Commit d0640ddf authored by Sébastien Villemot's avatar Sébastien Villemot
Browse files

Merge remote branch 'houtanb/master'

parents 24d109c2 43479f6e
......@@ -34,7 +34,7 @@ left = 1:qcols;
right = qcols+1:qcols+neq;
zerorows = find( sum(abs( hs(:,right)' ))==0 );
while( any(zerorows) & iq <= qrows )
while( any(zerorows) && iq <= qrows )
nz = length(zerorows);
q(iq+1:iq+nz,:) = hs(zerorows,left);
hs(zerorows,:) = SPShiftright(hs(zerorows,:),neq);
......
......@@ -35,7 +35,7 @@ right = qcols+1:qcols+neq;
[Q,R,E] = qr( h(:,right) );
zerorows = find( abs(diag(R)) <= condn );
while( any(zerorows) & iq <= qrows )
while( any(zerorows) && iq <= qrows )
h=sparse(h);
Q=sparse(Q);
h = Q'*h;
......
......@@ -14,7 +14,7 @@ function CutSample(M_, options_, estim_params_)
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2005-2009 Dynare Team
% Copyright (C) 2005-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -60,7 +60,7 @@ if (TotalNumberOfMhFiles-1)-(FirstMhFile+1)+1 > 0
record.MhDraws(end,3) ];
elseif TotalNumberOfMhFiles == 1
record.KeepedDraws.Distribution = [];
elseif TotalNumberOfMhFiles == 2 & FirstMhFile > 1
elseif TotalNumberOfMhFiles == 2 && FirstMhFile > 1
record.KeepedDraws.Distribution = [MAX_nruns-FirstLine+1 ; record.MhDraws(end,3)];
end
save([DirectoryName '/' M_.fname '_mh_history.mat'],'record');
......
......@@ -47,14 +47,14 @@ nobs = size(options_.varobs,1);
%------------------------------------------------------------------------------
% 1. Get the structural parameters & define penalties
%------------------------------------------------------------------------------
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
if options_.mode_compute ~= 1 && any(xparam1 < bayestopt_.lb)
k = find(xparam1 < bayestopt_.lb);
fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
cost_flag = 0;
info = 41;
return;
end
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
if options_.mode_compute ~= 1 && any(xparam1 > bayestopt_.ub)
k = find(xparam1 > bayestopt_.ub);
fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
cost_flag = 0;
......
......@@ -59,7 +59,7 @@ mXX = evalin('base', 'mXX');
fval = [];
cost_flag = 1;
if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
if options_.mode_compute ~= 1 && any(xparam1 < bayestopt_.lb)
k = find(xparam1 < bayestopt_.lb);
fval = bayestopt_.penalty+sum((bayestopt_.lb(k)-xparam1(k)).^2);
cost_flag = 0;
......@@ -67,7 +67,7 @@ if options_.mode_compute ~= 1 & any(xparam1 < bayestopt_.lb)
return;
end
if options_.mode_compute ~= 1 & any(xparam1 > bayestopt_.ub)
if options_.mode_compute ~= 1 && any(xparam1 > bayestopt_.ub)
k = find(xparam1 > bayestopt_.ub);
fval = bayestopt_.penalty+sum((xparam1(k)-bayestopt_.ub(k)).^2);
cost_flag = 0;
......
......@@ -42,7 +42,7 @@ function [ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags)
% - bvar_prior_{tau,decay,lambda,mu,omega,flat,train}
% Copyright (C) 2003-2007 Christopher Sims
% Copyright (C) 2007-2009 Dynare Team
% Copyright (C) 2007-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -87,7 +87,7 @@ end
idx = options_.first_obs+options_.presample-train-nlags:options_.first_obs+options_.nobs-1;
% Prepare dataset
if options_.loglinear & ~options_.logdata
if options_.loglinear && ~options_.logdata
dataset = log(dataset);
end
if options_.prefilter
......@@ -106,7 +106,7 @@ mu = options_.bvar_prior_mu;
flat = options_.bvar_prior_flat;
ny = size(dataset, 2);
if options_.prefilter | options_.noconstant
if options_.prefilter || options_.noconstant
nx = 0;
else
nx = 1;
......@@ -204,7 +204,7 @@ else
breaks = [];
lbreak = 0;
end
if ~isempty(vprior) & vprior.w>0
if ~isempty(vprior) && vprior.w>0
ydum2 = zeros(lags+1,nv,nv);
xdum2 = zeros(lags+1,nx,nv);
ydum2(end,:,:) = diag(vprior.sig);
......@@ -286,7 +286,7 @@ y = ydata(smpl,:);
% Everything now set up with input data for y=Xb+e
% Add persistence dummies
if lambda ~= 0 | mu > 0
if lambda ~= 0 || mu > 0
ybar = mean(ydata(1:lags,:),1);
if ~nox
xbar = mean(xdata(1:lags,:),1);
......
......@@ -11,7 +11,7 @@ function [result,info] = check
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2001-2010 Dynare Team
% Copyright (C) 2001-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -33,7 +33,7 @@ global M_ options_ oo_
temp_options = options_;
tempex = oo_.exo_simul;
if ~options_.initval_file & M_.exo_nbr > 1
if ~options_.initval_file && M_.exo_nbr > 1
oo_.exo_simul = ones(M_.maximum_lead+M_.maximum_lag+1,1)*oo_.exo_steady_state';
end
......@@ -47,7 +47,7 @@ end
oo_.dr = dr;
if info(1) ~= 0 & info(1) ~= 3 & info(1) ~= 4
if info(1) ~= 0 && info(1) ~= 3 && info(1) ~= 4
print_info(info, options_.noprint);
end
......@@ -76,7 +76,7 @@ if options_.noprint == 0
disp(sprintf('\nThere are %d eigenvalue(s) larger than 1 in modulus ', n_explod));
disp(sprintf('for %d forward-looking variable(s)',nyf));
disp(' ')
if dr.rank == nyf & nyf == n_explod
if dr.rank == nyf && nyf == n_explod
disp('The rank condition is verified.')
else
disp('The rank conditions ISN''T verified!')
......
function check_model()
% Copyright (C) 2005-2009 Dynare Team
% Copyright (C) 2005-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -29,7 +29,7 @@ if xlen > 1
' current period. Use additional endogenous variables']) ;
end
if (M_.exo_det_nbr > 0) & (M_.maximum_lag > 1 | M_.maximum_lead > 1)
if (M_.exo_det_nbr > 0) && (M_.maximum_lag > 1 || M_.maximum_lead > 1)
error(['Exogenous deterministic variables are currently only allowed in' ...
' models with leads and lags on only one period'])
end
......
......@@ -20,7 +20,7 @@ function [fhat,xhat,fcount,retcode] = csminit(fcn,x0,f0,g0,badg,H0,varargin)
% http://sims.princeton.edu/yftp/optimize/mfiles/csminit.m
% Copyright (C) 1993-2007 Christopher Sims
% Copyright (C) 2008-2009 Dynare Team
% Copyright (C) 2008-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -59,7 +59,7 @@ fhat=f0;
g = g0;
gnorm = norm(g);
%
if (gnorm < 1.e-12) & ~badg % put ~badg 8/4/94
if (gnorm < 1.e-12) && ~badg % put ~badg 8/4/94
retcode =1;
dxnorm=0;
% gradient convergence
......@@ -138,14 +138,14 @@ else
fcount=fcount+1;
shrinkSignal = (~badg & (f0-f < max([-THETA*dfhat*lambda 0]))) | (badg & (f0-f) < 0) ;
growSignal = ~badg & ( (lambda > 0) & (f0-f > -(1-THETA)*dfhat*lambda) );
if shrinkSignal & ( (lambda>lambdaPeak) | (lambda<0) )
if (lambda>0) & ((~shrink) | (lambda/factor <= lambdaPeak))
if shrinkSignal && ( (lambda>lambdaPeak) || (lambda<0) )
if (lambda>0) && ((~shrink) || (lambda/factor <= lambdaPeak))
shrink=1;
factor=factor^.6;
while lambda/factor <= lambdaPeak
factor=factor^.6;
end
%if (abs(lambda)*(factor-1)*dxnorm < MINDX) | (abs(lambda)*(factor-1) < MINLAMB)
%if (abs(lambda)*(factor-1)*dxnorm < MINDX) || (abs(lambda)*(factor-1) < MINLAMB)
if abs(factor-1)<MINDFAC
if abs(lambda)<4
retcode=2;
......@@ -155,12 +155,12 @@ else
done=1;
end
end
if (lambda<lambdaMax) & (lambda>lambdaPeak)
if (lambda<lambdaMax) && (lambda>lambdaPeak)
lambdaMax=lambda;
end
lambda=lambda/factor;
if abs(lambda) < MINLAMB
if (lambda > 0) & (f0 <= fhat)
if (lambda > 0) && (f0 <= fhat)
% try going against gradient, which may be inaccurate
lambda = -lambda*factor^6
else
......@@ -172,11 +172,11 @@ else
done = 1;
end
end
elseif (growSignal & lambda>0) | (shrinkSignal & ((lambda <= lambdaPeak) & (lambda>0)))
elseif (growSignal && lambda>0) || (shrinkSignal && ((lambda <= lambdaPeak) && (lambda>0)))
if shrink
shrink=0;
factor = factor^.6;
%if ( abs(lambda)*(factor-1)*dxnorm< MINDX ) | ( abs(lambda)*(factor-1)< MINLAMB)
%if ( abs(lambda)*(factor-1)*dxnorm< MINDX ) || ( abs(lambda)*(factor-1)< MINLAMB)
if abs(factor-1)<MINDFAC
if abs(lambda)<4
retcode=4;
......@@ -186,7 +186,7 @@ else
done=1;
end
end
if ( f<fPeak ) & (lambda>0)
if ( f<fPeak ) && (lambda>0)
fPeak=f;
lambdaPeak=lambda;
if lambdaMax<=lambdaPeak
......
......@@ -23,7 +23,7 @@ function [fh,xh,gh,H,itct,fcount,retcodeh] = csminwel1(fcn,x0,H0,grad,crit,nit,m
% http://sims.princeton.edu/yftp/optimize/mfiles/csminwel.m
% Copyright (C) 1993-2007 Christopher Sims
% Copyright (C) 2006-2010 Dynare Team
% Copyright (C) 2006-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -105,7 +105,7 @@ while ~done
% itct=itct+1;
fcount = fcount+fc;
% erased on 8/4/94
% if (retcode == 1) | (abs(f1-f) < crit)
% if (retcode == 1) || (abs(f1-f) < crit)
% done=1;
% end
% if itct > nit
......@@ -113,7 +113,7 @@ while ~done
% retcode = -retcode;
% end
if retcode1 ~= 1
if retcode1==2 | retcode1==4
if retcode1==2 || retcode1==4
wall1=1; badg1=1;
else
if NumGrad
......@@ -134,7 +134,7 @@ while ~done
%ARGLIST
%save g1 g1 x1 f1 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13;
end
if wall1 % & (~done) by Jinill
if wall1 % && (~done) by Jinill
% Bad gradient or back and forth on step length. Possibly at
% cliff edge. Try perturbing search direction.
%
......@@ -147,7 +147,7 @@ while ~done
% P5,P6,P7,P8,P9,P10,P11,P12,P13);
fcount = fcount+fc; % put by Jinill
if f2 < f
if retcode2==2 | retcode2==4
if retcode2==2 || retcode2==4
wall2=1; badg2=1;
else
if NumGrad
......@@ -182,7 +182,7 @@ while ~done
% P4,P5,P6,P7,P8,...
% P9,P10,P11,P12,P13);
fcount = fcount+fc; % put by Jinill
if retcode3==2 | retcode3==4
if retcode3==2 || retcode3==4
wall3=1; badg3=1;
else
if NumGrad
......@@ -218,13 +218,13 @@ while ~done
f2=f;f3=f;f1=f;retcode2=retcode1;retcode3=retcode1;
end
%how to pick gh and xh
if f3 < f - crit & badg3==0 & f3 < f2 & f3 < f1
if f3 < f - crit && badg3==0 && f3 < f2 && f3 < f1
ih=3;
fh=f3;xh=x3;gh=g3;badgh=badg3;retcodeh=retcode3;
elseif f2 < f - crit & badg2==0 & f2 < f1
elseif f2 < f - crit && badg2==0 && f2 < f1
ih=2;
fh=f2;xh=x2;gh=g2;badgh=badg2;retcodeh=retcode2;
elseif f1 < f - crit & badg1==0
elseif f1 < f - crit && badg1==0
ih=1;
fh=f1;xh=x1;gh=g1;badgh=badg1;retcodeh=retcode1;
else
......@@ -271,7 +271,7 @@ while ~done
%gh
%badgh
stuck = (abs(fh-f) < crit);
if (~badg)&(~badgh)&(~stuck)
if (~badg) && (~badgh) && (~stuck)
H = bfgsi(H,gh-g,xh-x);
end
if Verbose
......@@ -293,7 +293,7 @@ while ~done
disp('smallest step still improving too slow, reversed gradient')
elseif rc == 5
disp('largest step still improving too fast')
elseif (rc == 4) | (rc==2)
elseif (rc == 4) || (rc==2)
disp('back and forth on step length never finished')
elseif rc == 3
disp('smallest step still improving too slow')
......
......@@ -22,7 +22,7 @@ function [x,rc] = csolve(FUN,x,gradfun,crit,itmax,varargin)
% http://sims.princeton.edu/yftp/optimize/mfiles/csolve.m
% Copyright (C) 1993-2007 Christopher Sims
% Copyright (C) 2007 Dynare Team
% Copyright (C) 2007-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -66,7 +66,7 @@ af00=af0;
itct=0;
while ~done
% disp([af00-af0 crit*max(1,af0)])
if itct>3 & af00-af0<crit*max(1,af0) & rem(itct,2)==1
if itct>3 && af00-af0<crit*max(1,af0) && rem(itct,2)==1
randomize=1;
else
if ~analyticg
......@@ -117,14 +117,14 @@ while ~done
lambdamin=lambda;
xmin=x+dx;
end
if ((lambda >0) & (af0-af < alpha*lambda*af0)) | ((lambda<0) & (af0-af < 0) )
if ((lambda >0) && (af0-af < alpha*lambda*af0)) || ((lambda<0) && (af0-af < 0) )
if ~shrink
factor=factor^.6;
shrink=1;
end
if abs(lambda*(1-factor))*dxSize > .1*delta;
lambda = factor*lambda;
elseif (lambda > 0) & (factor==.6) %i.e., we've only been shrinking
elseif (lambda > 0) && (factor==.6) %i.e., we've only been shrinking
lambda=-.3;
else %
subDone=1;
......@@ -138,7 +138,7 @@ while ~done
rc=3;
end
end
elseif (lambda >0) & (af-af0 > (1-alpha)*lambda*af0)
elseif (lambda >0) && (af-af0 > (1-alpha)*lambda*af0)
if shrink
factor=factor^.6;
shrink=0;
......
......@@ -113,7 +113,7 @@ end
k = [1:length(dens)];
if pshape(indx) ~= 5
[junk,k1] = max(dens);
if k1 == 1 | k1 == length(dens)
if k1 == 1 || k1 == length(dens)
k = find(dens < 10);
end
end
......
......@@ -213,7 +213,7 @@ k = 1:size(J,2);
for i=1:n
if sum(abs(J(:,i))) < 1e-8
if m(i) < n1 | m(i) > n2
if m(i) < n1 || m(i) > n2
k(i) = 0;
m(i) = 0;
end
......
......@@ -11,7 +11,7 @@ function dynare_estimation(var_list,varargin)
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2003-2010 Dynare Team
% Copyright (C) 2003-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -62,13 +62,13 @@ else
dynare_estimation_1(var_list,varargin{:});
end
if nnobs > 1 & horizon > 0
if nnobs > 1 && horizon > 0
mh_replic = options_.mh_replic;
rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
gend = options_.nobs;
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
% Take the log of the variables if needed
if options_.loglinear & ~options_.logdata % and if the data are not in logs, then...
if options_.loglinear && ~options_.logdata % and if the data are not in logs, then...
rawdata = log(rawdata);
end
......
......@@ -77,10 +77,10 @@ if options_.prefilter == 1
end
%% Set options related to filtered variables.
if options_.filtered_vars ~= 0 & isempty(options_.filter_step_ahead),
if options_.filtered_vars ~= 0 && isempty(options_.filter_step_ahead),
options_.filter_step_ahead = 1;
end
if options_.filtered_vars ~= 0 & options_.filter_step_ahead == 0,
if options_.filtered_vars ~= 0 && options_.filter_step_ahead == 0,
options_.filter_step_ahead = 1;
end
if options_.filter_step_ahead ~= 0
......@@ -120,7 +120,7 @@ if ~isempty(estim_params_)
end
% Test if initial values of the estimated parameters are all between
% the prior lower and upper bounds.
if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
if any(xparam1 < bounds(:,1)) || any(xparam1 > bounds(:,2))
find(xparam1 < bounds(:,1))
find(xparam1 > bounds(:,2))
error('Initial parameter values are outside parameter bounds')
......@@ -684,7 +684,7 @@ if ~options_.mh_posterior_mode_estimation
end
end
if options_.mode_check == 1 & ~options_.mh_posterior_mode_estimation
if options_.mode_check == 1 && ~options_.mh_posterior_mode_estimation
mode_check(xparam1,0,hh,gend,data,lb,ub,data_index,number_of_observations,no_more_missing_observations);
end
......@@ -703,7 +703,7 @@ else
end
if any(bayestopt_.pshape > 0) & ~options_.mh_posterior_mode_estimation
if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
disp(' ')
disp('RESULTS FROM POSTERIOR MAXIMIZATION')
tstath = zeros(nx,1);
......@@ -815,7 +815,7 @@ if any(bayestopt_.pshape > 0) & ~options_.mh_posterior_mode_estimation
disp(' ')
disp(sprintf('Log data density [Laplace approximation] is %f.',md_Laplace))
disp(' ')
elseif ~any(bayestopt_.pshape > 0) & options_.mh_posterior_mode_estimation
elseif ~any(bayestopt_.pshape > 0) && options_.mh_posterior_mode_estimation
disp(' ')
disp('RESULTS FROM MAXIMUM LIKELIHOOD')
tstath = zeros(nx,1);
......@@ -900,7 +900,7 @@ end
OutputDirectoryName = CheckPath('Output');
if any(bayestopt_.pshape > 0) & options_.TeX %% Bayesian estimation (posterior mode) Latex output
if any(bayestopt_.pshape > 0) && options_.TeX %% Bayesian estimation (posterior mode) Latex output
if np
filename = [OutputDirectoryName '/' M_.fname '_Posterior_Mode_1.TeX'];
fidTeX = fopen(filename,'w');
......@@ -1083,21 +1083,21 @@ if np > 0
save([M_.fname '_params.mat'],'pindx');
end
if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
(any(bayestopt_.pshape >0 ) & options_.load_mh_file) %% not ML estimation
if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
(any(bayestopt_.pshape >0 ) && options_.load_mh_file) %% not ML estimation
bounds = prior_bounds(bayestopt_);
bounds(:,1)=max(bounds(:,1),lb);
bounds(:,2)=min(bounds(:,2),ub);
bayestopt_.lb = bounds(:,1);
bayestopt_.ub = bounds(:,2);
if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
if any(xparam1 < bounds(:,1)) || any(xparam1 > bounds(:,2))
find(xparam1 < bounds(:,1))
find(xparam1 > bounds(:,2))
error('Mode values are outside prior bounds. Reduce prior_trunc.')
end
% runs MCMC
if options_.mh_replic
if options_.load_mh_file & options_.use_mh_covariance_matrix
if options_.load_mh_file && options_.use_mh_covariance_matrix
invhess = compute_mh_covariance_matrix;
end
if options_.dsge_var
......@@ -1111,7 +1111,7 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
CutSample(M_, options_, estim_params_);
return
else
if ~options_.nodiagnostic & options_.mh_replic > 1000 & options_.mh_nblck > 1
if ~options_.nodiagnostic && options_.mh_replic > 1000 && options_.mh_nblck > 1
McMCDiagnostics(options_, estim_params_, M_);
end
%% Here i discard first half of the draws:
......@@ -1131,7 +1131,7 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
if options_.moments_varendo
oo_ = compute_moments_varendo('posterior',options_,M_,oo_,var_list_);
end
if options_.smoother | ~isempty(options_.filter_step_ahead) | options_.forecast
if options_.smoother || ~isempty(options_.filter_step_ahead) || options_.forecast
prior_posterior_statistics('posterior',data,gend,data_index,missing_value);
end
xparam = get_posterior_parameters('mean');
......@@ -1139,9 +1139,9 @@ if (any(bayestopt_.pshape >0 ) & options_.mh_replic) | ...
end
end
if (~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape ...
> 0) & options_.load_mh_file)) ...
| ~options_.smoother ) & M_.endo_nbr^2*gend < 1e7 & options_.partial_information == 0 % to be fixed
if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape ...
> 0) && options_.load_mh_file)) ...
|| ~options_.smoother ) && M_.endo_nbr^2*gend < 1e7 && options_.partial_information == 0 % to be fixed
%% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
[atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,data,data_index,missing_value);
oo_.Smoother.SteadyState = ys;
......@@ -1713,7 +1713,7 @@ if (~((any(bayestopt_.pshape > 0) & options_.mh_replic) | (any(bayestopt_.pshape
end
end
if options_.forecast > 0 & options_.mh_replic == 0 & ~options_.load_mh_file
if options_.forecast > 0 && options_.mh_replic == 0 && ~options_.load_mh_file
forecast(var_list_,'smoother');
end
......
......@@ -75,7 +75,7 @@ if options_.steadystate_flag
% Check if the steady state obtained from the _steadystate file is a
% steady state.
check1 = 0;
if isfield(options_,'unit_root_vars') & options_.diffuse_filter == 0
if isfield(options_,'unit_root_vars') && options_.diffuse_filter == 0
if isempty(options_.unit_root_vars)
if ~options_.bytecode
check1 = max(abs(feval([M_.fname '_static'],...
......@@ -101,7 +101,7 @@ if info(1) > 0
print_info(info, options_.noprint)
end
if any(abs(oo_.steady_state(bayestopt_.mfys))>1e-9) & (options_.prefilter==1)
if any(abs(oo_.steady_state(bayestopt_.mfys))>1e-9) && (options_.prefilter==1)
disp(['You are trying to estimate a model with a non zero steady state for the observed endogenous'])
disp(['variables using demeaned data!'])
error('You should change something in your mod file...')
......
......@@ -25,7 +25,7 @@ function [LIK, lik] = kalman_filter(T,R,Q,H,P,Y,start,mf,kalman_tol,riccati_tol)
% NOTES
% The vector "lik" is used to evaluate the jacobian of the likelihood.
% Copyright (C) 2004-2010 Dynare Team
% Copyright (C) 2004-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -55,7 +55,7 @@ oldK = Inf;
notsteady = 1; % Steady state flag.
F_singular = 1;
while notsteady & t<smpl
while notsteady && t<smpl
t = t+1;
v = Y(:,t)-a(mf);
F = P(mf,mf) + H;
......
......@@ -12,7 +12,7 @@ function make_ex_
% SPECIAL REQUIREMENTS
%
% Copyright (C) 1996-2009 Dynare Team
% Copyright (C) 1996-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -36,7 +36,7 @@ options_ = set_default_option(options_,'periods',0);
if isempty(oo_.exo_steady_state)
oo_.exo_steady_state = zeros(M_.exo_nbr,1);
end
if M_.exo_det_nbr > 1 & isempty(oo_.exo_det_steady_state)
if M_.exo_det_nbr > 1 && isempty(oo_.exo_det_steady_state)
oo_.exo_det_steady_state = zeros(M_.exo_det_nbr,1);
end
if isempty(oo_.exo_simul)
......
......@@ -15,7 +15,7 @@ function [marginal,oo_] = marginal_density(M_, options_, estim_params_, oo_)
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2005-2009 Dynare Team
% Copyright (C) 2005-2011 Dynare Team
%
% This file is part of Dynare.
%
......@@ -94,7 +94,7 @@ while check_coverage
marginal(linee,:) = [p, lpost_mode-log(tmp/((TotalNumberOfMhDraws-TODROP)*nblck))];
warning(warning_old_state);
end