{periods} option.
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@end defvr
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@anchor{oo_.exo_simul}
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@defvr {MATLAB/Octave variable} oo_.exo_simul
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This variable stores the path of exogenous variables during a
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simulation (computed by @code{simul}, @code{stoch_simul} or
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@code{extended_path}).
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The variables are arranged in columns, in order of declaration (as in
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@code{M_.endo_names}). Periods are in rows. Note that this convention
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regarding columns and rows is the opposite of the convention for
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@code{oo_.endo_simul}!
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@end defvr
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@node Stochastic solution and simulation
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@section Stochastic solution and simulation
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In a stochastic context, Dynare computes one or several simulations
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corresponding to a random draw of the shocks. Dynare uses a Taylor
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corresponding to a random draw of the shocks.
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The main algorithm for solving stochastic models relies on a Taylor
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approximation, up to third order, of the expectation functions (see
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@cite{Judd (1996)}, @cite{Collard and Juillard (2001a)}, @cite{Collard
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and Juillard (2001b)}, and @cite{Schmitt-Grohé and Uríbe (2004)}). The
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details of the Dynare implementation of the first order solution are
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given in @cite{Villemot (2011)}.
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given in @cite{Villemot (2011)}. Such a solution is computed using
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the @code{stoch_simul} command.
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As an alternative, it is possible to compute a simulation to a
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stochastic model using the @emph{extended path} method presented by
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@cite{Fair and Taylor (1983)}. This method is especially useful when
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there are strong nonlinearities or binding constraints. Such a
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solution is computed using the @code{extended_path} command.
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@menu
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* Computing the stochastic solution::
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@ -3052,7 +3075,9 @@ periods to use in the simulations. Values of the @code{initval} block,
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possibly recomputed by @code{steady}, will be used as starting point
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for the simulation. The simulated endogenous variables are made
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available to the user in a vector for each variable and in the global
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matrix @code{oo_.endo_simul} (@pxref{oo_.endo_simul}). Default: @code{0}.
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matrix @code{oo_.endo_simul} (@pxref{oo_.endo_simul}). The simulated
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exogenous variables are made available in @code{oo_.exo_simul}
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(@pxref{oo_.exo_simul}). Default: @code{0}.
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@item qz_criterium = @var{DOUBLE}
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Value used to split stable from unstable eigenvalues in reordering the
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@ -3250,6 +3275,37 @@ variables of the model as function of the previous state of the model and
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shocks oberved at the beginning of the period. The decision rules are stored
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in the structure @code{oo_.dr} which is described below.
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@deffn Command extended_path ;
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@deffnx Command extended_path (@var{OPTIONS}@dots{}) ;
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@descriptionhead
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@code{extended_path} solves a stochastic (@i{i.e.} rational
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expectations) model, using the @emph{extended path} method presented
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by @cite{Fair and Taylor (1983)}.
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This function first computes a random path for the exogenous variables
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(stored in @code{oo_.exo_simul}, @pxref{oo_.exo_simul}) and then
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computes the corresponding path for endogenous variables, taking the
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steady state as starting point. The result of the simulation is stored
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in @code{oo_.endo_simul} (@pxref{oo_.endo_simul}).
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@optionshead
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@table @code
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@item periods = @var{INTEGER}
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The number of periods for which the simulation is to be computed. No
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default value, mandatory option.
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@item solver_periods = @var{INTEGER}
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The number of periods used to compute the approximate solution
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at every iteration of the algorithm. Default: @code{200}.
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@end table
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@end deffn
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@node Typology and ordering of variables
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@subsection Typology and ordering of variables
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