Added documentation for the (stochastic) extended path simulation approach.
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@ -1514,7 +1514,7 @@ at the kink is bogus (as explained in the respective documentations of
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these functions and operators).
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Note that @code{extended_path} is not affected by this problem,
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because it uses a global approximation method, not a local one.
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because it does not rely on a local approximation of the model.
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@node Parameter initialization
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@section Parameter initialization
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@ -3578,15 +3578,19 @@ in the structure @code{oo_.dr} which is described below.
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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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@code{extended_path} solves a stochastic (@i{i.e.} rational
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expectations) model, using the @emph{extended path} method presented by
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@cite{Fair and Taylor (1983)}. Time series for the endogenous variables
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are generated by assuming that the agents believe that there will no
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more shocks in the following periods.
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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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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}). Note that this
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simulation approach does not solve for the policy and transition
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equations but for paths for the endogenous variables.
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@optionshead
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@ -3597,14 +3601,15 @@ 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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The number of periods used to compute the solution of the perfect
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foresight at every iteration of the algorithm. Default: @code{200}.
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@item order = @var{INTEGER}
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... Default: @code{0}.
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If @code{order} is greater than 0 Dynare uses a gaussian quadrature to take into account the effects of future uncertainty. If @code{order}=@var{S} then the time series for the endogenous variables
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are generated by assuming that the agents believe that there will no more shocks after period @var{t+S}. This is an experimental feature and can be quite slow. Default: @code{0}.
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@item hybrid
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...
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Use the constant of the second order perturbation reduced form to correct the paths generated by the (stochastic) extended path algorithm.
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@end table
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