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test suite: when saving use .mat file extension because Octave doesn’t add it by default

time-shift
Houtan Bastani 2016-09-23 18:49:51 +02:00
parent e84e81dd01
commit d4f5977380
9 changed files with 18 additions and 18 deletions
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4
tests/dsge_base2.mod
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@ -32,5 +32,5 @@ steady;
stoch_simul;
save dsge_base2 oo_ M_ options_;
close all, clc;
save dsge_base2.mat oo_ M_ options_;
close all, clc;

2
tests/estimation/fs2000.mod
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@ -90,7 +90,7 @@ options_.forecast=0;
copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh1_blck1.mat'],[M_.dname '_mh1_blck1.mat'])
estimation(mode_compute=0,mode_file=fs2000_mode,order=1, datafile=fsdat_simul, nobs=192, loglinear, mh_replic=1500, mh_nblocks=1, mh_jscale=0.8);
hh=eye(size(bayestopt_.name,1));
save('fs2000_mode','hh','-append')
save('fs2000_mode.mat','hh','-append')
Laplace = oo_.MarginalDensity.LaplaceApproximation;
estimation(mode_compute=0,mode_file=fs2000_mode,order=1, datafile=fsdat_simul, nobs=192, loglinear, mh_replic=1500, mh_nblocks=1, mh_jscale=10,load_mh_file);
oo_.MarginalDensity.LaplaceApproximation = Laplace;

2
tests/kalman_filter_fast/swk_simul.mod
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@ -96,7 +96,7 @@ 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;
save pseudo_data.mat Y_@{c} C_@{c} I_@{c} E_@{c} PIE_@{c} W_@{c} R_@{c} -append;
@#endfor

4
tests/loglinear/example4_exp.mod
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@ -4,7 +4,7 @@
*/
/*
* Copyright (C) 2001-2010 Dynare Team
* Copyright (C) 2001-2016 Dynare Team
*
* This file is part of Dynare.
*
@ -65,4 +65,4 @@ end;
stoch_simul(order=1);
oo_exp=oo_;
save results_exp oo_exp
save results_exp.mat oo_exp

2
tests/objectives/sgu_ex1.mod
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@ -29,5 +29,5 @@ stoch_simul(nomoments,irf=0,nocorr,ar=0);
global dr_
dr_obj_ = dr_;
save sgu_ex1 dr_obj_;
save sgu_ex1.mat dr_obj_;

16
tests/observation_trends_and_prefiltering/generate_trend_stationary_AR1.m
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@ -12,17 +12,17 @@ sigma_p=0.001;
orig_params=[rho_y rho_p g_y g_p sigma_y sigma_p]';
param_names=char('rho_y','rho_p','g_y','g_p','sigma_y','sigma_p');
save orig_params_prefilter orig_params param_names
save orig_params_prefilter.mat orig_params param_names
orig_params=[rho_y rho_p g_y g_p const_y const_p sigma_y sigma_p]';
param_names=char('rho_y','rho_p','g_y','g_p','const_y','const_p','sigma_y','sigma_p');
save orig_params orig_params param_names
save orig_params.mat orig_params param_names
jumping_covariance=diag([1e-8; 1e-8; 1e-16; 1e-16; 1e-8; 1e-8; 1e-12; 1e-12;])^-1;
save MCMC_jump_covar jumping_covariance
save MCMC_jump_covar.mat jumping_covariance
jumping_covariance=diag([1e-8; 1e-8; 1e-16; 1e-16; 1e-12; 1e-12;])^-1;
save MCMC_jump_covar_prefilter jumping_covariance
save MCMC_jump_covar_prefilter.mat jumping_covariance
%% data without constant
log_P=zeros(1,n_periods);
@ -40,7 +40,7 @@ log_Y=log_Y+g_y*(1:n_periods);
Y_obs=exp(log_Y);
P_obs=exp(log_P);
junk2=exp(junk2_orig);
save Exp_AR1_trend_data_no_constant Y_obs P_obs junk2
save Exp_AR1_trend_data_no_constant.mat Y_obs P_obs junk2
%
% [b_p,~,~,~,stats_p] = regress(log(P_obs(2:end))',[ones(n_periods-1,1) (2:n_periods)' log(P_obs(1:end-1)')]);
% [b_y,~,~,~,stats_y] = regress(log(Y_obs(2:end))',[ones(n_periods-1,1) (2:n_periods)' log(Y_obs(1:end-1)')]);
@ -48,7 +48,7 @@ save Exp_AR1_trend_data_no_constant Y_obs P_obs junk2
Y_obs=log_Y;
P_obs=log_P;
junk2=junk2_orig;
save AR1_trend_data_no_constant Y_obs P_obs junk2
save AR1_trend_data_no_constant.mat Y_obs P_obs junk2
% [b_p,~,~,~,stats_p] = regress((P_obs(2:end))',[ones(n_periods-1,1) (2:n_periods)' (P_obs(1:end-1)')]);
% [b_y,~,~,~,stats_y] = regress((Y_obs(2:end))',[ones(n_periods-1,1) (2:n_periods)' (Y_obs(1:end-1)')]);
@ -69,7 +69,7 @@ log_Y=log_Y+g_y*(1:n_periods);
Y_obs=exp(log_Y);
P_obs=exp(log_P);
junk2=exp(junk2_orig);
save Exp_AR1_trend_data_with_constant Y_obs P_obs junk2
save Exp_AR1_trend_data_with_constant.mat Y_obs P_obs junk2
% [b,bint,r,rint,stats] = regress(log(P_obs(2:end))',[ones(n_periods-1,1) (2:n_periods)' log(P_obs(1:end-1)')]);
% [b,bint,r,rint,stats] = regress(log(Y_obs(2:end))',[ones(n_periods-1,1) (2:n_periods)' log(Y_obs(1:end-1)')]);
@ -77,7 +77,7 @@ save Exp_AR1_trend_data_with_constant Y_obs P_obs junk2
Y_obs=log_Y;
P_obs=log_P;
junk2=junk2_orig;
save AR1_trend_data_with_constant Y_obs P_obs junk2
save AR1_trend_data_with_constant.mat Y_obs P_obs junk2
% [b_p,~,~,~,stats_p] = regress((P_obs(2:end))',[ones(n_periods-1,1) (2:n_periods)' (P_obs(1:end-1)')]);
% [b_y,~,~,~,stats_y] = regress((Y_obs(2:end))',[ones(n_periods-1,1) (2:n_periods)' (Y_obs(1:end-1)')]);

2
tests/practicing/hall1.mod
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@ -43,4 +43,4 @@ steady;
check;
stoch_simul(dr_algo=1, order=1, periods=500, irf=10);
save data_hall c in;
save data_hall.mat c in;

2
tests/practicing/rosen.mod
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@ -51,5 +51,5 @@ check;
stoch_simul(dr_algo=1, order=1, periods=500, irf=10);
//datasaver('simudata',[]);
save data_rosen s N P W;
save data_rosen.mat s N P W;

2
tests/practicing/sargent77.mod
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@ -38,4 +38,4 @@ end;
stoch_simul(dr_algo=1,drop=0, order=1, periods=33, irf=0);
save data_hyperinfl x mu;
save data_hyperinfl.mat x mu;