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Dóra Kocsis
dynare
Commits
32054371
Commit
32054371
authored
Jan 28, 2012
by
MichelJuillard
Browse files
new algorithm for deterministic simulations. Not yet integrated into
Dynare (no function calls it).
parent
6328a44f
Changes
1
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Inline
Side-by-side
matlab/sim1a.m
0 → 100644
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32054371
function
sim1
% function sim1
% performs deterministic simulations with lead or lag on one period
%
% INPUTS
% ...
% OUTPUTS
% ...
% ALGORITHM
% Laffargue, Boucekkine, Juillard (LBJ)
% see Juillard (1996) Dynare: A program for the resolution and
% simulation of dynamic models with forward variables through the use
% of a relaxation algorithm. CEPREMAP. Couverture Orange. 9602.
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 1996-2010 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global
M_
options_
oo_
lead_lag_incidence
=
M_
.
lead_lag_incidence
;
ny
=
M_
.
endo_nbr
;
max_lag
=
M_
.
maximum_endo_lag
;
nyp
=
nnz
(
lead_lag_incidence
(
1
,:))
;
iyp
=
find
(
lead_lag_incidence
(
1
,:)
>
0
)
;
ny0
=
nnz
(
lead_lag_incidence
(
2
,:))
;
iy0
=
find
(
lead_lag_incidence
(
2
,:)
>
0
)
;
nyf
=
nnz
(
lead_lag_incidence
(
3
,:))
;
iyf
=
find
(
lead_lag_incidence
(
3
,:)
>
0
)
;
nd
=
nyp
+
ny0
+
nyf
;
nrc
=
nyf
+
1
;
isp
=
[
1
:
nyp
]
;
is
=
[
nyp
+
1
:
ny
+
nyp
]
;
isf
=
iyf
+
nyp
;
isf1
=
[
nyp
+
ny
+
1
:
nyf
+
nyp
+
ny
+
1
]
;
stop
=
0
;
iz
=
[
1
:
ny
+
nyp
+
nyf
];
periods
=
options_
.
periods
steady_state
=
oo_
.
steady_state
;
params
=
M_
.
params
;
endo_simul
=
oo_
.
endo_simul
;
exo_simul
=
oo_
.
exo_simul
;
i_cols_1
=
nonzeros
(
lead_lag_incidence
(
2
:
3
,:)
'
);
i_cols_A1
=
find
(
lead_lag_incidence
(
2
:
3
,:)
'
);
i_cols_T
=
nonzeros
(
lead_lag_incidence
(
1
:
2
,:)
'
);
i_cols_j
=
1
:
nd
;
i_upd
=
ny
+
(
1
:
periods
*
ny
);
Y
=
endo_simul
(:);
disp
([
'-----------------------------------------------------'
])
;
disp
([
'MODEL SIMULATION :'
])
;
fprintf
(
'\n'
)
;
model_dynamic
=
str2func
([
M_
.
fname
,
'_dynamic'
]);
z
=
Y
(
find
(
lead_lag_incidence
'
));
[
d1
,
jacobian
]
=
model_dynamic
(
z
,
oo_
.
exo_simul
,
params
,
...
steady_state
,
2
);
A
=
sparse
([],[],[],
periods
*
ny
,
periods
*
ny
,
periods
*
nnz
(
jacobian
));
res
=
zeros
(
periods
*
ny
,
1
);
h1
=
clock
;
for
iter
=
1
:
options_
.
maxit_
h2
=
clock
;
i_rows
=
1
:
ny
;
i_cols
=
find
(
lead_lag_incidence
'
);
i_cols_A
=
i_cols
;
for
it
=
2
:(
periods
+
1
)
[
d1
,
jacobian
]
=
model_dynamic
(
Y
(
i_cols
),
exo_simul
,
params
,
...
steady_state
,
it
);
if
it
==
2
A
(
i_rows
,
i_cols_A1
)
=
jacobian
(:,
i_cols_1
);
elseif
it
==
periods
+
1
A
(
i_rows
,
i_cols_A
(
i_cols_T
))
=
jacobian
(:,
i_cols_T
);
else
A
(
i_rows
,
i_cols_A
)
=
jacobian
(:,
i_cols_j
);
end
res
(
i_rows
)
=
d1
;
i_rows
=
i_rows
+
ny
;
i_cols
=
i_cols
+
ny
;
if
it
>
2
i_cols_A
=
i_cols_A
+
ny
;
end
end
err
=
max
(
abs
(
res
));
if
err
<
options_
.
dynatol
.
f
stop
=
1
;
fprintf
(
'\n'
)
;
disp
([
' Total time of simulation :'
num2str
(
etime
(
clock
,
h1
))])
;
fprintf
(
'\n'
)
;
disp
([
' Convergency obtained.'
])
;
fprintf
(
'\n'
)
;
oo_
.
deterministic_simulation
.
status
=
1
;
% Convergency obtained.
oo_
.
deterministic_simulation
.
error
=
err
;
oo_
.
deterministic_simulation
.
iterations
=
iter
;
oo_
.
endo_simul
=
reshape
(
Y
,
ny
,
periods
+
2
);
break
end
dy
=
-
A
\
res
;
Y
(
i_upd
)
=
Y
(
i_upd
)
+
dy
;
end
if
~
stop
fprintf
(
'\n'
)
;
disp
([
' Total time of simulation :'
num2str
(
etime
(
clock
,
h1
))])
;
fprintf
(
'\n'
)
;
disp
([
'WARNING : maximum number of iterations is reached (modify options_.maxit_).'
])
;
fprintf
(
'\n'
)
;
oo_
.
deterministic_simulation
.
status
=
0
;
% more iterations are needed.
oo_
.
deterministic_simulation
.
error
=
err
;
oo_
.
deterministic_simulation
.
errors
=
c
/
abs
(
err
);
oo_
.
deterministic_simulation
.
iterations
=
options_
.
maxit_
;
end
disp
([
'-----------------------------------------------------'
])
;
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