adding Ed Herbst fast implementation of the Kalman filter and test
cases with timing. Still needs preprocessor interface (option) and documentation.time-shift
Michel Juillard
parent
78f8f04598
commit
bbd95b3a60
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function [LIK, LIKK, a, P] = kalman_filter_fast(Y,start,last,a,P,kalman_tol,riccati_tol,presample,T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods,analytic_derivation,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P)
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% [LIK, LIKK, a, P] =
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% kalman_filter_fast(Y,start,last,a,P,kalman_tol,riccati_tol,presample,T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods,analytic_derivation,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P)
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% computes the likelihood of a stationnary state space model using Ed
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% Herbst fast implementation of the Kalman filter.
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%@info:
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%! @deftypefn {Function File} {[@var{LIK},@var{likk},@var{a},@var{P} ] =} kalman_filter_fast(@var{Y}, @var{start}, @var{last}, @var{a}, @var{P}, @var{kalman_tol}, @var{riccati_tol},@var{presample},@var{T},@var{Q},@var{R},@var{H},@var{Z},@var{mm},@var{pp},@var{rr},@var{Zflag},@var{diffuse_periods})
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%! @anchor{kalman_filter}
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%! @sp 1
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%! Computes the likelihood of a stationary state space model, given initial condition for the states (mean and variance).
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%! @sp 2
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%! @strong{Inputs}
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%! @sp 1
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%! @table @ @var
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%! @item Y
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%! Matrix (@var{pp}*T) of doubles, data.
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%! @item start
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%! Integer scalar, first period.
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%! @item last
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%! Integer scalar, last period (@var{last}-@var{first} has to be inferior to T).
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%! @item a
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%! Vector (@var{mm}*1) of doubles, initial mean of the state vector.
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%! @item P
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%! Matrix (@var{mm}*@var{mm}) of doubles, initial covariance matrix of the state vector.
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%! @item kalman_tol
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%! Double scalar, tolerance parameter (rcond, inversibility of the covariance matrix of the prediction errors).
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%! @item riccati_tol
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%! Double scalar, tolerance parameter (iteration over the Riccati equation).
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%! @item presample
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%! Integer scalar, presampling if strictly positive (number of initial iterations to be discarded when evaluating the likelihood).
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%! @item T
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%! Matrix (@var{mm}*@var{mm}) of doubles, transition matrix of the state equation.
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%! @item Q
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%! Matrix (@var{rr}*@var{rr}) of doubles, covariance matrix of the structural innovations (noise in the state equation).
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%! @item R
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%! Matrix (@var{mm}*@var{rr}) of doubles, second matrix of the state equation relating the structural innovations to the state variables.
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%! @item H
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%! Matrix (@var{pp}*@var{pp}) of doubles, covariance matrix of the measurement errors (if no measurement errors set H as a zero scalar).
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%! @item Z
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%! Matrix (@var{pp}*@var{mm}) of doubles or vector of integers, matrix relating the states to the observed variables or vector of indices (depending on the value of @var{Zflag}).
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%! @item mm
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%! Integer scalar, number of state variables.
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%! @item pp
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%! Integer scalar, number of observed variables.
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%! @item rr
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%! Integer scalar, number of structural innovations.
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%! @item Zflag
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%! Integer scalar, equal to 0 if Z is a vector of indices targeting the obseved variables in the state vector, equal to 1 if Z is a @var{pp}*@var{mm} matrix.
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%! @item diffuse_periods
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%! Integer scalar, number of diffuse filter periods in the initialization step.
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%! @end table
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%! @sp 2
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%! @strong{Outputs}
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%! @sp 1
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%! @table @ @var
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%! @item LIK
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%! Double scalar, value of (minus) the likelihood.
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%! @item likk
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%! Column vector of doubles, values of the density of each observation.
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%! @item a
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%! Vector (@var{mm}*1) of doubles, mean of the state vector at the end of the (sub)sample.
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%! @item P
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%! Matrix (@var{mm}*@var{mm}) of doubles, covariance of the state vector at the end of the (sub)sample.
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%! @end table
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%! @sp 2
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%! @strong{This function is called by:}
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%! @sp 1
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%! @ref{DsgeLikelihood}
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%! @sp 2
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%! @strong{This function calls:}
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%! @sp 1
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%! @ref{kalman_filter_ss}
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%! @end deftypefn
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%@eod:
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% Copyright (C) 2004-2013 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% Set defaults.
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if nargin<17
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Zflag = 0;
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end
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if nargin<18
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diffuse_periods = 0;
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end
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if nargin<19
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analytic_derivation = 0;
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end
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if isempty(Zflag)
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Zflag = 0;
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end
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if isempty(diffuse_periods)
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diffuse_periods = 0;
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end
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% Get sample size.
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smpl = last-start+1;
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% Initialize some variables.
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dF = 1;
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QQ = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
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t = start; % Initialization of the time index.
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likk = zeros(smpl,1); % Initialization of the vector gathering the densities.
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LIK = Inf; % Default value of the log likelihood.
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oldK = Inf;
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notsteady = 1;
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F_singular = 1;
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asy_hess=0;
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DLIK=[];
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Hess=[];
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LIKK=[];
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if Zflag
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K = T*P*Z';
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F = Z*P*Z' + H;
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else
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K = T*P(:,Z);
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F = P(Z,Z) + H;
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end
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W = K;
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iF = inv(F);
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Kg = K*iF;
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M = -iF;
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while notsteady && t<=last
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s = t-start+1;
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if Zflag
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v = Y(:,t)-Z*a;
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else
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v = Y(:,t)-a(Z);
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end
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if rcond(F) < kalman_tol
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if ~all(abs(F(:))<kalman_tol)
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return
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else
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a = T*a;
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P = T*P*transpose(T)+QQ;
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end
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else
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F_singular = 0;
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dF = det(F);
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likk(s) = log(dF)+transpose(v)*iF*v;
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a = T*a+Kg*v;
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if Zflag
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ZWM = Z*W*M;
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ZWMWp = ZWM*W';
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M = M + ZWM'*iF*ZWM;
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F = F + ZWMWp*Z';
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iF = inv(F);
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K = K + T*ZWMWp';
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Kg = K*iF;
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W = (T - Kg*Z)*W;
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else
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ZWM = W(Z,:)*M;
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ZWMWp = ZWM*W';
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M = M + ZWM'*iF*ZWM;
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F = F + ZWMWp(:,Z);
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iF = inv(F);
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K = K + T*ZWMWp';
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Kg = K*iF;
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W = T*W - Kg*W(Z,:);
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end
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% notsteady = max(abs(K(:)-oldK))>riccati_tol;
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oldK = K(:);
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end
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t = t+1;
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end
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if F_singular
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error('The variance of the forecast error remains singular until the end of the sample')
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end
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% Add observation's densities constants and divide by two.
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likk(1:s) = .5*(likk(1:s) + pp*log(2*pi));
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if analytic_derivation,
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DLIK = DLIK/2;
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dlikk = dlikk/2;
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if analytic_derivation==2 || asy_hess,
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if asy_hess==0,
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Hess = Hess + tril(Hess,-1)';
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end
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Hess = -Hess/2;
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end
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end
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% Call steady state Kalman filter if needed.
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if t <= last
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if analytic_derivation,
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if analytic_derivation==2,
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[tmp, tmp2] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
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analytic_derivation,Da,DT,DYss,D2a,D2T,D2Yss);
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else
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[tmp, tmp2] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag, ...
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analytic_derivation,Da,DT,DYss,asy_hess);
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end
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likk(s+1:end)=tmp2{1};
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dlikk(s+1:end,:)=tmp2{2};
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DLIK = DLIK + tmp{2};
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if analytic_derivation==2 || asy_hess,
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Hess = Hess + tmp{3};
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end
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else
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[tmp, likk(s+1:end)] = kalman_filter_ss(Y,t,last,a,T,K,iF,dF,Z,pp,Zflag);
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end
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end
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% Compute minus the log-likelihood.
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if presample>diffuse_periods,
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LIK = sum(likk(1+(presample-diffuse_periods):end));
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else
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LIK = sum(likk);
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end
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if analytic_derivation,
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if analytic_derivation==2 || asy_hess,
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LIK={LIK, DLIK, Hess};
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else
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LIK={LIK, DLIK};
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end
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LIKK={likk, dlikk};
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else
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LIKK=likk;
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end
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@ -0,0 +1,84 @@
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// See fs2000.mod in the examples/ directory for details on the model
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var m P c e W R k d n l gy_obs gp_obs y dA;
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varexo e_a e_m;
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parameters alp bet gam mst rho psi del;
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alp = 0.33;
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bet = 0.99;
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gam = 0.003;
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mst = 1.011;
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rho = 0.7;
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psi = 0.787;
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del = 0.02;
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model;
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dA = exp(gam+e_a);
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log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
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-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
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W = l/n;
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-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
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R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
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1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
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c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
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P*c = m;
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m-1+d = l;
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e = exp(e_a);
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y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
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gy_obs = dA*y/y(-1);
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gp_obs = (P/P(-1))*m(-1)/dA;
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end;
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steady_state_model;
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dA = exp(gam);
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e = 1;
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gst = 1/dA;
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m = mst;
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khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
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xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
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nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
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n = xist/(nust+xist);
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P = xist + nust;
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k = khst*n;
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l = psi*mst*n/( (1-psi)*(1-n) );
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c = mst/P;
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d = l - mst + 1;
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y = k^alp*n^(1-alp)*gst^alp;
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R = mst/bet;
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W = l/n;
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gp_obs = mst/dA;
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gy_obs = dA;
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end;
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shocks;
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var e_a; stderr 0.014;
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var e_m; stderr 0.005;
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end;
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steady;
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check;
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estimated_params;
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alp, beta_pdf, 0.356, 0.02;
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bet, beta_pdf, 0.993, 0.002;
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gam, normal_pdf, 0.0085, 0.003;
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mst, normal_pdf, 1.0002, 0.007;
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rho, beta_pdf, 0.129, 0.223;
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psi, beta_pdf, 0.65, 0.05;
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del, beta_pdf, 0.01, 0.005;
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stderr e_a, inv_gamma_pdf, 0.035449, inf;
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stderr e_m, inv_gamma_pdf, 0.008862, inf;
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end;
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varobs gp_obs gy_obs;
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options_.solve_tolf = 1e-12;
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// this disable the switch to steady state filter
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options_.riccati_tol = 0;
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estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8,plot_priors=0,mode_compute=0);
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@ -0,0 +1,82 @@
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// See fs2000.mod in the examples/ directory for details on the model
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var m P c e W R k d n l gy_obs gp_obs y dA;
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varexo e_a e_m;
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parameters alp bet gam mst rho psi del;
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alp = 0.33;
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bet = 0.99;
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gam = 0.003;
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mst = 1.011;
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rho = 0.7;
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psi = 0.787;
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del = 0.02;
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model;
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dA = exp(gam+e_a);
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log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
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-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
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W = l/n;
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-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
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R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
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1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
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c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
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P*c = m;
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m-1+d = l;
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e = exp(e_a);
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y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
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gy_obs = dA*y/y(-1);
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gp_obs = (P/P(-1))*m(-1)/dA;
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end;
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steady_state_model;
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dA = exp(gam);
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e = 1;
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gst = 1/dA;
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m = mst;
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khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
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xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
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nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
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n = xist/(nust+xist);
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P = xist + nust;
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k = khst*n;
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l = psi*mst*n/( (1-psi)*(1-n) );
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c = mst/P;
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d = l - mst + 1;
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y = k^alp*n^(1-alp)*gst^alp;
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R = mst/bet;
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W = l/n;
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gp_obs = mst/dA;
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gy_obs = dA;
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end;
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shocks;
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var e_a; stderr 0.014;
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var e_m; stderr 0.005;
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end;
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steady;
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check;
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estimated_params;
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alp, beta_pdf, 0.356, 0.02;
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bet, beta_pdf, 0.993, 0.002;
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gam, normal_pdf, 0.0085, 0.003;
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mst, normal_pdf, 1.0002, 0.007;
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rho, beta_pdf, 0.129, 0.223;
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psi, beta_pdf, 0.65, 0.05;
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del, beta_pdf, 0.01, 0.005;
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stderr e_a, inv_gamma_pdf, 0.035449, inf;
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stderr e_m, inv_gamma_pdf, 0.008862, inf;
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end;
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varobs gp_obs gy_obs;
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options_.solve_tolf = 1e-12;
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options_.fast_kalman=1;
|
||||
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8,plot_priors=0,mode_compute=0);
|
||||
|
|
@ -0,0 +1,142 @@
|
|||
// country_nbr must be passed on the command line: dynare swk_0 -Dcountry_nbr=3
|
||||
@#define countries = [1:country_nbr]
|
||||
|
||||
var
|
||||
@#for c in countries
|
||||
MC_@{c} E_@{c} EF_@{c} R_KF_@{c} QF_@{c} CF_@{c} IF_@{c} YF_@{c} LF_@{c} PIEF_@{c} WF_@{c} RF_@{c} R_K_@{c} Q_@{c} C_@{c} I_@{c} Y_@{c} L_@{c} PIE_@{c} W_@{c} R_@{c} EE_A_@{c} PIE_BAR_@{c} EE_B_@{c} EE_G_@{c} EE_L_@{c} EE_I_@{c} KF_@{c} K_@{c}
|
||||
@#endfor
|
||||
;
|
||||
varexo
|
||||
@#for c in countries
|
||||
E_A_@{c} E_B_@{c} E_G_@{c} E_L_@{c} E_I_@{c} ETA_R_@{c} E_PIE_BAR_@{c} ETA_Q_@{c} ETA_P_@{c} ETA_W_@{c}
|
||||
@#endfor
|
||||
;
|
||||
|
||||
parameters xi_e lambda_w alpha czcap beta phi_i tau sig_c hab ccs cinvs phi_y gamma_w xi_w gamma_p xi_p sig_l r_dpi r_pie r_dy r_y rho rho_a rho_pb rho_b rho_g rho_l rho_i ;
|
||||
alpha=.30;
|
||||
beta=.99;
|
||||
tau=0.025;
|
||||
ccs=0.6;
|
||||
cinvs=.22;
|
||||
lambda_w = 0.5;
|
||||
phi_i= 6.771;
|
||||
sig_c= 1.353;
|
||||
hab= 0.573;
|
||||
xi_w= 0.737;
|
||||
sig_l= 2.400;
|
||||
xi_p= 0.908;
|
||||
xi_e= 0.599;
|
||||
gamma_w= 0.763;
|
||||
gamma_p= 0.469;
|
||||
czcap= 0.169;
|
||||
phi_y= 1.408;
|
||||
r_pie= 1.684;
|
||||
r_dpi= 0.14;
|
||||
rho= 0.961;
|
||||
r_y= 0.099;
|
||||
r_dy= 0.159;
|
||||
rho_a= 0.823;
|
||||
rho_b= 0.855;
|
||||
rho_g= 0.949;
|
||||
rho_l= 0.889;
|
||||
rho_i= 0.927;
|
||||
rho_pb= 0.924;
|
||||
|
||||
|
||||
model(linear);
|
||||
@#for c in countries
|
||||
CF_@{c} = (1/(1+hab))*(CF_@{c}(1)+hab*CF_@{c}(-1))-((1-hab)/((1+hab)*sig_c))*(RF_@{c}-PIEF_@{c}(1)-EE_B_@{c}) ;
|
||||
0 = alpha*R_KF_@{c}+(1-alpha)*WF_@{c} -EE_A_@{c} ;
|
||||
PIEF_@{c} = 0;
|
||||
IF_@{c} = (1/(1+beta))* (( IF_@{c}(-1) + beta*(IF_@{c}(1)))+(1/phi_i)*QF_@{c})+0*ETA_Q_@{c}+EE_I_@{c} ;
|
||||
QF_@{c} = -(RF_@{c}-PIEF_@{c}(1))+(1-beta*(1-tau))*((1+czcap)/czcap)*R_KF_@{c}(1)+beta*(1-tau)*QF_@{c}(1) +0*EE_I_@{c} ;
|
||||
KF_@{c} = (1-tau)*KF_@{c}(-1)+tau*IF_@{c}(-1) ;
|
||||
YF_@{c} = (ccs*CF_@{c}+cinvs*IF_@{c})+EE_G_@{c} ;
|
||||
YF_@{c} = 1*phi_y*( alpha*KF_@{c}+alpha*(1/czcap)*R_KF_@{c}+(1-alpha)*LF_@{c}+EE_A_@{c} ) ;
|
||||
WF_@{c} = (sig_c/(1-hab))*(CF_@{c}-hab*CF_@{c}(-1)) + sig_l*LF_@{c} - EE_L_@{c} ;
|
||||
LF_@{c} = R_KF_@{c}*((1+czcap)/czcap)-WF_@{c}+KF_@{c} ;
|
||||
EF_@{c} = EF_@{c}(-1)+EF_@{c}(1)-EF_@{c}+(LF_@{c}-EF_@{c})*((1-xi_e)*(1-xi_e*beta)/(xi_e));
|
||||
C_@{c} = (hab/(1+hab))*C_@{c}(-1)+(1/(1+hab))*C_@{c}(1)-((1-hab)/((1+hab)*sig_c))*(R_@{c}-PIE_@{c}(1)-EE_B_@{c}) ;
|
||||
I_@{c} = (1/(1+beta))* (( I_@{c}(-1) + beta*(I_@{c}(1)))+(1/phi_i)*Q_@{c} )+1*ETA_Q_@{c}+1*EE_I_@{c} ;
|
||||
Q_@{c} = -(R_@{c}-PIE_@{c}(1))+(1-beta*(1-tau))*((1+czcap)/czcap)*R_K_@{c}(1)+beta*(1-tau)*Q_@{c}(1) +EE_I_@{c}*0+0*ETA_Q_@{c} ;
|
||||
K_@{c} = (1-tau)*K_@{c}(-1)+tau*I_@{c}(-1) ;
|
||||
Y_@{c} = (ccs*C_@{c}+cinvs*I_@{c})+ EE_G_@{c} ;
|
||||
Y_@{c} = phi_y*( alpha*K_@{c}+alpha*(1/czcap)*R_K_@{c}+(1-alpha)*L_@{c} ) +phi_y*EE_A_@{c} ;
|
||||
PIE_@{c} = (1/(1+beta*gamma_p))*((beta)*(PIE_@{c}(1)) +(gamma_p)*(PIE_@{c}(-1))+((1-xi_p)*(1-beta*xi_p)/(xi_p))*(MC_@{c})) + ETA_P_@{c} ;
|
||||
MC_@{c} = alpha*R_K_@{c}+(1-alpha)*W_@{c} -EE_A_@{c};
|
||||
W_@{c} = (1/(1+beta))*(beta*W_@{c}(+1)+W_@{c}(-1))+(beta/(1+beta))*(PIE_@{c}(+1))-((1+beta*gamma_w)/(1+beta))*(PIE_@{c})+(gamma_w/(1+beta))*(PIE_@{c}(-1))-(1/(1+beta))*(((1-beta*xi_w)*(1-xi_w))/(((1+(((1+lambda_w)*sig_l)/(lambda_w))))*xi_w))*(W_@{c}-sig_l*L_@{c}-(sig_c/(1-hab))*(C_@{c}-hab*C_@{c}(-1))+EE_L_@{c})+ETA_W_@{c};
|
||||
L_@{c} = R_K_@{c}*((1+czcap)/czcap)-W_@{c}+K_@{c} ;
|
||||
R_@{c} = r_dpi*(PIE_@{c}-PIE_@{c}(-1))+(1-rho)*(r_pie*(PIE_@{c}(-1)-PIE_BAR_@{c})+r_y*(Y_@{c}-YF_@{c}))+r_dy*(Y_@{c}-YF_@{c}-(Y_@{c}(-1)-YF_@{c}(-1)))+rho*(R_@{c}(-1)-PIE_BAR_@{c})+PIE_BAR_@{c}+ETA_R_@{c};
|
||||
E_@{c} = E_@{c}(-1)+E_@{c}(1)-E_@{c}+(L_@{c}-E_@{c})*((1-xi_e)*(1-xi_e*beta)/(xi_e));
|
||||
EE_A_@{c} = (rho_a)*EE_A_@{c}(-1) + E_A_@{c};
|
||||
PIE_BAR_@{c} = rho_pb*PIE_BAR_@{c}(-1)+ E_PIE_BAR_@{c} ;
|
||||
EE_B_@{c} = rho_b*EE_B_@{c}(-1) + E_B_@{c} ;
|
||||
EE_G_@{c} = rho_g*EE_G_@{c}(-1) + E_G_@{c} ;
|
||||
EE_L_@{c} = rho_l*EE_L_@{c}(-1) + E_L_@{c} ;
|
||||
EE_I_@{c} = rho_i*EE_I_@{c}(-1) + E_I_@{c} ;
|
||||
@#endfor
|
||||
end;
|
||||
|
||||
|
||||
estimated_params;
|
||||
@#for c in countries
|
||||
stderr E_A_@{c},0.543,0.01,4,INV_GAMMA_PDF,0.4,2;
|
||||
stderr E_PIE_BAR_@{c},0.072,0.001,4,INV_GAMMA_PDF,0.02,10;
|
||||
stderr E_B_@{c},0.2694,0.01,4,INV_GAMMA_PDF,0.2,2;
|
||||
stderr E_G_@{c},0.3052,0.01,4,INV_GAMMA_PDF,0.3,2;
|
||||
stderr E_L_@{c},1.4575,0.1,6,INV_GAMMA_PDF,1,2;
|
||||
stderr E_I_@{c},0.1318,0.01,4,INV_GAMMA_PDF,0.1,2;
|
||||
stderr ETA_R_@{c},0.1363,0.01,4,INV_GAMMA_PDF,0.1,2;
|
||||
stderr ETA_Q_@{c},0.4842,0.01,4,INV_GAMMA_PDF,0.4,2;
|
||||
stderr ETA_P_@{c},0.1731,0.01,4,INV_GAMMA_PDF,0.15,2;
|
||||
stderr ETA_W_@{c},0.2462,0.1,4,INV_GAMMA_PDF,0.25,2;
|
||||
@#endfor
|
||||
rho_a,.9722,.1,.9999,BETA_PDF,0.85,0.1;
|
||||
rho_pb,.85,.1,.999,BETA_PDF,0.85,0.1;
|
||||
rho_b,.7647,.1,.99,BETA_PDF,0.85,0.1;
|
||||
rho_g,.9502,.1,.9999,BETA_PDF,0.85,0.1;
|
||||
rho_l,.9542,.1,.9999,BETA_PDF,0.85,0.1;
|
||||
rho_i,.6705,.1,.99,BETA_PDF,0.85,0.1;
|
||||
phi_i,5.2083,1,15,NORMAL_PDF,4,1.5;
|
||||
sig_c,0.9817,0.25,3,NORMAL_PDF,1,0.375;
|
||||
hab,0.5612,0.3,0.95,BETA_PDF,0.7,0.1;
|
||||
xi_w,0.7661,0.3,0.9,BETA_PDF,0.75,0.05;
|
||||
sig_l,1.7526,0.5,5,NORMAL_PDF,2,0.75;
|
||||
xi_p,0.8684,0.3,0.95,BETA_PDF,0.75,0.05;
|
||||
xi_e,0.5724,0.1,0.95,BETA_PDF,0.5,0.15;
|
||||
gamma_w,0.6202,0.1,0.99,BETA_PDF,0.75,0.15;
|
||||
gamma_p,0.6638,0.1,0.99,BETA_PDF,0.75,0.15;
|
||||
czcap,0.2516,0.01,2,NORMAL_PDF,0.2,0.075;
|
||||
phi_y,1.3011,1.001,2,NORMAL_PDF,1.45,0.125;
|
||||
r_pie,1.4616,1.2,2,NORMAL_PDF,1.7,0.1;
|
||||
r_dpi,0.1144,0.01,0.5,NORMAL_PDF,0.3,0.1;
|
||||
rho,0.8865,0.5,0.99,BETA_PDF,0.8,0.10;
|
||||
r_y,0.0571,0.01,0.2,NORMAL_PDF,0.125,0.05;
|
||||
r_dy,0.2228,0.05,0.5,NORMAL_PDF,0.0625,0.05;
|
||||
end;
|
||||
|
||||
varobs
|
||||
@#for c in countries
|
||||
Y_@{c} C_@{c} I_@{c} E_@{c} PIE_@{c} W_@{c} R_@{c}
|
||||
@#endfor
|
||||
;
|
||||
|
||||
// set estimation globals
|
||||
estimation(datafile=pseudo_data,presample=40, first_obs=1, nobs=118, mh_replic=0, mode_compute=0,plot_priors=0);
|
||||
|
||||
// get variables
|
||||
[dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list_, M_.fname, [], M_, options_, oo_, estim_params_, bayestopt_);
|
||||
|
||||
// this disable the switch to steady state filter
|
||||
options_.riccati_tol = 0;
|
||||
|
||||
// actual computation for timing
|
||||
tic;
|
||||
for i=1:100;
|
||||
fval = dsge_likelihood(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_,[]);
|
||||
end;
|
||||
|
||||
toc;
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,138 @@
|
|||
// country_nbr must be passed on the command line: dynare swk_1 -Dcountry_nbr=3
|
||||
@#define countries = [1:country_nbr]
|
||||
|
||||
var
|
||||
@#for c in countries
|
||||
MC_@{c} E_@{c} EF_@{c} R_KF_@{c} QF_@{c} CF_@{c} IF_@{c} YF_@{c} LF_@{c} PIEF_@{c} WF_@{c} RF_@{c} R_K_@{c} Q_@{c} C_@{c} I_@{c} Y_@{c} L_@{c} PIE_@{c} W_@{c} R_@{c} EE_A_@{c} PIE_BAR_@{c} EE_B_@{c} EE_G_@{c} EE_L_@{c} EE_I_@{c} KF_@{c} K_@{c}
|
||||
@#endfor
|
||||
;
|
||||
varexo
|
||||
@#for c in countries
|
||||
E_A_@{c} E_B_@{c} E_G_@{c} E_L_@{c} E_I_@{c} ETA_R_@{c} E_PIE_BAR_@{c} ETA_Q_@{c} ETA_P_@{c} ETA_W_@{c}
|
||||
@#endfor
|
||||
;
|
||||
|
||||
parameters xi_e lambda_w alpha czcap beta phi_i tau sig_c hab ccs cinvs phi_y gamma_w xi_w gamma_p xi_p sig_l r_dpi r_pie r_dy r_y rho rho_a rho_pb rho_b rho_g rho_l rho_i ;
|
||||
alpha=.30;
|
||||
beta=.99;
|
||||
tau=0.025;
|
||||
ccs=0.6;
|
||||
cinvs=.22;
|
||||
lambda_w = 0.5;
|
||||
phi_i= 6.771;
|
||||
sig_c= 1.353;
|
||||
hab= 0.573;
|
||||
xi_w= 0.737;
|
||||
sig_l= 2.400;
|
||||
xi_p= 0.908;
|
||||
xi_e= 0.599;
|
||||
gamma_w= 0.763;
|
||||
gamma_p= 0.469;
|
||||
czcap= 0.169;
|
||||
phi_y= 1.408;
|
||||
r_pie= 1.684;
|
||||
r_dpi= 0.14;
|
||||
rho= 0.961;
|
||||
r_y= 0.099;
|
||||
r_dy= 0.159;
|
||||
rho_a= 0.823;
|
||||
rho_b= 0.855;
|
||||
rho_g= 0.949;
|
||||
rho_l= 0.889;
|
||||
rho_i= 0.927;
|
||||
rho_pb= 0.924;
|
||||
|
||||
|
||||
model(linear);
|
||||
@#for c in countries
|
||||
CF_@{c} = (1/(1+hab))*(CF_@{c}(1)+hab*CF_@{c}(-1))-((1-hab)/((1+hab)*sig_c))*(RF_@{c}-PIEF_@{c}(1)-EE_B_@{c}) ;
|
||||
0 = alpha*R_KF_@{c}+(1-alpha)*WF_@{c} -EE_A_@{c} ;
|
||||
PIEF_@{c} = 0;
|
||||
IF_@{c} = (1/(1+beta))* (( IF_@{c}(-1) + beta*(IF_@{c}(1)))+(1/phi_i)*QF_@{c})+0*ETA_Q_@{c}+EE_I_@{c} ;
|
||||
QF_@{c} = -(RF_@{c}-PIEF_@{c}(1))+(1-beta*(1-tau))*((1+czcap)/czcap)*R_KF_@{c}(1)+beta*(1-tau)*QF_@{c}(1) +0*EE_I_@{c} ;
|
||||
KF_@{c} = (1-tau)*KF_@{c}(-1)+tau*IF_@{c}(-1) ;
|
||||
YF_@{c} = (ccs*CF_@{c}+cinvs*IF_@{c})+EE_G_@{c} ;
|
||||
YF_@{c} = 1*phi_y*( alpha*KF_@{c}+alpha*(1/czcap)*R_KF_@{c}+(1-alpha)*LF_@{c}+EE_A_@{c} ) ;
|
||||
WF_@{c} = (sig_c/(1-hab))*(CF_@{c}-hab*CF_@{c}(-1)) + sig_l*LF_@{c} - EE_L_@{c} ;
|
||||
LF_@{c} = R_KF_@{c}*((1+czcap)/czcap)-WF_@{c}+KF_@{c} ;
|
||||
EF_@{c} = EF_@{c}(-1)+EF_@{c}(1)-EF_@{c}+(LF_@{c}-EF_@{c})*((1-xi_e)*(1-xi_e*beta)/(xi_e));
|
||||
C_@{c} = (hab/(1+hab))*C_@{c}(-1)+(1/(1+hab))*C_@{c}(1)-((1-hab)/((1+hab)*sig_c))*(R_@{c}-PIE_@{c}(1)-EE_B_@{c}) ;
|
||||
I_@{c} = (1/(1+beta))* (( I_@{c}(-1) + beta*(I_@{c}(1)))+(1/phi_i)*Q_@{c} )+1*ETA_Q_@{c}+1*EE_I_@{c} ;
|
||||
Q_@{c} = -(R_@{c}-PIE_@{c}(1))+(1-beta*(1-tau))*((1+czcap)/czcap)*R_K_@{c}(1)+beta*(1-tau)*Q_@{c}(1) +EE_I_@{c}*0+0*ETA_Q_@{c} ;
|
||||
K_@{c} = (1-tau)*K_@{c}(-1)+tau*I_@{c}(-1) ;
|
||||
Y_@{c} = (ccs*C_@{c}+cinvs*I_@{c})+ EE_G_@{c} ;
|
||||
Y_@{c} = phi_y*( alpha*K_@{c}+alpha*(1/czcap)*R_K_@{c}+(1-alpha)*L_@{c} ) +phi_y*EE_A_@{c} ;
|
||||
PIE_@{c} = (1/(1+beta*gamma_p))*((beta)*(PIE_@{c}(1)) +(gamma_p)*(PIE_@{c}(-1))+((1-xi_p)*(1-beta*xi_p)/(xi_p))*(MC_@{c})) + ETA_P_@{c} ;
|
||||
MC_@{c} = alpha*R_K_@{c}+(1-alpha)*W_@{c} -EE_A_@{c};
|
||||
W_@{c} = (1/(1+beta))*(beta*W_@{c}(+1)+W_@{c}(-1))+(beta/(1+beta))*(PIE_@{c}(+1))-((1+beta*gamma_w)/(1+beta))*(PIE_@{c})+(gamma_w/(1+beta))*(PIE_@{c}(-1))-(1/(1+beta))*(((1-beta*xi_w)*(1-xi_w))/(((1+(((1+lambda_w)*sig_l)/(lambda_w))))*xi_w))*(W_@{c}-sig_l*L_@{c}-(sig_c/(1-hab))*(C_@{c}-hab*C_@{c}(-1))+EE_L_@{c})+ETA_W_@{c};
|
||||
L_@{c} = R_K_@{c}*((1+czcap)/czcap)-W_@{c}+K_@{c} ;
|
||||
R_@{c} = r_dpi*(PIE_@{c}-PIE_@{c}(-1))+(1-rho)*(r_pie*(PIE_@{c}(-1)-PIE_BAR_@{c})+r_y*(Y_@{c}-YF_@{c}))+r_dy*(Y_@{c}-YF_@{c}-(Y_@{c}(-1)-YF_@{c}(-1)))+rho*(R_@{c}(-1)-PIE_BAR_@{c})+PIE_BAR_@{c}+ETA_R_@{c};
|
||||
E_@{c} = E_@{c}(-1)+E_@{c}(1)-E_@{c}+(L_@{c}-E_@{c})*((1-xi_e)*(1-xi_e*beta)/(xi_e));
|
||||
EE_A_@{c} = (rho_a)*EE_A_@{c}(-1) + E_A_@{c};
|
||||
PIE_BAR_@{c} = rho_pb*PIE_BAR_@{c}(-1)+ E_PIE_BAR_@{c} ;
|
||||
EE_B_@{c} = rho_b*EE_B_@{c}(-1) + E_B_@{c} ;
|
||||
EE_G_@{c} = rho_g*EE_G_@{c}(-1) + E_G_@{c} ;
|
||||
EE_L_@{c} = rho_l*EE_L_@{c}(-1) + E_L_@{c} ;
|
||||
EE_I_@{c} = rho_i*EE_I_@{c}(-1) + E_I_@{c} ;
|
||||
@#endfor
|
||||
end;
|
||||
|
||||
|
||||
estimated_params;
|
||||
@#for c in countries
|
||||
stderr E_A_@{c},0.543,0.01,4,INV_GAMMA_PDF,0.4,2;
|
||||
stderr E_PIE_BAR_@{c},0.072,0.001,4,INV_GAMMA_PDF,0.02,10;
|
||||
stderr E_B_@{c},0.2694,0.01,4,INV_GAMMA_PDF,0.2,2;
|
||||
stderr E_G_@{c},0.3052,0.01,4,INV_GAMMA_PDF,0.3,2;
|
||||
stderr E_L_@{c},1.4575,0.1,6,INV_GAMMA_PDF,1,2;
|
||||
stderr E_I_@{c},0.1318,0.01,4,INV_GAMMA_PDF,0.1,2;
|
||||
stderr ETA_R_@{c},0.1363,0.01,4,INV_GAMMA_PDF,0.1,2;
|
||||
stderr ETA_Q_@{c},0.4842,0.01,4,INV_GAMMA_PDF,0.4,2;
|
||||
stderr ETA_P_@{c},0.1731,0.01,4,INV_GAMMA_PDF,0.15,2;
|
||||
stderr ETA_W_@{c},0.2462,0.1,4,INV_GAMMA_PDF,0.25,2;
|
||||
@#endfor
|
||||
rho_a,.9722,.1,.9999,BETA_PDF,0.85,0.1;
|
||||
rho_pb,.85,.1,.999,BETA_PDF,0.85,0.1;
|
||||
rho_b,.7647,.1,.99,BETA_PDF,0.85,0.1;
|
||||
rho_g,.9502,.1,.9999,BETA_PDF,0.85,0.1;
|
||||
rho_l,.9542,.1,.9999,BETA_PDF,0.85,0.1;
|
||||
rho_i,.6705,.1,.99,BETA_PDF,0.85,0.1;
|
||||
phi_i,5.2083,1,15,NORMAL_PDF,4,1.5;
|
||||
sig_c,0.9817,0.25,3,NORMAL_PDF,1,0.375;
|
||||
hab,0.5612,0.3,0.95,BETA_PDF,0.7,0.1;
|
||||
xi_w,0.7661,0.3,0.9,BETA_PDF,0.75,0.05;
|
||||
sig_l,1.7526,0.5,5,NORMAL_PDF,2,0.75;
|
||||
xi_p,0.8684,0.3,0.95,BETA_PDF,0.75,0.05;
|
||||
xi_e,0.5724,0.1,0.95,BETA_PDF,0.5,0.15;
|
||||
gamma_w,0.6202,0.1,0.99,BETA_PDF,0.75,0.15;
|
||||
gamma_p,0.6638,0.1,0.99,BETA_PDF,0.75,0.15;
|
||||
czcap,0.2516,0.01,2,NORMAL_PDF,0.2,0.075;
|
||||
phi_y,1.3011,1.001,2,NORMAL_PDF,1.45,0.125;
|
||||
r_pie,1.4616,1.2,2,NORMAL_PDF,1.7,0.1;
|
||||
r_dpi,0.1144,0.01,0.5,NORMAL_PDF,0.3,0.1;
|
||||
rho,0.8865,0.5,0.99,BETA_PDF,0.8,0.10;
|
||||
r_y,0.0571,0.01,0.2,NORMAL_PDF,0.125,0.05;
|
||||
r_dy,0.2228,0.05,0.5,NORMAL_PDF,0.0625,0.05;
|
||||
end;
|
||||
|
||||
varobs
|
||||
@#for c in countries
|
||||
Y_@{c} C_@{c} I_@{c} E_@{c} PIE_@{c} W_@{c} R_@{c}
|
||||
@#endfor
|
||||
;
|
||||
|
||||
estimation(datafile=pseudo_data,presample=40, first_obs=1, nobs=118, mh_replic=0, mode_compute=0,plot_priors=0);
|
||||
|
||||
options_.fast_kalman = 1;
|
||||
|
||||
[dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list_, M_.fname, [], M_, options_, oo_, estim_params_, bayestopt_);
|
||||
|
||||
tic;
|
||||
for i=1:100;
|
||||
fval = dsge_likelihood(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_,[]);
|
||||
end;
|
||||
|
||||
toc;
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,102 @@
|
|||
// country_nbr must be passed on the command line: dynare swk_0 -Dcountry_nbr=3
|
||||
@#define countries = [1:country_nbr]
|
||||
|
||||
var
|
||||
@#for c in countries
|
||||
MC_@{c} E_@{c} EF_@{c} R_KF_@{c} QF_@{c} CF_@{c} IF_@{c} YF_@{c} LF_@{c} PIEF_@{c} WF_@{c} RF_@{c} R_K_@{c} Q_@{c} C_@{c} I_@{c} Y_@{c} L_@{c} PIE_@{c} W_@{c} R_@{c} EE_A_@{c} PIE_BAR_@{c} EE_B_@{c} EE_G_@{c} EE_L_@{c} EE_I_@{c} KF_@{c} K_@{c}
|
||||
@#endfor
|
||||
;
|
||||
varexo
|
||||
@#for c in countries
|
||||
E_A_@{c} E_B_@{c} E_G_@{c} E_L_@{c} E_I_@{c} ETA_R_@{c} E_PIE_BAR_@{c} ETA_Q_@{c} ETA_P_@{c} ETA_W_@{c}
|
||||
@#endfor
|
||||
;
|
||||
|
||||
parameters xi_e lambda_w alpha czcap beta phi_i tau sig_c hab ccs cinvs phi_y gamma_w xi_w gamma_p xi_p sig_l r_dpi r_pie r_dy r_y rho rho_a rho_pb rho_b rho_g rho_l rho_i ;
|
||||
alpha=.30;
|
||||
beta=.99;
|
||||
tau=0.025;
|
||||
ccs=0.6;
|
||||
cinvs=.22;
|
||||
lambda_w = 0.5;
|
||||
phi_i= 6.771;
|
||||
sig_c= 1.353;
|
||||
hab= 0.573;
|
||||
xi_w= 0.737;
|
||||
sig_l= 2.400;
|
||||
xi_p= 0.908;
|
||||
xi_e= 0.599;
|
||||
gamma_w= 0.763;
|
||||
gamma_p= 0.469;
|
||||
czcap= 0.169;
|
||||
phi_y= 1.408;
|
||||
r_pie= 1.684;
|
||||
r_dpi= 0.14;
|
||||
rho= 0.961;
|
||||
r_y= 0.099;
|
||||
r_dy= 0.159;
|
||||
rho_a= 0.823;
|
||||
rho_b= 0.855;
|
||||
rho_g= 0.949;
|
||||
rho_l= 0.889;
|
||||
rho_i= 0.927;
|
||||
rho_pb= 0.924;
|
||||
|
||||
|
||||
model(linear);
|
||||
@#for c in countries
|
||||
CF_@{c} = (1/(1+hab))*(CF_@{c}(1)+hab*CF_@{c}(-1))-((1-hab)/((1+hab)*sig_c))*(RF_@{c}-PIEF_@{c}(1)-EE_B_@{c}) ;
|
||||
0 = alpha*R_KF_@{c}+(1-alpha)*WF_@{c} -EE_A_@{c} ;
|
||||
PIEF_@{c} = 0;
|
||||
IF_@{c} = (1/(1+beta))* (( IF_@{c}(-1) + beta*(IF_@{c}(1)))+(1/phi_i)*QF_@{c})+0*ETA_Q_@{c}+EE_I_@{c} ;
|
||||
QF_@{c} = -(RF_@{c}-PIEF_@{c}(1))+(1-beta*(1-tau))*((1+czcap)/czcap)*R_KF_@{c}(1)+beta*(1-tau)*QF_@{c}(1) +0*EE_I_@{c} ;
|
||||
KF_@{c} = (1-tau)*KF_@{c}(-1)+tau*IF_@{c}(-1) ;
|
||||
YF_@{c} = (ccs*CF_@{c}+cinvs*IF_@{c})+EE_G_@{c} ;
|
||||
YF_@{c} = 1*phi_y*( alpha*KF_@{c}+alpha*(1/czcap)*R_KF_@{c}+(1-alpha)*LF_@{c}+EE_A_@{c} ) ;
|
||||
WF_@{c} = (sig_c/(1-hab))*(CF_@{c}-hab*CF_@{c}(-1)) + sig_l*LF_@{c} - EE_L_@{c} ;
|
||||
LF_@{c} = R_KF_@{c}*((1+czcap)/czcap)-WF_@{c}+KF_@{c} ;
|
||||
EF_@{c} = EF_@{c}(-1)+EF_@{c}(1)-EF_@{c}+(LF_@{c}-EF_@{c})*((1-xi_e)*(1-xi_e*beta)/(xi_e));
|
||||
C_@{c} = (hab/(1+hab))*C_@{c}(-1)+(1/(1+hab))*C_@{c}(1)-((1-hab)/((1+hab)*sig_c))*(R_@{c}-PIE_@{c}(1)-EE_B_@{c}) ;
|
||||
I_@{c} = (1/(1+beta))* (( I_@{c}(-1) + beta*(I_@{c}(1)))+(1/phi_i)*Q_@{c} )+1*ETA_Q_@{c}+1*EE_I_@{c} ;
|
||||
Q_@{c} = -(R_@{c}-PIE_@{c}(1))+(1-beta*(1-tau))*((1+czcap)/czcap)*R_K_@{c}(1)+beta*(1-tau)*Q_@{c}(1) +EE_I_@{c}*0+0*ETA_Q_@{c} ;
|
||||
K_@{c} = (1-tau)*K_@{c}(-1)+tau*I_@{c}(-1) ;
|
||||
Y_@{c} = (ccs*C_@{c}+cinvs*I_@{c})+ EE_G_@{c} ;
|
||||
Y_@{c} = phi_y*( alpha*K_@{c}+alpha*(1/czcap)*R_K_@{c}+(1-alpha)*L_@{c} ) +phi_y*EE_A_@{c} ;
|
||||
PIE_@{c} = (1/(1+beta*gamma_p))*((beta)*(PIE_@{c}(1)) +(gamma_p)*(PIE_@{c}(-1))+((1-xi_p)*(1-beta*xi_p)/(xi_p))*(MC_@{c})) + ETA_P_@{c} ;
|
||||
MC_@{c} = alpha*R_K_@{c}+(1-alpha)*W_@{c} -EE_A_@{c};
|
||||
W_@{c} = (1/(1+beta))*(beta*W_@{c}(+1)+W_@{c}(-1))+(beta/(1+beta))*(PIE_@{c}(+1))-((1+beta*gamma_w)/(1+beta))*(PIE_@{c})+(gamma_w/(1+beta))*(PIE_@{c}(-1))-(1/(1+beta))*(((1-beta*xi_w)*(1-xi_w))/(((1+(((1+lambda_w)*sig_l)/(lambda_w))))*xi_w))*(W_@{c}-sig_l*L_@{c}-(sig_c/(1-hab))*(C_@{c}-hab*C_@{c}(-1))+EE_L_@{c})+ETA_W_@{c};
|
||||
L_@{c} = R_K_@{c}*((1+czcap)/czcap)-W_@{c}+K_@{c} ;
|
||||
R_@{c} = r_dpi*(PIE_@{c}-PIE_@{c}(-1))+(1-rho)*(r_pie*(PIE_@{c}(-1)-PIE_BAR_@{c})+r_y*(Y_@{c}-YF_@{c}))+r_dy*(Y_@{c}-YF_@{c}-(Y_@{c}(-1)-YF_@{c}(-1)))+rho*(R_@{c}(-1)-PIE_BAR_@{c})+PIE_BAR_@{c}+ETA_R_@{c};
|
||||
E_@{c} = E_@{c}(-1)+E_@{c}(1)-E_@{c}+(L_@{c}-E_@{c})*((1-xi_e)*(1-xi_e*beta)/(xi_e));
|
||||
EE_A_@{c} = (rho_a)*EE_A_@{c}(-1) + E_A_@{c};
|
||||
PIE_BAR_@{c} = rho_pb*PIE_BAR_@{c}(-1)+ E_PIE_BAR_@{c} ;
|
||||
EE_B_@{c} = rho_b*EE_B_@{c}(-1) + E_B_@{c} ;
|
||||
EE_G_@{c} = rho_g*EE_G_@{c}(-1) + E_G_@{c} ;
|
||||
EE_L_@{c} = rho_l*EE_L_@{c}(-1) + E_L_@{c} ;
|
||||
EE_I_@{c} = rho_i*EE_I_@{c}(-1) + E_I_@{c} ;
|
||||
@#endfor
|
||||
end;
|
||||
|
||||
|
||||
shocks;
|
||||
@#for c in countries
|
||||
var E_A_@{c}; stderr 0.543;
|
||||
var E_PIE_BAR_@{c}; stderr 0.072;
|
||||
var E_B_@{c}; stderr 0.2694;
|
||||
var E_G_@{c}; stderr 0.3052;
|
||||
var E_L_@{c}; stderr 1.4575;
|
||||
var E_I_@{c}; stderr 0.1318;
|
||||
var ETA_R_@{c}; stderr 0.1363;
|
||||
var ETA_Q_@{c}; stderr 0.4842;
|
||||
var ETA_P_@{c}; stderr 0.1731;
|
||||
var ETA_W_@{c}; stderr 0.2462;
|
||||
@#endfor
|
||||
end;
|
||||
|
||||
stoch_simul(order=1,periods=200,irf=0,noprint);
|
||||
|
||||
@#for c in countries
|
||||
save pseudo_data Y_@{c} C_@{c} I_@{c} E_@{c} PIE_@{c} W_@{c} R_@{c} -append;
|
||||
@#endfor
|
||||
|
||||
|
||||
Loading…
Reference in New Issue