Commit b257c139 authored by Stéphane Adjemian's avatar Stéphane Adjemian
Browse files

Added documentation for the (stochastic) extended path simulation approach.

parent 8c1b5864
......@@ -1514,7 +1514,7 @@ at the kink is bogus (as explained in the respective documentations of
these functions and operators).
Note that @code{extended_path} is not affected by this problem,
because it uses a global approximation method, not a local one.
because it does not rely on a local approximation of the model.
@node Parameter initialization
@section Parameter initialization
......@@ -3578,15 +3578,19 @@ in the structure @code{oo_.dr} which is described below.
@descriptionhead
@code{extended_path} solves a stochastic (@i{i.e.} rational
expectations) model, using the @emph{extended path} method presented
by @cite{Fair and Taylor (1983)}.
@code{extended_path} solves a stochastic (@i{i.e.} rational
expectations) model, using the @emph{extended path} method presented by
@cite{Fair and Taylor (1983)}. Time series for the endogenous variables
are generated by assuming that the agents believe that there will no
more shocks in the following periods.
This function first computes a random path for the exogenous variables
(stored in @code{oo_.exo_simul}, @pxref{oo_.exo_simul}) and then
computes the corresponding path for endogenous variables, taking the
steady state as starting point. The result of the simulation is stored
in @code{oo_.endo_simul} (@pxref{oo_.endo_simul}).
This function first computes a random path for the exogenous variables
(stored in @code{oo_.exo_simul}, @pxref{oo_.exo_simul}) and then
computes the corresponding path for endogenous variables, taking the
steady state as starting point. The result of the simulation is stored
in @code{oo_.endo_simul} (@pxref{oo_.endo_simul}). Note that this
simulation approach does not solve for the policy and transition
equations but for paths for the endogenous variables.
@optionshead
......@@ -3597,14 +3601,15 @@ The number of periods for which the simulation is to be computed. No
default value, mandatory option.
@item solver_periods = @var{INTEGER}
The number of periods used to compute the approximate solution
at every iteration of the algorithm. Default: @code{200}.
The number of periods used to compute the solution of the perfect
foresight at every iteration of the algorithm. Default: @code{200}.
@item order = @var{INTEGER}
... Default: @code{0}.
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
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}.
@item hybrid
...
Use the constant of the second order perturbation reduced form to correct the paths generated by the (stochastic) extended path algorithm.
@end table
......
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