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 ----------------- ----------------- 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 ---- Improvement on iteration 1 = 22.978632351 ----------------- ----------------- 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 ---- Improvement on iteration 2 = 13.202698103 ----------------- ----------------- 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 ---- Improvement on iteration 3 = 11.698788781 ----------------- ----------------- f at the beginning of new iteration, 898.9346085212 Predicted improvement: 5.385779607 lambda = 1; f = 892.3423092 Norm of dx 0.037175 ---- Improvement on iteration 4 = 6.592299275 ----------------- ----------------- f at the beginning of new iteration, 892.3423092457 Predicted improvement: 5.551288261 lambda = 1; f = 887.3097788 Norm of dx 0.058313 ---- Improvement on iteration 5 = 5.032530426 ----------------- ----------------- 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 ---- Improvement on iteration 6 = 2.568991378 ----------------- ----------------- f at the beginning of new iteration, 884.7407874418 Predicted improvement: 2.723723971 lambda = 1; f = 881.5382307 Norm of dx 0.039772 ---- Improvement on iteration 7 = 3.202556786 ----------------- ----------------- 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 ---- Improvement on iteration 8 = 7.006952193 ----------------- ----------------- 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 ---- Improvement on iteration 9 = 7.124862936 ----------------- ----------------- 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 ---- Improvement on iteration 10 = 8.095058980 ----------------- ----------------- 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 ---- Improvement on iteration 11 = 4.731321598 ----------------- ----------------- 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 ---- Improvement on iteration 12 = 4.516711483 ----------------- ----------------- 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 ---- Improvement on iteration 13 = 2.143432882 ----------------- ----------------- f at the beginning of new iteration, 847.9198905852 Predicted improvement: 0.460180758 lambda = 1; f = 847.2917655 Norm of dx 0.051336 ---- Improvement on iteration 14 = 0.628125111 ----------------- ----------------- f at the beginning of new iteration, 847.2917654742 Predicted improvement: 0.315599948 lambda = 1; f = 846.8794546 Norm of dx 0.031842 ---- Improvement on iteration 15 = 0.412310854 ----------------- ----------------- 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 ---- Improvement on iteration 16 = 0.327986453 ----------------- ----------------- 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 ----------------- ----------------- 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 ---- Improvement on iteration 18 = 0.815689783 ----------------- ----------------- 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 ---- Improvement on iteration 19 = 0.432172154 ----------------- ----------------- 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 ---- Improvement on iteration 20 = 0.399919824 ----------------- ----------------- 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 ---- Improvement on iteration 21 = 0.277064613 ----------------- ----------------- 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 ---- Improvement on iteration 22 = 0.203117609 ----------------- ----------------- 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 ---- Improvement on iteration 23 = 0.164866990 ----------------- ----------------- 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 ---- Improvement on iteration 24 = 0.140213395 ----------------- ----------------- 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 ---- Improvement on iteration 25 = 0.171029016 ----------------- ----------------- 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 ---- Improvement on iteration 26 = 0.160143757 ----------------- ----------------- 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 ---- Improvement on iteration 27 = 0.097867461 ----------------- ----------------- 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 ---- Improvement on iteration 28 = 0.064508819 ----------------- ----------------- 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 ---- Improvement on iteration 29 = 0.042026853 ----------------- ----------------- 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 ---- Improvement on iteration 30 = 0.039961023 ----------------- ----------------- 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 ---- Improvement on iteration 31 = 0.070613292 ----------------- ----------------- 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 ---- Improvement on iteration 32 = 0.090907578 ----------------- ----------------- 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 ---- Improvement on iteration 33 = 0.118867856 ----------------- ----------------- f at the beginning of new iteration, 842.6411005064 Predicted improvement: 0.062450120 lambda = 1; f = 842.5723106 Norm of dx 0.029749 ---- Improvement on iteration 34 = 0.068789942 ----------------- ----------------- 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 ---- Improvement on iteration 35 = 0.039562356 ----------------- ----------------- 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 ---- Improvement on iteration 36 = 0.039786485 ----------------- ----------------- 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 ---- Improvement on iteration 37 = 0.014082086 ----------------- ----------------- 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 ---- Improvement on iteration 38 = 0.007182791 ----------------- ----------------- 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 ---- Improvement on iteration 39 = 0.006916539 ----------------- ----------------- 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 ---- Improvement on iteration 40 = 0.017992067 ----------------- ----------------- 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 ---- Improvement on iteration 41 = 0.033319482 ----------------- ----------------- 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 ---- Improvement on iteration 42 = 0.024959038 ----------------- ----------------- 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 ---- Improvement on iteration 43 = 0.017512118 ----------------- ----------------- 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 ---- Improvement on iteration 44 = 0.016342326 ----------------- ----------------- 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 ---- Improvement on iteration 45 = 0.009278269 ----------------- ----------------- 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 ---- Improvement on iteration 46 = 0.008408560 ----------------- ----------------- 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 ---- Improvement on iteration 47 = 0.020533851 ----------------- ----------------- 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 ---- Improvement on iteration 48 = 0.036640294 ----------------- ----------------- 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 ---- Improvement on iteration 49 = 0.078995274 ----------------- ----------------- f at the beginning of new iteration, 842.2007990269 Predicted improvement: 0.017059627 lambda = 1; f = 842.1770887 Norm of dx 0.0068018 ---- Improvement on iteration 50 = 0.023710283 ----------------- ----------------- f at the beginning of new iteration, 842.1770887440 Predicted improvement: 0.007377384 lambda = 1; f = 842.1677217 Norm of dx 0.0092663 ---- Improvement on iteration 51 = 0.009367014 ----------------- ----------------- 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 ---- Improvement on iteration 52 = 0.005828479 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 ----------------- ----------------- 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 0.2000 beta 0.2000 crhob 0.500 0.1833 0.2000 beta 0.2000 crhog 0.500 0.9761 0.2000 beta 0.2000 crhoqs 0.500 0.7032 0.2000 beta 0.2000 crhoms 0.500 0.1227 0.2000 beta 0.2000 crhopinf 0.500 0.9078 0.2000 beta 0.2000 crhow 0.500 0.9743 0.2000 beta 0.2000 cmap 0.500 0.7438 0.2000 beta 0.2000 cmaw 0.500 0.8928 0.2000 beta 0.2000 csadjcost 4.000 5.4879 1.5000 norm 1.5000 csigma 1.500 1.4219 0.3750 norm 0.3750 chabb 0.700 0.7063 0.1000 beta 0.1000 cprobw 0.500 0.7343 0.1000 beta 0.1000 csigl 2.000 1.8748 0.7500 norm 0.7500 cprobp 0.500 0.6542 0.1000 beta 0.1000 cindw 0.500 0.5983 0.1500 beta 0.1500 cindp 0.500 0.2186 0.1500 beta 0.1500 czcap 0.500 0.5453 0.1500 beta 0.1500 cfc 1.250 1.6097 0.1250 norm 0.1250 crpi 1.500 2.0216 0.2500 norm 0.2500 crr 0.750 0.8145 0.1000 beta 0.1000 cry 0.125 0.0881 0.0500 norm 0.0500 crdy 0.125 0.2223 0.0500 norm 0.0500 constepinf 0.625 0.7652 0.1000 gamm 0.1000 constebeta 0.250 0.1444 0.1000 gamm 0.1000 constelab 0.000 0.7261 2.0000 norm 2.0000 ctrend 0.400 0.4344 0.1000 norm 0.1000 cgy 0.500 0.5232 0.2500 norm 0.2500 calfa 0.300 0.1910 0.0500 norm 0.0500 standard deviation of shocks prior mean mode s.d. prior pstdev ea 0.100 0.4529 2.0000 invg 2.0000 eb 0.100 0.2416 2.0000 invg 2.0000 eg 0.100 0.5213 2.0000 invg 2.0000 eqs 0.100 0.4552 2.0000 invg 2.0000 em 0.100 0.2389 2.0000 invg 2.0000 epinf 0.100 0.1398 2.0000 invg 2.0000 ew 0.100 0.2465 2.0000 invg 2.0000 Log data density [Laplace approximation] is -853.450828. Estimation::mcmc: Multiple chains mode. Estimation::mcmc: Searching for initial values... 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: 0.035999% Chain 2: 0.0079998% 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 = 1.977987e-020.} > 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 = 1.977987e-020.} > 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 = 1.977987e-020.} > 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 = 3.667293e-020.} > 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 = 1.672067e-020.} > 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 = 2.971857e-020.} > 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 = 2.672842e-020.} > 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 = 3.868636e-020.} > 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 = 1.861621e-020.} > 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 = 3.579866e-020.} > 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 = 2.186969e-020.} > 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 = 2.315592e-020.} > 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 = 2.295485e-020.} > 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 = 2.230408e-020.} > 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 = 2.767664e-020.} > 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 = 2.968261e-020.} > 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 = 2.189125e-020.} > 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 = 2.237705e-020.} > 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 = 3.095939e-020.} > 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 = 2.745182e-020.} > 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 0.9146 0.8928 0.9242 beta 0.2000 crhob 0.500 0.1714 0.1431 0.1965 beta 0.2000 crhog 0.500 0.8608 0.7951 0.9347 beta 0.2000 crhoqs 0.500 0.5762 0.4941 0.6422 beta 0.2000 crhoms 0.500 0.3356 0.3217 0.3802 beta 0.2000 crhopinf 0.500 0.8590 0.8065 0.9405 beta 0.2000 crhow 0.500 0.9396 0.8842 0.9790 beta 0.2000 cmap 0.500 0.7438 0.6766 0.8469 beta 0.2000 cmaw 0.500 0.8098 0.6795 0.9188 beta 0.2000 csadjcost 4.000 6.4110 5.4378 6.9728 norm 1.5000 csigma 1.500 1.4729 1.0523 1.9301 norm 0.3750 chabb 0.700 0.7051 0.6116 0.8220 beta 0.1000 cprobw 0.500 0.7413 0.7186 0.7592 beta 0.1000 csigl 2.000 2.4756 1.0977 3.8717 norm 0.7500 cprobp 0.500 0.6613 0.6256 0.6826 beta 0.1000 cindw 0.500 0.6585 0.6339 0.6873 beta 0.1500 cindp 0.500 0.2965 0.1077 0.4973 beta 0.1500 czcap 0.500 0.5981 0.4958 0.6754 beta 0.1500 cfc 1.250 1.6262 1.5393 1.7131 norm 0.1250 crpi 1.500 2.1724 2.0499 2.3099 norm 0.2500 crr 0.750 0.8238 0.8144 0.8451 beta 0.1000 cry 0.125 0.1220 0.0906 0.1480 norm 0.0500 crdy 0.125 0.2340 0.2229 0.2506 norm 0.0500 constepinf 0.625 0.8226 0.7995 0.8436 gamma 0.1000 constebeta 0.250 0.2290 0.1975 0.2571 gamma 0.1000 constelab 0.000 0.1824 -0.9354 1.1739 norm 2.0000 ctrend 0.400 0.4664 0.4584 0.4688 norm 0.1000 cgy 0.500 0.6452 0.5940 0.7007 norm 0.2500 calfa 0.300 0.1843 0.1212 0.2418 norm 0.0500 standard deviation of shocks prior mean post. mean 90% HPD interval prior pstdev ea 0.100 0.4600 0.3476 0.6339 invg 2.0000 eb 0.100 0.4272 0.2514 0.5282 invg 2.0000 eg 0.100 1.0354 0.4755 1.5481 invg 2.0000 eqs 0.100 0.7688 0.5368 1.1433 invg 2.0000 em 0.100 0.2989 0.2655 0.4029 invg 2.0000 epinf 0.100 0.2161 0.1379 0.2424 invg 2.0000 ew 0.100 0.2725 0.2564 0.2810 invg 2.0000 Total computing time : 0h31m14s Note: warning(s) encountered in MATLAB/Octave code