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Merge pull request #625 from JohannesPfeifer/check_fix 

Check fix
time-shift
Sébastien Villemot 2014-02-28 14:22:41 +01:00
parent 70b5cc88b2 cf1cec341f
commit b759318be9
2 changed files with 24 additions and 8 deletions
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30
doc/dynare.texi
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@ -3251,13 +3251,29 @@ models, this return to equilibrium is only an asymptotic phenomenon,
which one must approximate by an horizon of simulation far enough in
the future. Another exercise for which Dynare is well suited is to
study the transition path to a new equilibrium following a permanent
shock. For deterministic simulations, Dynare uses a Newton-type
algorithm, first proposed by @cite{Laffargue (1990)} and
@cite{Boucekkine (1995)}, instead of a first order technique like the
one proposed by @cite{Fair and Taylor (1983)}, and used in earlier
generation simulation programs. We believe this approach to be in
general both faster and more robust. The details of the algorithm can
be found in @cite{Juillard (1996)}.
shock. For deterministic simulations, the numerical problem consists of solving
a nonlinar system of simultaneous equations in @code{n} endogenous
variables in @code{T} periods. Dynare offers several algorithms for
solving this problem, which can be chosen via the
@code{stack_solve_algo}-option. By default (@code{stack_solve_algo=0}),
Dynare uses a Newton-type method to solve the simultaneous equation
system. Because the resulting Jacobian is in the order of @code{n} by
@code{T} and hence will be very large for long simulations with many
variables, Dynare makes use of the sparse matrix capacities of
MATLAB/Octave. A slower but potentially less memory consuming alternative
(@code{stack_solve_algo=6}) is based on a Newton-type algorithm first
proposed by @cite{Laffargue (1990)} and @cite{Boucekkine (1995)}, which
uses relaxation techniques. Thereby, the algorithm avoids ever storing
the full Jacobian. The details of the algorithm can be found in
@cite{Juillard (1996)}. The third type of algorithms makes use of block
decomposition techniques (divide-and-conquer methods) that exploit the
structure of the model. The principle is to identify recursive and
simultaneous blocks in the model structure and use this information to
aid the solution process. These solution algorithms can provide a
significant speed-up on large models.
@deffn Command simul ;
@deffnx Command simul (@var{OPTIONS}@dots{});

2
matlab/check.m
View File

@ -73,7 +73,7 @@ oo.dr=set_state_space(oo.dr,M,options);
[dr,info,M,options,oo] = resol(1,M,options,oo);
if info(1) ~= 0 && info(1) ~= 3 && info(1) ~= 4
print_info(info, options.noprint, options);
print_info(info, 0, options);
end
eigenvalues_ = dr.eigval;