Starting Dynare (version 4.4.1).
Starting preprocessing of the model file ...
Found 40 equation(s).
Evaluating expressions...done
Computing static model derivatives:
- order 1
Computing dynamic model derivatives:
- order 1
- order 2
Processing outputs ...done
Preprocessing completed.
Starting MATLAB/Octave computing.
You did not declare endogenous variables after the estimation/calib_smoother command.
Loading 230 observations from usmodel_data.mat
Restricting the sample to observations 71 to 230. Using in total 160 observations.
Initial value of the log posterior (or likelihood): -946.8147
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f at the beginning of new iteration, 946.8147277559
Predicted improvement: 153.150201895
lambda = 1; f = 946.8191087
lambda = 0.33333; f = 946.8147387
lambda = 0.11111; f = 923.8360954
Norm of dx 0.17501
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Improvement on iteration 1 = 22.978632351
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f at the beginning of new iteration, 923.8360954050
Predicted improvement: 116.239615870
lambda = 1; f = 923.8381299
lambda = 0.33333; f = 913.8674154
lambda = 0.11111; f = 910.6333973
Norm of dx 0.20522
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Improvement on iteration 2 = 13.202698103
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f at the beginning of new iteration, 910.6333973018
Predicted improvement: 26.999194364
lambda = 1; f = 910.1939542
lambda = 0.33333; f = 898.9346085
Norm of dx 0.088241
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Improvement on iteration 3 = 11.698788781
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f at the beginning of new iteration, 898.9346085212
Predicted improvement: 5.385779607
lambda = 1; f = 892.3423092
Norm of dx 0.037175
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Improvement on iteration 4 = 6.592299275
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f at the beginning of new iteration, 892.3423092457
Predicted improvement: 5.551288261
lambda = 1; f = 887.3097788
Norm of dx 0.058313
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Improvement on iteration 5 = 5.032530426
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f at the beginning of new iteration, 887.3097788202
Predicted improvement: 1.489499709
lambda = 1; f = 885.1656132
lambda = 1.9332; f = 884.7407874
Norm of dx 0.02139
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Improvement on iteration 6 = 2.568991378
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f at the beginning of new iteration, 884.7407874418
Predicted improvement: 2.723723971
lambda = 1; f = 881.5382307
Norm of dx 0.039772
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Improvement on iteration 7 = 3.202556786
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f at the beginning of new iteration, 881.5382306555
Predicted improvement: 2.704363693
lambda = 1; f = 877.0075632
lambda = 1.9332; f = 874.5312785
Norm of dx 0.053607
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Improvement on iteration 8 = 7.006952193
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f at the beginning of new iteration, 874.5312784630
Predicted improvement: 3.437269113
lambda = 1; f = 869.2137925
lambda = 1.9332; f = 867.4064155
Norm of dx 0.093957
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Improvement on iteration 9 = 7.124862936
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f at the beginning of new iteration, 867.4064155274
Predicted improvement: 3.502334226
lambda = 1; f = 861.9556512
lambda = 1.9332; f = 859.3113565
Norm of dx 0.067979
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Improvement on iteration 10 = 8.095058980
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f at the beginning of new iteration, 859.3113565479
Predicted improvement: 2.335076337
lambda = 1; f = 855.7395064
lambda = 1.9332; f = 854.5800350
Norm of dx 0.063072
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Improvement on iteration 11 = 4.731321598
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f at the beginning of new iteration, 854.5800349501
Predicted improvement: 1.251620799
lambda = 1; f = 852.4095961
lambda = 1.9332; f = 850.9992344
lambda = 3.7372; f = 850.0633235
Norm of dx 0.039421
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Improvement on iteration 12 = 4.516711483
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f at the beginning of new iteration, 850.0633234674
Predicted improvement: 1.195326806
lambda = 1; f = 848.3486251
lambda = 1.9332; f = 847.9198906
Norm of dx 0.030863
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Improvement on iteration 13 = 2.143432882
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f at the beginning of new iteration, 847.9198905852
Predicted improvement: 0.460180758
lambda = 1; f = 847.2917655
Norm of dx 0.051336
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Improvement on iteration 14 = 0.628125111
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f at the beginning of new iteration, 847.2917654742
Predicted improvement: 0.315599948
lambda = 1; f = 846.8794546
Norm of dx 0.031842
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Improvement on iteration 15 = 0.412310854
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f at the beginning of new iteration, 846.8794546203
Predicted improvement: 0.181457590
lambda = 1; f = 846.6163341
lambda = 1.9332; f = 846.5514682
Norm of dx 0.013142
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Improvement on iteration 16 = 0.327986453
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f at the beginning of new iteration, 846.5514681672
Predicted improvement: 0.173852422
lambda = 1; f = 846.2596595
lambda = 1.9332; f = 846.0777774
lambda = 3.7372; f = 845.9300705
Norm of dx 0.0089194
----
Improvement on iteration 17 = 0.621397636
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f at the beginning of new iteration, 845.9300705309
Predicted improvement: 0.367983055
lambda = 1; f = 845.3544739
lambda = 1.9332; f = 845.1143807
Norm of dx 0.023021
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Improvement on iteration 18 = 0.815689783
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f at the beginning of new iteration, 845.1143807481
Predicted improvement: 0.196106170
lambda = 1; f = 844.8087316
lambda = 1.9332; f = 844.6822086
Norm of dx 0.028654
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Improvement on iteration 19 = 0.432172154
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f at the beginning of new iteration, 844.6822085937
Predicted improvement: 0.156515789
lambda = 1; f = 844.4242578
lambda = 1.9332; f = 844.2822888
Norm of dx 0.012877
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Improvement on iteration 20 = 0.399919824
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f at the beginning of new iteration, 844.2822887696
Predicted improvement: 0.163831843
lambda = 1; f = 844.0495333
lambda = 1.9332; f = 844.0052242
Norm of dx 0.020603
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Improvement on iteration 21 = 0.277064613
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f at the beginning of new iteration, 844.0052241562
Predicted improvement: 0.076047012
lambda = 1; f = 843.8773373
lambda = 1.9332; f = 843.8021065
Norm of dx 0.0049134
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Improvement on iteration 22 = 0.203117609
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f at the beginning of new iteration, 843.8021065473
Predicted improvement: 0.102960691
lambda = 1; f = 843.6573445
lambda = 1.9332; f = 843.6372396
Norm of dx 0.014659
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Improvement on iteration 23 = 0.164866990
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f at the beginning of new iteration, 843.6372395568
Predicted improvement: 0.056008929
lambda = 1; f = 843.5456717
lambda = 1.9332; f = 843.4970262
Norm of dx 0.0047603
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Improvement on iteration 24 = 0.140213395
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f at the beginning of new iteration, 843.4970261615
Predicted improvement: 0.071209918
lambda = 1; f = 843.3828496
lambda = 1.9332; f = 843.3259971
Norm of dx 0.014449
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Improvement on iteration 25 = 0.171029016
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f at the beginning of new iteration, 843.3259971452
Predicted improvement: 0.075827264
lambda = 1; f = 843.2078648
lambda = 1.9332; f = 843.1658534
Norm of dx 0.017463
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Improvement on iteration 26 = 0.160143757
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f at the beginning of new iteration, 843.1658533880
Predicted improvement: 0.041253034
lambda = 1; f = 843.0998527
lambda = 1.9332; f = 843.0679859
Norm of dx 0.005329
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Improvement on iteration 27 = 0.097867461
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f at the beginning of new iteration, 843.0679859270
Predicted improvement: 0.034233841
lambda = 1; f = 843.0177065
lambda = 1.9332; f = 843.0034771
Norm of dx 0.0061365
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Improvement on iteration 28 = 0.064508819
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f at the beginning of new iteration, 843.0034771076
Predicted improvement: 0.017526169
lambda = 1; f = 842.9753043
lambda = 1.9332; f = 842.9614503
Norm of dx 0.0039647
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Improvement on iteration 29 = 0.042026853
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f at the beginning of new iteration, 842.9614502549
Predicted improvement: 0.019882144
lambda = 1; f = 842.9315196
lambda = 1.9332; f = 842.9214892
Norm of dx 0.010551
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Improvement on iteration 30 = 0.039961023
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f at the beginning of new iteration, 842.9214892321
Predicted improvement: 0.019899234
lambda = 1; f = 842.8873749
lambda = 1.9332; f = 842.8656578
lambda = 3.7372; f = 842.8508759
Norm of dx 0.0049852
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Improvement on iteration 31 = 0.070613292
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f at the beginning of new iteration, 842.8508759401
Predicted improvement: 0.026397258
lambda = 1; f = 842.8056030
lambda = 1.9332; f = 842.7770403
lambda = 3.7372; f = 842.7599684
Norm of dx 0.0053499
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Improvement on iteration 32 = 0.090907578
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f at the beginning of new iteration, 842.7599683626
Predicted improvement: 0.046928485
lambda = 1; f = 842.6830424
lambda = 1.9332; f = 842.6411005
Norm of dx 0.011463
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Improvement on iteration 33 = 0.118867856
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f at the beginning of new iteration, 842.6411005064
Predicted improvement: 0.062450120
lambda = 1; f = 842.5723106
Norm of dx 0.029749
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Improvement on iteration 34 = 0.068789942
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f at the beginning of new iteration, 842.5723105645
Predicted improvement: 0.022289052
lambda = 1; f = 842.5400389
lambda = 1.9332; f = 842.5327482
Norm of dx 0.016794
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Improvement on iteration 35 = 0.039562356
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f at the beginning of new iteration, 842.5327482085
Predicted improvement: 0.019245035
lambda = 1; f = 842.5035998
lambda = 1.9332; f = 842.4929617
Norm of dx 0.0030601
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Improvement on iteration 36 = 0.039786485
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f at the beginning of new iteration, 842.4929617236
Predicted improvement: 0.007211630
lambda = 1; f = 842.4822495
lambda = 1.9332; f = 842.4788796
Norm of dx 0.0029364
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Improvement on iteration 37 = 0.014082086
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f at the beginning of new iteration, 842.4788796371
Predicted improvement: 0.002748214
lambda = 1; f = 842.4743081
lambda = 1.9332; f = 842.4716968
Norm of dx 0.0018487
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Improvement on iteration 38 = 0.007182791
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f at the beginning of new iteration, 842.4716968462
Predicted improvement: 0.003331073
lambda = 1; f = 842.4666312
lambda = 1.9332; f = 842.4647803
Norm of dx 0.0023186
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Improvement on iteration 39 = 0.006916539
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f at the beginning of new iteration, 842.4647803068
Predicted improvement: 0.002813644
lambda = 1; f = 842.4595791
lambda = 1.9332; f = 842.4555026
lambda = 3.7372; f = 842.4497651
lambda = 7.2247; f = 842.4467882
Norm of dx 0.0018051
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Improvement on iteration 40 = 0.017992067
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f at the beginning of new iteration, 842.4467882395
Predicted improvement: 0.007982745
lambda = 1; f = 842.4327052
lambda = 1.9332; f = 842.4229672
lambda = 3.7372; f = 842.4134688
Norm of dx 0.0030234
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Improvement on iteration 41 = 0.033319482
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f at the beginning of new iteration, 842.4134687570
Predicted improvement: 0.011371990
lambda = 1; f = 842.3958096
lambda = 1.9332; f = 842.3885097
Norm of dx 0.012017
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Improvement on iteration 42 = 0.024959038
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f at the beginning of new iteration, 842.3885097185
Predicted improvement: 0.006743973
lambda = 1; f = 842.3773117
lambda = 1.9332; f = 842.3709976
Norm of dx 0.0067663
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Improvement on iteration 43 = 0.017512118
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f at the beginning of new iteration, 842.3709976002
Predicted improvement: 0.008894341
lambda = 1; f = 842.3580356
lambda = 1.9332; f = 842.3546553
Norm of dx 0.0066614
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Improvement on iteration 44 = 0.016342326
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f at the beginning of new iteration, 842.3546552743
Predicted improvement: 0.003713666
lambda = 1; f = 842.3485918
lambda = 1.9332; f = 842.3453770
Norm of dx 0.002023
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Improvement on iteration 45 = 0.009278269
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f at the beginning of new iteration, 842.3453770057
Predicted improvement: 0.003362520
lambda = 1; f = 842.3398879
lambda = 1.9332; f = 842.3369684
Norm of dx 0.0035383
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Improvement on iteration 46 = 0.008408560
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f at the beginning of new iteration, 842.3369684457
Predicted improvement: 0.005502386
lambda = 1; f = 842.3274564
lambda = 1.9332; f = 842.3212359
lambda = 3.7372; f = 842.3164346
Norm of dx 0.0069679
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Improvement on iteration 47 = 0.020533851
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f at the beginning of new iteration, 842.3164345945
Predicted improvement: 0.009048522
lambda = 1; f = 842.3005607
lambda = 1.9332; f = 842.2897501
lambda = 3.7372; f = 842.2797943
Norm of dx 0.0051434
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Improvement on iteration 48 = 0.036640294
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f at the beginning of new iteration, 842.2797943004
Predicted improvement: 0.011756803
lambda = 1; f = 842.2580271
lambda = 1.9332; f = 842.2408565
lambda = 3.7372; f = 842.2162571
lambda = 7.2247; f = 842.2007990
Norm of dx 0.010842
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Improvement on iteration 49 = 0.078995274
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f at the beginning of new iteration, 842.2007990269
Predicted improvement: 0.017059627
lambda = 1; f = 842.1770887
Norm of dx 0.0068018
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Improvement on iteration 50 = 0.023710283
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f at the beginning of new iteration, 842.1770887440
Predicted improvement: 0.007377384
lambda = 1; f = 842.1677217
Norm of dx 0.0092663
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Improvement on iteration 51 = 0.009367014
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f at the beginning of new iteration, 842.1677217297
Predicted improvement: 0.002537375
lambda = 1; f = 842.1637259
lambda = 1.9332; f = 842.1618933
Norm of dx 0.0063631
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Improvement on iteration 52 = 0.005828479
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f at the beginning of new iteration, 842.1618932512
Predicted improvement: 0.002618985
lambda = 1; f = 842.1571917
lambda = 1.9332; f = 842.1537766
lambda = 3.7372; f = 842.1498403
Norm of dx 0.004315
----
Improvement on iteration 53 = 0.012052985
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f at the beginning of new iteration, 842.1498402659
Predicted improvement: 0.007189756
lambda = 1; f = 842.1372029
lambda = 1.9332; f = 842.1286760
lambda = 3.7372; f = 842.1216667
Norm of dx 0.0062651
----
Improvement on iteration 54 = 0.028173555
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f at the beginning of new iteration, 842.1216667109
Predicted improvement: 0.012105250
lambda = 1; f = 842.0999607
lambda = 1.9332; f = 842.0843503
lambda = 3.7372; f = 842.0675155
Norm of dx 0.0075398
----
Improvement on iteration 55 = 0.054151217
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f at the beginning of new iteration, 842.0675154941
Predicted improvement: 0.029238628
lambda = 1; f = 842.0234309
lambda = 1.9332; f = 842.0080766
Norm of dx 0.037256
----
Improvement on iteration 56 = 0.059438864
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f at the beginning of new iteration, 842.0080766297
Predicted improvement: 0.015555604
lambda = 1; f = 841.9869904
Norm of dx 0.022446
----
Improvement on iteration 57 = 0.021086274
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f at the beginning of new iteration, 841.9869903560
Predicted improvement: 0.005938191
lambda = 1; f = 841.9789574
Norm of dx 0.0091673
----
Improvement on iteration 58 = 0.008032938
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f at the beginning of new iteration, 841.9789574178
Predicted improvement: 0.004174695
lambda = 1; f = 841.9718342
lambda = 1.9332; f = 841.9674072
lambda = 3.7372; f = 841.9649371
Norm of dx 0.0065664
----
Improvement on iteration 59 = 0.014020337
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f at the beginning of new iteration, 841.9649370809
Predicted improvement: 0.009712015
lambda = 1; f = 841.9459662
lambda = 1.9332; f = 841.9290755
lambda = 3.7372; f = 841.8986457
lambda = 7.2247; f = 841.8481235
lambda = 13.967; f = 841.7814623
Norm of dx 0.0069449
----
Improvement on iteration 60 = 0.183474808
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f at the beginning of new iteration, 841.7814622734
Predicted improvement: 0.117862186
lambda = 1; f = 841.6125811
lambda = 1.9332; f = 841.5758833
Norm of dx 0.16503
----
Improvement on iteration 61 = 0.205578982
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f at the beginning of new iteration, 841.5758832918
Predicted improvement: 0.031876573
lambda = 1; f = 841.5335217
Norm of dx 0.01811
----
Improvement on iteration 62 = 0.042361613
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f at the beginning of new iteration, 841.5335216791
Predicted improvement: 0.009195267
lambda = 1; f = 841.5217216
Norm of dx 0.016789
----
Improvement on iteration 63 = 0.011800051
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f at the beginning of new iteration, 841.5217216280
Predicted improvement: 0.004566884
lambda = 1; f = 841.5138204
lambda = 1.9332; f = 841.5086346
lambda = 3.7372; f = 841.5045534
Norm of dx 0.01031
----
Improvement on iteration 64 = 0.017168204
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f at the beginning of new iteration, 841.5045534239
Predicted improvement: 0.011569419
lambda = 1; f = 841.4824074
lambda = 1.9332; f = 841.4635092
lambda = 3.7372; f = 841.4317778
lambda = 7.2247; f = 841.3881724
Norm of dx 0.015034
----
Improvement on iteration 65 = 0.116381034
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f at the beginning of new iteration, 841.3881723897
Predicted improvement: 0.057066763
lambda = 1; f = 841.3186785
Norm of dx 0.067112
----
Improvement on iteration 66 = 0.069493843
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f at the beginning of new iteration, 841.3186785463
Predicted improvement: 0.018323864
lambda = 1; f = 841.2924681
lambda = 1.9332; f = 841.2870911
Norm of dx 0.020568
----
Improvement on iteration 67 = 0.031587409
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f at the beginning of new iteration, 841.2870911377
Predicted improvement: 0.006411683
lambda = 1; f = 841.2787857
Norm of dx 0.019904
----
Improvement on iteration 68 = 0.008305477
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f at the beginning of new iteration, 841.2787856605
Predicted improvement: 0.001901742
lambda = 1; f = 841.2761186
lambda = 1.9332; f = 841.2757328
Norm of dx 0.0034507
----
Improvement on iteration 69 = 0.003052886
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f at the beginning of new iteration, 841.2757327742
Predicted improvement: 0.001394160
lambda = 1; f = 841.2731191
lambda = 1.9332; f = 841.2709825
lambda = 3.7372; f = 841.2676697
lambda = 7.2247; f = 841.2642634
Norm of dx 0.0031323
----
Improvement on iteration 70 = 0.011469344
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f at the beginning of new iteration, 841.2642634306
Predicted improvement: 0.007343858
lambda = 1; f = 841.2520407
lambda = 1.9332; f = 841.2450877
Norm of dx 0.015275
----
Improvement on iteration 71 = 0.019175774
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f at the beginning of new iteration, 841.2450876563
Predicted improvement: 0.004055380
lambda = 1; f = 841.2408296
Norm of dx 0.0057579
----
Improvement on iteration 72 = 0.004258093
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f at the beginning of new iteration, 841.2408295635
Predicted improvement: 0.000138341
lambda = 1; f = 841.2406023
lambda = 1.9332; f = 841.2404834
Norm of dx 0.0016444
----
Improvement on iteration 73 = 0.000346121
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f at the beginning of new iteration, 841.2404834424
Predicted improvement: 0.000218804
lambda = 1; f = 841.2400917
lambda = 1.9332; f = 841.2398119
lambda = 3.7372; f = 841.2395063
Norm of dx 0.0033504
----
Improvement on iteration 74 = 0.000977169
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f at the beginning of new iteration, 841.2395062729
Predicted improvement: 0.000735774
lambda = 1; f = 841.2381611
lambda = 1.9332; f = 841.2371317
lambda = 3.7372; f = 841.2357592
Norm of dx 0.0016817
----
Improvement on iteration 75 = 0.003747094
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f at the beginning of new iteration, 841.2357591787
Predicted improvement: 0.001835757
lambda = 1; f = 841.2334886
Norm of dx 0.011018
----
Improvement on iteration 76 = 0.002270579
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f at the beginning of new iteration, 841.2334885998
Predicted improvement: 0.000285602
lambda = 1; f = 841.2331363
Norm of dx 0.0064945
----
Improvement on iteration 77 = 0.000352315
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f at the beginning of new iteration, 841.2331362845
Predicted improvement: 0.000089187
lambda = 1; f = 841.2329844
lambda = 1.9332; f = 841.2328897
lambda = 3.7372; f = 841.2328350
Norm of dx 0.0017564
----
Improvement on iteration 78 = 0.000301252
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f at the beginning of new iteration, 841.2328350320
Predicted improvement: 0.000201966
lambda = 1; f = 841.2324448
lambda = 1.9332; f = 841.2321042
lambda = 3.7372; f = 841.2315106
lambda = 7.2247; f = 841.2306051
lambda = 13.967; f = 841.2297611
Norm of dx 0.00068528
----
Improvement on iteration 79 = 0.003073925
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-----------------
f at the beginning of new iteration, 841.2297611067
Predicted improvement: 0.000739486
lambda = 1; f = 841.2288295
Norm of dx 0.0046442
----
Improvement on iteration 80 = 0.000931596
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-----------------
f at the beginning of new iteration, 841.2288295110
Predicted improvement: 0.000113859
lambda = 1; f = 841.2286865
Norm of dx 0.0029158
----
Improvement on iteration 81 = 0.000142996
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-----------------
f at the beginning of new iteration, 841.2286865150
Predicted improvement: 0.000056626
lambda = 1; f = 841.2285872
lambda = 1.9332; f = 841.2285177
lambda = 3.7372; f = 841.2284468
Norm of dx 0.0011151
----
Improvement on iteration 82 = 0.000239753
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f at the beginning of new iteration, 841.2284467622
Predicted improvement: 0.000196941
lambda = 1; f = 841.2280615
lambda = 1.9332; f = 841.2277158
lambda = 3.7372; f = 841.2270854
lambda = 7.2247; f = 841.2260079
lambda = 13.967; f = 841.2244524
lambda = 27; f = 841.2234119
Norm of dx 0.0014718
----
Improvement on iteration 83 = 0.005034861
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f at the beginning of new iteration, 841.2234119016
Predicted improvement: 0.003755469
lambda = 1; f = 841.2167556
lambda = 1.9332; f = 841.2120918
lambda = 3.7372; f = 841.2073158
Norm of dx 0.010528
----
Improvement on iteration 84 = 0.016096064
-----------------
-----------------
f at the beginning of new iteration, 841.2073158373
Predicted improvement: 0.000839937
lambda = 1; f = 841.2061656
Norm of dx 0.013594
----
Improvement on iteration 85 = 0.001150234
-----------------
-----------------
f at the beginning of new iteration, 841.2061656035
Predicted improvement: 0.000684762
lambda = 1; f = 841.2049889
lambda = 1.9332; f = 841.2042358
lambda = 3.7372; f = 841.2037230
Norm of dx 0.0099576
----
Improvement on iteration 86 = 0.002442611
-----------------
-----------------
f at the beginning of new iteration, 841.2037229925
Predicted improvement: 0.001737388
lambda = 1; f = 841.2002841
lambda = 1.9332; f = 841.1971412
lambda = 3.7372; f = 841.1912465
lambda = 7.2247; f = 841.1805277
lambda = 13.967; f = 841.1623340
lambda = 27; f = 841.1366012
lambda = 52.196; f = 841.1221259
Norm of dx 0.0048602
----
Improvement on iteration 87 = 0.081597066
-----------------
-----------------
f at the beginning of new iteration, 841.1221259268
Predicted improvement: 0.012272152
lambda = 1; f = 841.1078724
Norm of dx 0.079717
----
Improvement on iteration 88 = 0.014253490
-----------------
-----------------
f at the beginning of new iteration, 841.1078724370
Predicted improvement: 0.000750780
lambda = 1; f = 841.1068392
Norm of dx 0.020483
----
Improvement on iteration 89 = 0.001033234
-----------------
-----------------
f at the beginning of new iteration, 841.1068392028
Predicted improvement: 0.000607290
lambda = 1; f = 841.1058059
lambda = 1.9332; f = 841.1051691
lambda = 3.7372; f = 841.1048362
Norm of dx 0.013632
----
Improvement on iteration 90 = 0.002002999
-----------------
-----------------
f at the beginning of new iteration, 841.1048362034
Predicted improvement: 0.001368727
lambda = 1; f = 841.1022067
lambda = 1.9332; f = 841.0999451
lambda = 3.7372; f = 841.0961012
lambda = 7.2247; f = 841.0906603
lambda = 13.967; f = 841.0876976
Norm of dx 0.0030617
----
Improvement on iteration 91 = 0.017138643
-----------------
-----------------
f at the beginning of new iteration, 841.0876975604
Predicted improvement: 0.004413567
lambda = 1; f = 841.0821702
Norm of dx 0.061521
----
Improvement on iteration 92 = 0.005527353
-----------------
-----------------
f at the beginning of new iteration, 841.0821702073
Predicted improvement: 0.000406763
lambda = 1; f = 841.0817635
Norm of dx 0.02012
----
Improvement on iteration 93 = 0.000406717
-----------------
-----------------
f at the beginning of new iteration, 841.0817634899
Predicted improvement: 0.000005210
lambda = 1; f = 841.0817534
lambda = 1.9332; f = 841.0817443
lambda = 3.7372; f = 841.0817277
lambda = 7.2247; f = 841.0816993
lambda = 13.967; f = 841.0816578
lambda = 27; f = 841.0816278
Norm of dx 0.00018287
----
Improvement on iteration 94 = 0.000135646
-----------------
-----------------
f at the beginning of new iteration, 841.0816278436
Predicted improvement: 0.000098897
lambda = 1; f = 841.0814522
lambda = 1.9332; f = 841.0813270
lambda = 3.7372; f = 841.0811913
Norm of dx 0.00088232
----
Improvement on iteration 95 = 0.000436518
-----------------
-----------------
f at the beginning of new iteration, 841.0811913258
Predicted improvement: 0.000053608
lambda = 1; f = 841.0811368
Norm of dx 0.0012208
----
Improvement on iteration 96 = 0.000054539
-----------------
-----------------
f at the beginning of new iteration, 841.0811367868
Predicted improvement: 0.000000475
lambda = 1; f = 841.0811362
Norm of dx 0.00028881
----
Improvement on iteration 97 = 0.000000613
-----------------
-----------------
f at the beginning of new iteration, 841.0811361737
Predicted improvement: 0.000000112
lambda = 1; f = 841.0811360
lambda = 1.9332; f = 841.0811358
lambda = 3.7372; f = 841.0811355
lambda = 7.2247; f = 841.0811354
Norm of dx 8.5996e-005
----
Improvement on iteration 98 = 0.000000809
-----------------
-----------------
f at the beginning of new iteration, 841.0811353649
Predicted improvement: 0.000000640
lambda = 1; f = 841.0811341
lambda = 1.9332; f = 841.0811330
lambda = 3.7372; f = 841.0811311
lambda = 7.2247; f = 841.0811281
lambda = 13.967; f = 841.0811246
Norm of dx 0.00010607
----
Improvement on iteration 99 = 0.000010726
-----------------
-----------------
f at the beginning of new iteration, 841.0811246387
Predicted improvement: 0.000002361
lambda = 1; f = 841.0811222
Norm of dx 0.00074461
----
Improvement on iteration 100 = 0.000002443
-----------------
-----------------
f at the beginning of new iteration, 841.0811221959
Predicted improvement: 0.000000071
lambda = 1; f = 841.0811222
lambda = 0.33333; f = 841.0811222
Norm of dx 0.00021663
----
Improvement on iteration 101 = 0.000000031
improvement < crit termination
Objective function at mode: 841.081122
RESULTS FROM POSTERIOR ESTIMATION
parameters
prior mean mode s.d. prior pstdev
crhoa 0.500 0.9607 1.0000 beta 0.2000
crhob 0.500 0.1833 1.0000 beta 0.2000
crhog 0.500 0.9761 1.0000 beta 0.2000
crhoqs 0.500 0.7032 1.0000 beta 0.2000
crhoms 0.500 0.1227 1.0000 beta 0.2000
crhopinf 0.500 0.9078 1.0000 beta 0.2000
crhow 0.500 0.9743 1.0000 beta 0.2000
cmap 0.500 0.7438 1.0000 beta 0.2000
cmaw 0.500 0.8928 1.0000 beta 0.2000
csadjcost 4.000 5.4879 1.0000 norm 1.5000
csigma 1.500 1.4219 1.0000 norm 0.3750
chabb 0.700 0.7063 1.0000 beta 0.1000
cprobw 0.500 0.7343 1.0000 beta 0.1000
csigl 2.000 1.8748 1.0000 norm 0.7500
cprobp 0.500 0.6542 1.0000 beta 0.1000
cindw 0.500 0.5983 1.0000 beta 0.1500
cindp 0.500 0.2186 1.0000 beta 0.1500
czcap 0.500 0.5453 1.0000 beta 0.1500
cfc 1.250 1.6097 1.0000 norm 0.1250
crpi 1.500 2.0216 1.0000 norm 0.2500
crr 0.750 0.8145 1.0000 beta 0.1000
cry 0.125 0.0881 1.0000 norm 0.0500
crdy 0.125 0.2223 1.0000 norm 0.0500
constepinf 0.625 0.7652 1.0000 gamm 0.1000
constebeta 0.250 0.1444 1.0000 gamm 0.1000
constelab 0.000 0.7261 1.0000 norm 2.0000
ctrend 0.400 0.4344 1.0000 norm 0.1000
cgy 0.500 0.5232 1.0000 norm 0.2500
calfa 0.300 0.1910 1.0000 norm 0.0500
standard deviation of shocks
prior mean mode s.d. prior pstdev
ea 0.100 0.4529 1.0000 invg 2.0000
eb 0.100 0.2416 1.0000 invg 2.0000
eg 0.100 0.5213 1.0000 invg 2.0000
eqs 0.100 0.4552 1.0000 invg 2.0000
em 0.100 0.2389 1.0000 invg 2.0000
epinf 0.100 0.1398 1.0000 invg 2.0000
ew 0.100 0.2465 1.0000 invg 2.0000
Log data density [Laplace approximation] is NaN.
Estimation::mcmc: Multiple chains mode.
Estimation::mcmc: Searching for initial values...
Estimation::mcmc: I couldn't get a valid initial value in 100 trials.
Estimation::mcmc: You should Reduce mh_init_scale...
Estimation::mcmc: Parameter mh_init_scale is equal to 0.400000.
Estimation::mcmc: Enter a new value... .2
Estimation::mcmc: I couldn't get a valid initial value in 100 trials.
Estimation::mcmc: You should Reduce mh_init_scale...
Estimation::mcmc: Parameter mh_init_scale is equal to 0.200000.
Estimation::mcmc: Enter a new value... .1
Estimation::mcmc: I couldn't get a valid initial value in 100 trials.
Estimation::mcmc: You should Reduce mh_init_scale...
Estimation::mcmc: Parameter mh_init_scale is equal to 0.100000.
Estimation::mcmc: Enter a new value... .09
Estimation::mcmc: I couldn't get a valid initial value in 100 trials.
Estimation::mcmc: You should Reduce mh_init_scale...
Estimation::mcmc: Parameter mh_init_scale is equal to 0.090000.
Estimation::mcmc: Enter a new value... .08
Estimation::mcmc: I couldn't get a valid initial value in 100 trials.
Estimation::mcmc: You should Reduce mh_init_scale...
Estimation::mcmc: Parameter mh_init_scale is equal to 0.080000.
Estimation::mcmc: Enter a new value... .07
Estimation::mcmc: Initial values found!
Estimation::mcmc: Write details about the MCMC... Ok!
Estimation::mcmc: Details about the MCMC are available in usmodel/metropolis\usmodel_mh_history_0.mat
Estimation::mcmc: Number of mh files: 16 per block.
Estimation::mcmc: Total number of generated files: 32.
Estimation::mcmc: Total number of iterations: 50000.
Estimation::mcmc: Current acceptance ratio per chain:
Chain 1: 0.0059999%
Chain 2: 0.002%
Estimation::mcmc::diagnostics: Univariate convergence diagnostic, Brooks and Gelman (1998):
Parameter 1... Done!
Parameter 2... Done!
Parameter 3... Done!
Parameter 4... Done!
Parameter 5... Done!
Parameter 6... Done!
Parameter 7... Done!
Parameter 8... Done!
Parameter 9... Done!
Parameter 10... Done!
Parameter 11... Done!
Parameter 12... Done!
Parameter 13... Done!
Parameter 14... Done!
Parameter 15... Done!
Parameter 16... Done!
Parameter 17... Done!
Parameter 18... Done!
Parameter 19... Done!
Parameter 20... Done!
Parameter 21... Done!
Parameter 22... Done!
Parameter 23... Done!
Parameter 24... Done!
Parameter 25... Done!
Parameter 26... Done!
Parameter 27... Done!
Parameter 28... Done!
Parameter 29... Done!
Parameter 30... Done!
Parameter 31... Done!
Parameter 32... Done!
Parameter 33... Done!
Parameter 34... Done!
Parameter 35... Done!
Parameter 36... Done!
Estimation::mcmc: Total number of MH draws: 50000.
Estimation::mcmc: Total number of generated MH files: 16.
Estimation::mcmc: I'll use mh-files 4 to 16.
Estimation::mcmc: In MH-file number 4 I'll start at line 130.
Estimation::mcmc: Finally I keep 40000 draws.
Estimation::marginal density: I'm computing the posterior mean and covariance... {Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.226758e-018.}
> In compute_mh_covariance_matrix at 77
In marginal_density at 53
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.226758e-018.}
> In marginal_density at 59
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Done!
Estimation::marginal density: I'm computing the posterior log marginal density (modified harmonic mean)... {Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.226758e-018.}
> In marginal_density at 69
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: The support of the weighting density function is not large enough...
Estimation::marginal density: I increase the variance of this distribution.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.686684e-018.}
> In marginal_density at 105
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.757054e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.871477e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.964258e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.908933e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.303311e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.128949e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.680215e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 2.038151e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.059808e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 2.023398e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.527259e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.898738e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 9.740733e-019.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.511000e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.055946e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: Let me try again.
{Warning: Matrix is close to singular or badly scaled.
Results may be inaccurate. RCOND = 1.436853e-018.}
> In marginal_density at 111
In dynare_estimation_1 at 816
In dynare_estimation at 84
In usmodel at 502
In dynare at 174
Estimation::marginal density: There's probably a problem with the modified harmonic mean estimator.
ESTIMATION RESULTS
Log data density is -Inf.
parameters
prior mean post. mean 90% HPD interval prior pstdev
crhoa 0.500 0.9461 0.8217 0.9968 beta 0.2000
crhob 0.500 0.3321 0.1157 0.4465 beta 0.2000
crhog 0.500 0.8737 0.8133 0.8882 beta 0.2000
crhoqs 0.500 0.7417 0.5683 0.9453 beta 0.2000
crhoms 0.500 0.2869 0.1959 0.3486 beta 0.2000
crhopinf 0.500 0.7749 0.5101 0.8593 beta 0.2000
crhow 0.500 0.8822 0.6486 0.9460 beta 0.2000
cmap 0.500 0.4199 0.1859 0.5571 beta 0.2000
cmaw 0.500 0.8608 0.8507 0.9482 beta 0.2000
csadjcost 4.000 5.7940 5.6989 6.2229 norm 1.5000
csigma 1.500 1.6800 1.3484 1.8253 norm 0.3750
chabb 0.700 0.6900 0.6238 0.7925 beta 0.1000
cprobw 0.500 0.8936 0.8656 0.9302 beta 0.1000
csigl 2.000 1.8478 1.6162 2.2919 norm 0.7500
cprobp 0.500 0.6878 0.5669 0.8041 beta 0.1000
cindw 0.500 0.3650 0.0413 0.5980 beta 0.1500
cindp 0.500 0.4778 0.4566 0.5427 beta 0.1500
czcap 0.500 0.5450 0.2663 0.7359 beta 0.1500
cfc 1.250 1.4734 1.3106 1.6960 norm 0.1250
crpi 1.500 2.0886 1.7026 2.3120 norm 0.2500
crr 0.750 0.8646 0.6729 0.9592 beta 0.1000
cry 0.125 0.1558 0.1034 0.3922 norm 0.0500
crdy 0.125 0.0847 0.0096 0.1095 norm 0.0500
constepinf 0.625 0.9195 0.8488 1.1775 gamma 0.1000
constebeta 0.250 0.0942 0.0234 0.1236 gamma 0.1000
constelab 0.000 0.8060 0.6412 0.8922 norm 2.0000
ctrend 0.400 0.4511 0.1207 0.5326 norm 0.1000
cgy 0.500 0.5215 0.3500 0.5788 norm 0.2500
calfa 0.300 0.2501 0.2131 0.2995 norm 0.0500
standard deviation of shocks
prior mean post. mean 90% HPD interval prior pstdev
ea 0.100 0.5283 0.5089 0.5765 invg 2.0000
eb 0.100 0.3601 0.1887 0.5553 invg 2.0000
eg 0.100 0.4564 0.2898 0.5356 invg 2.0000
eqs 0.100 0.5219 0.4355 0.7732 invg 2.0000
em 0.100 0.5475 0.2909 0.9348 invg 2.0000
epinf 0.100 0.4888 0.3023 0.5967 invg 2.0000
ew 0.100 0.4010 0.3199 0.5724 invg 2.0000
Total computing time : 0h26m30s
Note: warning(s) encountered in MATLAB/Octave code