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estimation_dll: adding test for logposterior

time-shift
Michel Juillard 2010-12-10 21:34:09 +01:00
parent 667a25ce9e
commit d38c4de498
3 changed files with 1436 additions and 0 deletions
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285
mex/sources/estimation/tests/logposterior_dll_test/random_walk_metropolis_hastings_core.m Normal file
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function myoutput = random_walk_metropolis_hastings_core(myinputs,fblck,nblck,whoiam, ThisMatlab)
% PARALLEL CONTEXT
% This function contain the most computationally intensive portion of code in
% random_walk_metropolis_hastings (the 'for xxx = fblck:nblck' loop). The branches in 'for'
% cycle and are completely independent than suitable to be executed in parallel way.
%
% INPUTS
% o myimput [struc] The mandatory variables for local/remote
% parallel computing obtained from random_walk_metropolis_hastings.m
% function.
% o fblck and nblck [integer] The Metropolis-Hastings chains.
% o whoiam [integer] In concurrent programming a modality to refer to the differents thread running in parallel is needed.
% The integer whoaim is the integer that
% allows us to distinguish between them. Then it is the index number of this CPU among all CPUs in the
% cluster.
% o ThisMatlab [integer] Allows us to distinguish between the
% 'main' matlab, the slave matlab worker, local matlab, remote matlab,
% ... Then it is the index number of this slave machine in the cluster.
% OUTPUTS
% o myoutput [struc]
% If executed without parallel is the original output of 'for b =
% fblck:nblck' otherwise a portion of it computed on a specific core or
% remote machine. In this case:
% record;
% irun;
% NewFile;
% OutputFileName
%
% ALGORITHM
% Portion of Metropolis-Hastings.
%
% SPECIAL REQUIREMENTS.
% None.
% PARALLEL CONTEXT
% The most computationally intensive part of this function may be executed
% in parallel. The code sutable to be executed in parallel on multi core or cluster machine,
% is removed from this function and placed in random_walk_metropolis_hastings_core.m funtion.
% Then the DYNARE parallel package contain a set of pairs matlab functios that can be executed in
% parallel and called name_function.m and name_function_core.m.
% In addition in the parallel package we have second set of functions used
% to manage the parallel computation.
%
% This function was the first function to be parallelized, later other
% functions have been parallelized using the same methodology.
% Then the comments write here can be used for all the other pairs of
% parallel functions and also for management funtions.
% Copyright (C) 2006-2008,2010 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/>.
if nargin<4,
whoiam=0;
end
global bayestopt_ estim_params_ options_ M_ oo_
% reshape 'myinputs' for local computation.
% In order to avoid confusion in the name space, the instruction struct2local(myinputs) is replaced by:
TargetFun=myinputs.TargetFun;
ProposalFun=myinputs.ProposalFun;
xparam1=myinputs.xparam1;
vv=myinputs.vv;
mh_bounds=myinputs.mh_bounds;
ix2=myinputs.ix2;
ilogpo2=myinputs.ilogpo2;
ModelName=myinputs.ModelName;
fline=myinputs.fline;
npar=myinputs.npar;
nruns=myinputs.nruns;
NewFile=myinputs.NewFile;
MAX_nruns=myinputs.MAX_nruns;
d=myinputs.d;
InitSizeArray=myinputs.InitSizeArray;
record=myinputs.record;
varargin=myinputs.varargin;
% Necessary only for remote computing!
if whoiam
Parallel=myinputs.Parallel;
% initialize persistent variables in priordens()
priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7, ...
bayestopt_.p3,bayestopt_.p4,1);
end
% (re)Set the penalty
bayestopt_.penalty = Inf;
MhDirectoryName = CheckPath('metropolis');
options_.lik_algo = 1;
OpenOldFile = ones(nblck,1);
if strcmpi(ProposalFun,'rand_multivariate_normal')
n = npar;
elseif strcmpi(ProposalFun,'rand_multivariate_student')
n = options_.student_degrees_of_freedom;
end
% load([MhDirectoryName '/' ModelName '_mh_history.mat'],'record');
%%%%
%%%% NOW i run the (nblck-fblck+1) metropolis-hastings chains
%%%%
if any(isnan(bayestopt_.jscale))
if exist([ModelName '_optimal_mh_scale_parameter.mat'])% This file is created by mode_compute=6.
load([ModelName '_optimal_mh_scale_parameter'])
proposal_covariance = d*Scale;
else
error('mh:: Something is wrong. I can''t figure out the value of the scale parameter.')
end
else
proposal_covariance = d*diag(bayestopt_.jscale);
end
jloop=0;
for b = fblck:nblck,
jloop=jloop+1;
randn('state',record.Seeds(b).Normal);
rand('state',record.Seeds(b).Unifor);
if (options_.load_mh_file~=0) & (fline(b)>1) & OpenOldFile(b)
load(['./' MhDirectoryName '/' ModelName '_mh' int2str(NewFile(b)) ...
'_blck' int2str(b) '.mat'])
x2 = [x2;zeros(InitSizeArray(b)-fline(b)+1,npar)];
logpo2 = [logpo2;zeros(InitSizeArray(b)-fline(b)+1,1)];
OpenOldFile(b) = 0;
else
x2 = zeros(InitSizeArray(b),npar);
logpo2 = zeros(InitSizeArray(b),1);
end
if exist('OCTAVE_VERSION') || options_.console_mode
diary off
disp(' ')
elseif whoiam
% keyboard;
waitbarString = ['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...'];
% waitbarTitle=['Metropolis-Hastings ',options_.parallel(ThisMatlab).ComputerName];
if options_.parallel(ThisMatlab).Local,
waitbarTitle=['Local '];
else
waitbarTitle=[options_.parallel(ThisMatlab).ComputerName];
end
fMessageStatus(0,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab));
else,
hh = waitbar(0,['Please wait... Metropolis-Hastings (' int2str(b) '/' int2str(options_.mh_nblck) ')...']);
set(hh,'Name','Metropolis-Hastings');
end
isux = 0;
jsux = 0;
irun = fline(b);
j = 1;
while j <= nruns(b)
par = feval(ProposalFun, ix2(b,:), proposal_covariance, n);
if all( par(:) > mh_bounds(:,1) ) & all( par(:) < mh_bounds(:,2) )
try
logpost = - feval(TargetFun, par(:),varargin{:});
catch
logpost = -inf;
end
% testing logposterior DLL
[junk,logpost1] = logposterior(par(:),varargin{2},mexext);
if abs(logpost+logpost1) > 1e-10;
disp ([logpost -logpost1])
end
else
logpost = -inf;
end
if (logpost > -inf) && (log(rand) < logpost-ilogpo2(b))
x2(irun,:) = par;
ix2(b,:) = par;
logpo2(irun) = logpost;
ilogpo2(b) = logpost;
isux = isux + 1;
jsux = jsux + 1;
else
x2(irun,:) = ix2(b,:);
logpo2(irun) = ilogpo2(b);
end
prtfrc = j/nruns(b);
if exist('OCTAVE_VERSION') || options_.console_mode
if mod(j, 10) == 0
if exist('OCTAVE_VERSION')
printf('MH: Computing Metropolis-Hastings (chain %d/%d): %3.f%% done, acception rate: %3.f%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
else
fprintf(' MH: Computing Metropolis-Hastings (chain %d/%d): %3.f \b%% done, acceptance rate: %3.f \b%%\r', b, nblck, 100 * prtfrc, 100 * isux / j);
end
end
if mod(j,50)==0 & whoiam
% keyboard;
waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) '), ' sprintf('accept. %3.f%%%%', 100 * isux/j)];
fMessageStatus(prtfrc,whoiam,waitbarString, '', options_.parallel(ThisMatlab));
end
else
if mod(j, 3)==0 & ~whoiam
waitbar(prtfrc,hh,[ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)]);
elseif mod(j,50)==0 & whoiam,
% keyboard;
waitbarString = [ '(' int2str(b) '/' int2str(options_.mh_nblck) ') ' sprintf('%f done, acceptation rate %f',prtfrc,isux/j)];
fMessageStatus(prtfrc,whoiam,waitbarString, waitbarTitle, options_.parallel(ThisMatlab));
end
end
if (irun == InitSizeArray(b)) | (j == nruns(b)) % Now I save the simulations
save([MhDirectoryName '/' ModelName '_mh' int2str(NewFile(b)) '_blck' int2str(b) '.mat'],'x2','logpo2');
fidlog = fopen([MhDirectoryName '/metropolis.log'],'a');
fprintf(fidlog,['\n']);
fprintf(fidlog,['%% Mh' int2str(NewFile(b)) 'Blck' int2str(b) ' (' datestr(now,0) ')\n']);
fprintf(fidlog,' \n');
fprintf(fidlog,[' Number of simulations.: ' int2str(length(logpo2)) '\n']);
fprintf(fidlog,[' Acceptation rate......: ' num2str(jsux/length(logpo2)) '\n']);
fprintf(fidlog,[' Posterior mean........:\n']);
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(mean(x2(:,i))) '\n']);
end
fprintf(fidlog,[' log2po:' num2str(mean(logpo2)) '\n']);
fprintf(fidlog,[' Minimum value.........:\n']);;
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(min(x2(:,i))) '\n']);
end
fprintf(fidlog,[' log2po:' num2str(min(logpo2)) '\n']);
fprintf(fidlog,[' Maximum value.........:\n']);
for i=1:length(x2(1,:))
fprintf(fidlog,[' params:' int2str(i) ': ' num2str(max(x2(:,i))) '\n']);
end
fprintf(fidlog,[' log2po:' num2str(max(logpo2)) '\n']);
fprintf(fidlog,' \n');
fclose(fidlog);
jsux = 0;
if j == nruns(b) % I record the last draw...
record.LastParameters(b,:) = x2(end,:);
record.LastLogLiK(b) = logpo2(end);
end
% size of next file in chain b
InitSizeArray(b) = min(nruns(b)-j,MAX_nruns);
% initialization of next file if necessary
if InitSizeArray(b)
x2 = zeros(InitSizeArray(b),npar);
logpo2 = zeros(InitSizeArray(b),1);
NewFile(b) = NewFile(b) + 1;
irun = 0;
end
end
j=j+1;
irun = irun + 1;
end% End of the simulations for one mh-block.
record.AcceptationRates(b) = isux/j;
if exist('OCTAVE_VERSION') || options_.console_mode
if exist('OCTAVE_VERSION')
printf('\n');
else
fprintf('\n');
end
diary on;
elseif ~whoiam
close(hh);
end
record.Seeds(b).Normal = randn('state');
record.Seeds(b).Unifor = rand('state');
OutputFileName(jloop,:) = {[MhDirectoryName,filesep], [ModelName '_mh*_blck' int2str(b) '.mat']};
end% End of the loop over the mh-blocks.
myoutput.record = record;
myoutput.irun = irun;
myoutput.NewFile = NewFile;
myoutput.OutputFileName = OutputFileName;

967
mex/sources/estimation/tests/logposterior_dll_test/rawdata_euromodel_1.m Normal file
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@ -0,0 +1,967 @@
C =[
-7.4073
-6.1860
-6.5983
-5.6088
-5.0547
-4.4774
-3.8081
-3.8425
-2.4178
-1.9835
-1.0395
-0.1583
-0.0397
0.3505
-0.1879
-0.0067
0.0478
-1.2247
-1.4349
-0.7973
-0.0461
0.5844
1.1372
1.3801
1.8023
2.2972
2.0469
2.5435
2.8169
3.2007
2.6705
3.0518
3.2445
3.8443
3.8525
4.9494
4.2770
4.9532
5.1441
3.7124
3.9880
3.6926
2.6005
1.8679
1.9085
1.5563
1.2308
0.3264
-0.2208
-0.2483
-0.4082
-1.0315
-1.6030
-1.5499
-1.3777
-2.1675
-2.5138
-2.8820
-2.6958
-2.4719
-1.9854
-1.7954
-2.2362
-1.0595
-0.8808
-0.8548
-1.2839
-0.1363
0.2104
0.8810
0.3555
0.4766
1.3269
1.4506
1.4308
1.6263
1.9842
2.3948
2.8710
3.0177
2.9305
3.1739
3.7380
3.8285
3.3342
3.7447
3.7830
3.1039
2.8413
3.0338
0.3669
0.0847
0.0104
0.2115
-0.6649
-0.9625
-0.7330
-0.8664
-1.4441
-1.0179
-1.2729
-1.9539
-1.4427
-2.0371
-1.9764
-2.5654
-2.8570
-2.5842
-3.0427
-2.8312
-2.3320
-2.2768
-2.1816
-2.1043
-1.8969
-2.2388
-2.1679
-2.1172
];
E =[
0.6263
0.7368
0.7477
1.0150
0.6934
0.4135
0.3845
0.2380
0.2853
0.5999
0.8622
1.2116
1.4921
1.5816
1.7259
1.6276
1.2422
0.8084
0.4710
-0.3704
-0.6427
-0.5323
-0.5562
-0.3651
-0.4356
-0.7164
-0.5816
-0.4635
-0.8456
-0.9708
-0.7138
-0.7499
-0.6941
-0.6656
-0.2912
-0.1650
0.0774
0.2307
0.4484
0.4942
0.4653
0.2196
0.1736
-0.1595
-0.3918
-0.4611
-0.8493
-0.7384
-1.0604
-1.2166
-1.7187
-1.6932
-1.7830
-1.7035
-2.2079
-2.3769
-2.2511
-2.1093
-2.4638
-2.4027
-2.1313
-1.9199
-1.7941
-1.4661
-1.2269
-1.0392
-1.0725
-0.7156
-0.4778
-0.4233
-0.0409
0.1620
0.4280
0.5873
1.0323
1.3420
1.6902
2.0680
2.8219
3.2511
3.2930
3.5633
3.8992
3.6874
3.2849
3.1614
2.6221
2.5067
1.9223
1.1777
0.4483
-0.0661
-0.4424
-0.9000
-1.1478
-1.2047
-1.1412
-1.2383
-1.1048
-0.9716
-0.9287
-1.0057
-1.0827
-1.0200
-1.0072
-1.1740
-1.2809
-1.1086
-0.9866
-0.8947
-0.5875
-0.2329
0.1493
0.4906
0.8400
1.0720
1.2648
1.5431
];
I =[
2.6617
2.4325
1.9592
3.2530
2.9949
3.7918
4.7444
4.8289
5.5983
7.8923
9.4297
9.5010
10.0150
10.0413
9.6046
6.4766
5.9647
3.0114
0.5683
-2.1226
-2.1855
-0.8329
-1.5207
-1.3419
-1.7897
-0.1476
0.4675
-1.6516
-1.5419
-1.3050
-1.2451
-0.7815
-0.7796
-0.3612
-2.4072
1.1162
1.1383
3.4132
5.0356
2.8016
2.1734
0.9366
-0.7050
-1.5021
-2.9868
-6.0237
-6.2589
-6.9138
-8.2340
-9.2589
-9.2465
-9.6988
-9.7782
-10.5645
-10.7544
-13.1583
-12.2718
-12.0131
-13.5983
-12.3579
-10.9146
-11.1572
-12.4935
-9.4393
-8.5535
-7.3723
-10.0169
-6.6088
-5.2045
-4.1024
-2.8472
-1.3139
0.0477
1.5629
3.6947
4.0327
4.1320
7.1400
9.1036
8.5609
7.6576
8.8022
8.9611
10.0871
9.4797
9.3964
10.0363
8.6340
6.6522
4.4471
0.2854
-2.1879
-2.9879
-4.1021
-2.7713
-2.2281
-1.2908
-0.3250
0.6534
0.3942
0.3534
-0.1532
-1.7936
0.4909
0.3634
0.4290
-0.9709
0.1942
0.6103
1.4426
2.7225
1.7525
3.2780
3.5985
4.9011
5.3312
6.4402
6.6529
];
L =[
0.6263
0.7368
0.7477
1.0150
0.6934
0.4135
0.3845
0.2380
0.2853
0.5999
0.8622
1.2116
1.4921
1.5816
1.7259
1.6276
1.2422
0.8084
0.4710
-0.3704
-0.6427
-0.5323
-0.5562
-0.3651
-0.4356
-0.7164
-0.5816
-0.4635
-0.8456
-0.9708
-0.7138
-0.7499
-0.6941
-0.6656
-0.2912
-0.1650
0.0774
0.2307
0.4484
0.4942
0.4653
0.2196
0.1736
-0.1595
-0.3918
-0.4611
-0.8493
-0.7384
-1.0604
-1.2166
-1.7187
-1.6932
-1.7830
-1.7035
-2.2079
-2.3769
-2.2511
-2.1093
-2.4638
-2.4027
-2.1313
-1.9199
-1.7941
-1.4661
-1.2269
-1.0392
-1.0725
-0.7156
-0.4778
-0.4233
-0.0409
0.1620
0.4280
0.5873
1.0323
1.3420
1.6902
2.0680
2.8219
3.2511
3.2930
3.5633
3.8992
3.6874
3.2849
3.1614
2.6221
2.5067
1.9223
1.1777
0.4483
-0.0661
-0.4424
-0.9000
-1.1478
-1.2047
-1.1412
-1.2383
-1.1048
-0.9716
-0.9287
-1.0057
-1.0827
-1.0200
-1.0072
-1.1740
-1.2809
-1.1086
-0.9866
-0.8947
-0.5875
-0.2329
0.1493
0.4906
0.8400
1.0720
1.2648
1.5431
];
PIE =[
-1.0113
-0.8305
0.2332
-0.8746
-0.7978
-0.9220
-0.2487
-0.7749
-0.5460
-0.5347
0.5050
-0.0334
0.6756
0.8791
0.7267
1.0997
1.1750
1.1927
0.4420
0.5357
0.0345
0.0196
0.3371
0.9379
1.2160
0.3393
0.5813
0.7410
0.3374
0.2616
0.4025
0.4799
0.5981
-0.1523
0.4458
0.2182
0.9793
0.7562
1.0064
0.8203
0.6966
0.3352
0.6581
0.6111
0.9833
1.1991
0.9562
0.3868
0.2939
0.2471
0.8331
0.0715
0.3910
0.3301
0.2547
-0.2702
-0.2998
-0.1953
-0.2293
-0.3284
0.0480
-0.0374
0.3253
-0.3434
-0.3892
-0.7178
-0.4758
-0.6794
-0.8505
-0.3512
-0.4436
-0.5101
-0.4574
-0.2696
-0.1047
-0.5745
-0.2989
-0.0063
0.0088
-0.1184
-0.1506
-0.4073
0.2674
0.2896
0.0669
0.1166
-0.1699
-0.2518
-0.0562
-0.3269
-0.0703
-0.1046
-0.4888
-0.3524
-0.2485
-0.5870
-0.4546
-0.3970
-0.2353
-0.0352
-0.2171
-0.3754
-0.4322
-0.4572
-0.4903
-0.4518
-0.6435
-0.6304
-0.4148
-0.2892
-0.4318
-0.6010
-0.4148
-0.4315
-0.3531
-0.8053
-0.4680
-0.4263
];
R =[
-1.0750
-1.1540
-1.3682
-1.4569
-1.3490
-1.4011
-1.6486
-1.6968
-1.6976
-1.2567
-1.1392
-0.7783
-0.3021
-0.0435
0.0066
-0.0043
0.1029
-0.0628
-0.5358
-0.9627
-1.1079
-1.0918
-0.9966
-0.6223
-0.3616
-0.2711
-0.0997
-0.2810
-0.3710
-0.3167
-0.5301
-0.5826
-0.3194
-0.2713
-0.5287
-0.2432
0.1098
0.5349
0.7094
0.8415
0.6226
0.7376
0.9316
1.4370
1.5853
1.4267
1.1783
1.2046
0.9689
0.7918
0.6315
0.5950
0.6853
0.7171
0.5887
0.4873
0.4027
0.3489
0.2934
0.3060
0.1741
0.0348
0.0771
-0.1005
-0.1518
-0.1104
-0.0681
-0.0059
0.0256
0.0404
-0.1721
-0.2002
0.0015
0.1249
0.3738
0.4320
0.5579
0.8186
0.8727
0.7356
0.7243
0.8635
0.9058
0.7656
0.7936
0.8631
0.9074
0.9547
1.2045
1.0850
0.9178
0.5242
0.3178
0.1472
0.0227
-0.0799
-0.0611
-0.0140
0.1132
0.1774
0.0782
0.0436
-0.1596
-0.2691
-0.2895
-0.3791
-0.4020
-0.4166
-0.4037
-0.3636
-0.4075
-0.4311
-0.4470
-0.5111
-0.6274
-0.7261
-0.6974
-0.5012
];
W =[
-14.8791
-13.2300
-13.5037
-13.0249
-11.2546
-10.0148
-8.8586
-8.5739
-7.7851
-6.7136
-5.5878
-4.6881
-3.8039
-3.0366
-2.7342
-1.3135
-0.7387
-0.1131
-0.2769
0.8696
1.8855
2.3667
2.4942
3.2049
3.9682
5.1500
4.7047
4.7827
5.3377
5.6614
5.2813
5.2967
5.5175
6.1526
5.6627
6.0694
6.5824
6.9032
6.7849
6.6896
6.6201
6.9933
5.8959
6.7419
6.9999
6.4009
5.5083
5.1054
5.2813
4.5790
3.9589
3.8599
3.8978
2.7957
3.2480
1.4634
1.9219
1.8398
1.9279
1.8316
1.6092
1.2741
0.2031
-0.0236
-0.1004
-0.3034
-1.0273
-0.2205
0.0458
0.2386
-0.0977
-0.3145
-0.1416
-0.7009
-0.9082
-0.8802
-0.5644
-0.5852
-0.5346
0.0652
0.1301
0.3444
-0.3592
0.8096
0.9644
1.0289
1.2781
1.2298
2.2134
2.0808
0.4925
0.6506
0.5531
0.2456
-0.5351
-0.8183
-0.8967
-0.7268
-1.0738
-1.2844
-1.4338
-1.6995
-1.7085
-2.2889
-2.1018
-2.4273
-2.4609
-2.1407
-2.3847
-3.1689
-4.5581
-4.1027
-4.2436
-4.8836
-5.9660
-4.9971
-5.2386
-5.6618
];
Y =[
-4.9347
-4.6205
-5.2198
-4.5937
-3.8015
-3.6643
-2.7239
-2.7524
-2.0634
-1.0112
0.0530
0.7623
1.7927
2.1486
2.4866
2.1456
2.1671
-0.0254
-1.6716
-1.9673
-1.6109
-1.0292
-0.1222
0.7329
1.1234
2.0603
1.7998
1.4820
1.1732
1.6424
1.5382
2.1399
2.0127
2.7210
2.4966
3.5249
3.6237
4.2011
4.5634
3.3442
2.7761
1.9812
1.3779
1.4616
1.3029
0.7594
0.3695
0.0832
-0.8118
-1.4557
-1.4850
-1.2346
-1.5696
-1.3785
-0.7682
-2.0308
-1.7778
-1.7801
-2.1711
-1.7469
-1.3413
-1.3352
-2.4390
-1.2125
-1.1695
-1.0891
-2.4753
-1.3503
-0.9412
-0.1470
0.0026
0.1108
0.6890
1.3520
1.6018
2.0667
1.7625
2.6658
3.4048
3.2507
3.4251
3.2174
3.1903
3.3396
3.1358
2.8625
3.3546
2.4609
1.9534
0.9962
-0.7904
-1.1672
-1.2586
-1.3593
-1.3443
-0.9413
-0.6023
-0.4516
-0.5129
-0.8741
-1.0784
-1.4091
-1.3627
-1.5731
-1.6037
-1.8814
-2.1482
-1.3597
-1.1855
-1.1122
-0.8424
-0.9747
-1.1385
-1.4548
-1.4284
-1.4633
-1.0621
-0.7871
];

184
mex/sources/estimation/tests/logposterior_dll_test/sweuromodel_dll.mod Normal file
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//options_.usePartInfo=1;
var MC E EF R_KF QF CF IF YF LF PIEF WF RF R_K Q C I Y L PIE W R EE_A PIE_BAR EE_B EE_G EE_L EE_I KF K one BIGTHETA;
varexo E_A E_B E_G E_L E_I ETA_R E_PIE_BAR ETA_Q ETA_P ETA_W ;
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 LMP ;
alpha=.30;
beta=.99;
tau=0.025;
ccs=0.6;
cinvs=.22; //% alpha*(tau+ctrend)/R_K R_K=ctrend/beta-1+tau
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;
LMP = 0.0 ; //NEW.
model(linear, use_dll);
CF = (1/(1+hab))*(CF(1)+hab*CF(-1))-((1-hab)/((1+hab)*sig_c))*(RF-PIEF(1)-EE_B) ;
0 = alpha*R_KF+(1-alpha)*WF -EE_A ;
PIEF = 0*one;
IF = (1/(1+beta))* (( IF(-1) + beta*(IF(1)))+(1/phi_i)*QF)+0*ETA_Q+EE_I ;
QF = -(RF-PIEF(1))+(1-beta*(1-tau))*((0+czcap)/czcap)*R_KF(1)+beta*(1-tau)*QF(1) +0*EE_I ;
KF = (1-tau)*KF(-1)+tau*IF(-1) ;
YF = (ccs*CF+cinvs*IF)+EE_G ;
YF = 1*phi_y*( alpha*KF+alpha*(1/czcap)*R_KF+(1-alpha)*LF+EE_A ) ;
WF = (sig_c/(1-hab))*(CF-hab*CF(-1)) + sig_l*LF - EE_L ;
LF = R_KF*((1+czcap)/czcap)-WF+KF ;
EF = EF(-1)+EF(1)-EF+(LF-EF)*((1-xi_e)*(1-xi_e*beta)/(xi_e));
C = (hab/(1+hab))*C(-1)+(1/(1+hab))*C(1)-((1-hab)/((1+hab)*sig_c))*(R-PIE(1)-EE_B) ;
I = (1/(1+beta))* (( I(-1) + beta*(I(1)))+(1/phi_i)*Q )+1*ETA_Q+1*EE_I ;
Q = -(R-PIE(1))+(1-beta*(1-tau))*((0+czcap)/czcap)*R_K(1)+beta*(1-tau)*Q(1) +EE_I*0+0*ETA_Q ;
K = (1-tau)*K(-1)+tau*I(-1) ;
Y = (ccs*C+cinvs*I)+ EE_G ;
Y = phi_y*( alpha*K+alpha*(1/czcap)*R_K+(1-alpha)*L ) +phi_y*EE_A ;
PIE = (1/(1+beta*gamma_p))*
(
(beta)*(PIE(1)) +(gamma_p)*(PIE(-1))
+((1-xi_p)*(1-beta*xi_p)/(xi_p))*(MC)
) + ETA_P ;
MC = alpha*R_K+(1-alpha)*W -EE_A;
W = (1/(1+beta))*(beta*W(+1)+W(-1))
+(beta/(1+beta))*(PIE(+1))
-((1+beta*gamma_w)/(1+beta))*(PIE)
+(gamma_w/(1+beta))*(PIE(-1))
-(1/(1+beta))*(((1-beta*xi_w)*(1-xi_w))/(((1+(((1+lambda_w)*sig_l)/(lambda_w))))*xi_w))*(W-sig_l*L-(sig_c/(1-hab))*(C-hab*C(-1))+EE_L)
+ETA_W;
L = R_K*((1+czcap)/czcap)-W+K ;
// R = r_dpi*(PIE-PIE(-1))
// +(1-rho)*(r_pie*(PIE(-1)-PIE_BAR)+r_y*(Y-YF))
// +r_dy*(Y-YF-(Y(-1)-YF(-1)))
// +rho*(R(-1)-PIE_BAR)
// +PIE_BAR
// +ETA_R;
R =
r_dpi*(PIE-PIE(-1))
+(1-rho)*(r_pie*(BIGTHETA)+r_y*(Y-YF))
+r_dy*(Y-YF-(Y(-1)-YF(-1)))
+rho*(R(-1)-PIE_BAR)
+PIE_BAR
+ETA_R;
E = E(-1)+E(1)-E+(L-E)*((1-xi_e)*(1-xi_e*beta)/(xi_e));
EE_A = (rho_a)*EE_A(-1) + E_A;
PIE_BAR = rho_pb*PIE_BAR(-1)+ E_PIE_BAR ;
EE_B = rho_b*EE_B(-1) + E_B ;
EE_G = rho_g*EE_G(-1) + E_G ;
EE_L = rho_l*EE_L(-1) + E_L ;
EE_I = rho_i*EE_I(-1) + E_I ;
one = 0*one(-1) ;
LMP*BIGTHETA(1) = BIGTHETA - (PIE(-1) - PIE_BAR) ;
end;
shocks;
var E_A; stderr 0.598;
var E_B; stderr 0.336;
var E_G; stderr 0.325;
var E_I; stderr 0.085;
var E_L; stderr 3.520;
var ETA_P; stderr 0.160;
var ETA_W; stderr 0.289;
var ETA_R; stderr 0.081;
var ETA_Q; stderr 0.604;
var E_PIE_BAR; stderr 0.017;
end;
//stoch_simul(irf=20) Y C PIE R W R_K L Q I K ;
// stoch_simul generates what kind of standard errors for the shocks ?
//steady;
//check;
//stoch_simul(periods=200,irf=20,simul_seed=3) Y C PIE MC R W R_K E L I ;
//datatomfile('ddd',[]);
// new syntax
estimated_params;
// PARAM NAME, INITVAL, LB, UB, PRIOR_SHAPE, PRIOR_P1, PRIOR_P2, PRIOR_P3, PRIOR_P4, JSCALE
// PRIOR_SHAPE: BETA_PDF, GAMMA_PDF, NORMAL_PDF, INV_GAMMA_PDF
stderr E_A,0.543,0.01,4,INV_GAMMA_PDF,0.4,2;
stderr E_PIE_BAR,0.072,0.001,4,INV_GAMMA_PDF,0.02,10;
stderr E_B,0.2694,0.01,4,INV_GAMMA_PDF,0.2,2;
stderr E_G,0.3052,0.01,4,INV_GAMMA_PDF,0.3,2;
stderr E_L,1.4575,0.1,6,INV_GAMMA_PDF,1,2;
stderr E_I,0.1318,0.01,4,INV_GAMMA_PDF,0.1,2;
stderr ETA_R,0.1363,0.01,4,INV_GAMMA_PDF,0.1,2;
stderr ETA_Q,0.4842,0.01,4,INV_GAMMA_PDF,0.4,2;
stderr ETA_P,0.1731,0.01,4,INV_GAMMA_PDF,0.15,2;
stderr ETA_W,0.2462,0.1,4,INV_GAMMA_PDF,0.25,2;
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 Y C I E PIE W R;
//estimation(datafile=rawdata_euromodel_1,presample=40, first_obs=1, nobs=118, lik_init=2, mode_compute=1,mh_replic=0);
estimation(datafile=rawdata_euromodel_1,presample=40, first_obs=1, nobs=118,mh_jscale=0.2,mh_replic=1000
//,mode_compute=0,mode_file=sweuromodel_dll_mode
);
//stoch_simul(periods=200,irf=20,simul_seed=3) Y C PIE R W R_K L Q I K ;