1 Small Test Exact Solution Spousal Shocks
This is the example vignette for function: snw_vfi_main_bisec_vec from the PrjOptiSNW Package. This function solves for policy function with vectorized bisection. Small Solution Analysis, husband 5 shocks, wife 3 shocks.
1.1 Test SNW_VFI_MAIN Defaults Small
Call the function with default parameters.
mp_param = snw_mp_param('default_small53');
[V_VFI,ap_VFI,cons_VFI,mp_valpol_more] = snw_vfi_main_bisec_vec(mp_param);
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:18 of 17, time-this-age:0.10593
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:17 of 17, time-this-age:0.086471
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:16 of 17, time-this-age:0.079532
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:15 of 17, time-this-age:0.095977
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:14 of 17, time-this-age:0.081549
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:13 of 17, time-this-age:0.08703
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:12 of 17, time-this-age:0.089059
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:11 of 17, time-this-age:0.094402
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:10 of 17, time-this-age:0.10011
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:9 of 17, time-this-age:0.093424
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:8 of 17, time-this-age:0.095447
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:7 of 17, time-this-age:0.10832
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:6 of 17, time-this-age:0.089896
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:5 of 17, time-this-age:0.09249
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:4 of 17, time-this-age:0.094699
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:3 of 17, time-this-age:0.087703
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:2 of 17, time-this-age:0.0939
SNW_VFI_MAIN_BISEC_VEC: Finished Age Group:1 of 17, time-this-age:0.087152
Completed SNW_VFI_MAIN_BISEC_VEC;SNW_MP_PARAM=default_small53;SNW_MP_CONTROL=default_base;time=1.71571.2 Small Param Results Define Frames
Define the matrix dimensions names and dimension vector values. Policy and Value Functions share the same ND dimensional structure.
% Grids:
age_grid = [19, 22:5:97, 100];
agrid = mp_param('agrid')';
eta_H_grid = mp_param('eta_H_grid')';
eta_S_grid = mp_param('eta_S_grid')';
ar_st_eta_HS_grid = string(cellstr([num2str(eta_H_grid', 'hz=%3.2f;'), num2str(eta_S_grid', 'wz=%3.2f')]));
edu_grid = [0,1];
marry_grid = [0,1];
kids_grid = (1:1:mp_param('n_kidsgrid'))';
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
cl_mp_datasetdesc = {};
cl_mp_datasetdesc{1} = containers.Map({'name', 'labval'}, {'age', age_grid});
cl_mp_datasetdesc{2} = containers.Map({'name', 'labval'}, {'savings', agrid});
cl_mp_datasetdesc{3} = containers.Map({'name', 'labval'}, {'eta', 1:length(eta_H_grid)});
cl_mp_datasetdesc{4} = containers.Map({'name', 'labval'}, {'edu', edu_grid});
cl_mp_datasetdesc{5} = containers.Map({'name', 'labval'}, {'marry', marry_grid});
cl_mp_datasetdesc{6} = containers.Map({'name', 'labval'}, {'kids', kids_grid});1.3 Analyze Savings and Shocks
First, analyze Savings Levels and Shocks, Aggregate Over All Others, and do various other calculations.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
mp_support_graph('cl_st_xtitle') = {'Savings States, a'};
mp_support_graph('st_legend_loc') = 'best';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('it_legend_select') = 9; % how many shock legends to show
mp_support_graph('cl_colors') = 'jet';MEAN(VAL(A,Z)), MEAN(AP(A,Z)), MEAN(C(A,Z))
Tabulate value and policies along savings and shocks:
% Set
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
ar_permute = [1,4,5,6,3,2];
% Value Function
tb_az_v = ff_summ_nd_array("MEAN(VAL(A,Z))", V_VFI, true, ["mean"], 4, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(VAL(A,Z)) xxxxxxxxxxxxxxxxxxxxxxxxxxx
group savings mean_eta_1 mean_eta_2 mean_eta_3 mean_eta_4 mean_eta_5 mean_eta_6 mean_eta_7 mean_eta_8 mean_eta_9 mean_eta_10 mean_eta_11 mean_eta_12 mean_eta_13 mean_eta_14 mean_eta_15
_____ _________ __________ __________ __________ __________ __________ __________ __________ __________ __________ ___________ ___________ ___________ ___________ ___________ ___________
1 0 -24.423 -14.383 -9.0838 -6.1681 -4.6368 -21.16 -12.873 -8.1373 -5.3766 -3.8904 -16.593 -9.8032 -6.1944 -4.1246 -2.992
2 0.0097656 -23.655 -14.141 -8.9428 -6.0477 -4.521 -20.721 -12.725 -8.0508 -5.3059 -3.8237 -16.217 -9.6854 -6.1244 -4.0657 -2.9359
3 0.078125 -20.274 -12.864 -8.2087 -5.441 -3.9447 -18.597 -11.897 -7.5808 -4.9362 -3.4813 -14.463 -9.0428 -5.7563 -3.7684 -2.6574
4 0.26367 -16.052 -10.951 -7.1333 -4.627 -3.2033 -15.258 -10.427 -6.7883 -4.3683 -2.9802 -11.797 -7.9418 -5.1698 -3.3421 -2.2775
5 0.625 -12.169 -8.9647 -6.0525 -3.918 -2.6128 -11.753 -8.67 -5.8499 -3.7794 -2.5033 -9.0813 -6.6522 -4.4995 -2.9252 -1.9394
6 1.2207 -8.9956 -7.1143 -5.0287 -3.3224 -2.17 -8.7621 -6.936 -4.9021 -3.241 -2.1112 -6.8238 -5.3898 -3.8277 -2.555 -1.673
7 2.1094 -6.6026 -5.5315 -4.126 -2.8111 -1.8354 -6.4689 -5.4217 -4.0462 -2.7603 -1.8014 -5.1255 -4.2907 -3.2213 -2.2246 -1.465
8 3.3496 -4.8705 -4.2629 -3.3542 -2.3677 -1.5738 -4.7931 -4.1953 -3.3029 -2.3333 -1.5524 -3.8915 -3.3981 -2.689 -1.925 -1.2946
9 5 -3.6341 -3.2853 -2.7087 -1.9911 -1.3595 -3.5888 -3.2436 -2.6752 -1.9675 -1.3452 -2.9917 -2.6943 -2.2294 -1.6593 -1.1473
10 7.1191 -2.7516 -2.5471 -2.1826 -1.6748 -1.1772 -2.7246 -2.5212 -2.1608 -1.6587 -1.1674 -2.3319 -2.1467 -1.842 -1.4274 -1.0161
11 9.7656 -2.1163 -1.9932 -1.7614 -1.4073 -1.0193 -2.0999 -1.977 -1.7471 -1.3963 -1.0123 -1.8424 -1.7234 -1.5212 -1.2238 -0.89713
12 12.998 -1.653 -1.5768 -1.4275 -1.1818 -0.88344 -1.6428 -1.5665 -1.418 -1.1741 -0.8782 -1.4731 -1.3952 -1.2592 -1.0464 -0.79051
13 16.875 -1.3103 -1.2619 -1.1642 -0.99332 -0.76691 -1.3038 -1.2552 -1.1578 -0.98793 -0.76296 -1.1903 -1.1387 -1.0462 -0.89385 -0.69565
14 21.455 -1.0532 -1.0216 -0.95651 -0.83698 -0.66655 -1.0489 -1.0172 -0.95213 -0.83317 -0.66362 -0.97149 -0.93682 -0.87335 -0.76404 -0.61178
15 26.797 -0.85714 -0.83614 -0.79193 -0.7076 -0.57964 -0.85429 -0.83312 -0.78891 -0.70488 -0.57747 -0.80054 -0.77679 -0.73269 -0.65391 -0.53753
16 32.959 -0.7057 -0.69138 -0.66083 -0.60072 -0.50456 -0.70375 -0.6893 -0.65871 -0.59875 -0.50294 -0.66579 -0.6492 -0.61808 -0.56093 -0.47215
17 40 -0.58719 -0.57722 -0.55573 -0.51237 -0.43988 -0.58583 -0.57576 -0.55422 -0.51094 -0.43866 -0.55862 -0.54682 -0.52452 -0.48265 -0.41486
18 47.979 -0.49334 -0.48626 -0.47088 -0.43924 -0.38431 -0.49239 -0.48522 -0.46979 -0.43817 -0.38338 -0.47262 -0.46407 -0.44785 -0.41687 -0.36491
19 56.953 -0.41818 -0.41306 -0.40187 -0.37849 -0.33661 -0.41749 -0.4123 -0.40107 -0.37769 -0.33589 -0.40296 -0.39666 -0.38469 -0.36151 -0.32149
20 66.982 -0.35736 -0.35359 -0.34532 -0.32783 -0.29568 -0.35685 -0.35303 -0.34473 -0.32724 -0.29513 -0.34603 -0.34132 -0.33236 -0.31483 -0.28383
21 78.125 -0.30764 -0.30483 -0.29864 -0.28541 -0.26055 -0.30727 -0.30442 -0.2982 -0.28496 -0.26012 -0.29912 -0.29556 -0.28878 -0.27538 -0.2512
22 90.439 -0.26663 -0.26451 -0.25982 -0.24971 -0.23034 -0.26635 -0.2642 -0.25949 -0.24936 -0.23 -0.26018 -0.25746 -0.25226 -0.24193 -0.22294
23 103.98 -0.23253 -0.23091 -0.22732 -0.21951 -0.20431 -0.23232 -0.23068 -0.22707 -0.21925 -0.20405 -0.22759 -0.22549 -0.22148 -0.21344 -0.19844
24 118.82 -0.20396 -0.20271 -0.19994 -0.19386 -0.18184 -0.2038 -0.20253 -0.19974 -0.19365 -0.18164 -0.20014 -0.19851 -0.19538 -0.18908 -0.17714
25 135 -0.17985 -0.17888 -0.17671 -0.17193 -0.16238 -0.17973 -0.17874 -0.17656 -0.17177 -0.16221 -0.17685 -0.17557 -0.1731 -0.1681 -0.15852
% Aprime Choice
tb_az_ap = ff_summ_nd_array("MEAN(AP(A,Z))", ap_VFI, true, ["mean"], 4, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(AP(A,Z)) xxxxxxxxxxxxxxxxxxxxxxxxxxx
group savings mean_eta_1 mean_eta_2 mean_eta_3 mean_eta_4 mean_eta_5 mean_eta_6 mean_eta_7 mean_eta_8 mean_eta_9 mean_eta_10 mean_eta_11 mean_eta_12 mean_eta_13 mean_eta_14 mean_eta_15
_____ _________ __________ __________ __________ __________ __________ __________ __________ __________ __________ ___________ ___________ ___________ ___________ ___________ ___________
1 0 3.2159e-05 0.0024668 0.032046 0.17185 0.75713 0.0035503 0.0057037 0.047179 0.22128 0.83929 1.5949 1.7717 1.9927 2.3825 3.261
2 0.0097656 0.00055365 0.0039513 0.034103 0.17524 0.76251 0.0051645 0.0082793 0.050114 0.22508 0.84479 1.5992 1.7765 1.9982 2.3881 3.2676
3 0.078125 0.015815 0.020023 0.052271 0.20181 0.8015 0.036634 0.037379 0.078236 0.2558 0.88531 1.6417 1.8168 2.0406 2.4302 3.3143
4 0.26367 0.094638 0.094808 0.12856 0.29193 0.91314 0.14372 0.15181 0.19153 0.36171 1.0035 1.7785 1.9511 2.1757 2.555 3.4442
5 0.625 0.31851 0.32442 0.35753 0.52484 1.1543 0.39249 0.40496 0.43751 0.61133 1.2467 2.0525 2.2281 2.4483 2.8196 3.7031
6 1.2207 0.75143 0.75176 0.79303 0.95962 1.5772 0.8339 0.84934 0.88966 1.0532 1.6786 2.5043 2.6788 2.9087 3.2694 4.1441
7 2.1094 1.4241 1.4284 1.4709 1.6269 2.2293 1.5113 1.5285 1.5802 1.7093 2.329 3.1791 3.3637 3.5917 3.9381 4.7981
8 3.3496 2.3733 2.3796 2.421 2.5737 3.1535 2.4564 2.4749 2.5266 2.646 3.2655 4.145 4.3264 4.5493 4.8846 5.7519
9 5 3.6394 3.6466 3.6884 3.8506 4.3901 3.7225 3.7419 3.7947 3.9465 4.5072 5.4003 5.5927 5.824 6.1639 6.9962
10 7.1191 5.2875 5.2955 5.3372 5.5015 5.9876 5.3719 5.3915 5.4459 5.6201 6.097 7.0272 7.2068 7.4331 7.8074 8.5649
11 9.7656 7.3153 7.3234 7.3642 7.5288 8.0042 7.4069 7.4251 7.4813 7.6575 8.0966 9.1171 9.2822 9.5067 9.8589 10.592
12 12.998 9.7556 9.7616 9.8006 9.967 10.49 9.8338 9.8499 9.9027 10.084 10.556 11.529 11.72 11.94 12.294 13.012
13 16.875 12.766 12.773 12.807 12.97 13.562 12.844 12.858 12.906 13.086 13.646 14.521 14.72 14.97 15.326 16.074
14 21.455 16.338 16.342 16.377 16.524 17.139 16.426 16.44 16.486 16.653 17.268 18.053 18.254 18.515 18.904 19.658
15 26.797 20.401 20.403 20.431 20.57 21.185 20.487 20.497 20.537 20.693 21.324 22.072 22.259 22.513 22.91 23.737
16 32.959 25.088 25.095 25.124 25.248 25.842 25.176 25.189 25.224 25.366 25.983 26.768 26.951 27.19 27.575 28.418
17 40 30.463 30.471 30.51 30.633 31.193 30.54 30.556 30.606 30.737 31.322 32.154 32.337 32.575 32.941 33.784
18 47.979 36.558 36.567 36.609 36.754 37.274 36.633 36.649 36.697 36.853 37.394 38.337 38.51 38.745 39.114 39.912
19 56.953 43.57 43.576 43.612 43.757 44.279 43.643 43.658 43.705 43.86 44.396 45.2 45.358 45.578 45.94 46.719
20 66.982 51.378 51.387 51.429 51.567 52.101 51.457 51.473 51.523 51.675 52.22 52.915 53.068 53.277 53.62 54.386
21 78.125 59.666 59.675 59.721 59.878 60.41 59.76 59.777 59.832 59.994 60.541 61.518 61.668 61.872 62.207 62.952
22 90.439 69.02 69.027 69.069 69.228 69.775 69.1 69.116 69.169 69.339 69.894 70.927 71.078 71.279 71.606 72.331
23 103.98 79.509 79.516 79.557 79.706 80.27 79.589 79.604 79.653 79.814 80.386 81.356 81.509 81.709 82.032 82.742
24 118.82 90.88 90.887 90.929 91.074 91.625 90.956 90.971 91.022 91.18 91.743 92.752 92.917 93.126 93.447 94.141
25 135 103.23 103.23 103.27 103.42 103.97 103.31 103.32 103.37 103.53 104.08 105.13 105.34 105.6 106 106.84
% Consumption Choices
tb_az_c = ff_summ_nd_array("MEAN(C(A,Z))", cons_VFI, true, ["mean"], 4, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(C(A,Z)) xxxxxxxxxxxxxxxxxxxxxxxxxxx
group savings mean_eta_1 mean_eta_2 mean_eta_3 mean_eta_4 mean_eta_5 mean_eta_6 mean_eta_7 mean_eta_8 mean_eta_9 mean_eta_10 mean_eta_11 mean_eta_12 mean_eta_13 mean_eta_14 mean_eta_15
_____ _________ __________ __________ __________ __________ __________ __________ __________ __________ __________ ___________ ___________ ___________ ___________ ___________ ___________
1 0 0.16881 0.29125 0.55695 1.1299 2.3014 0.29922 0.42884 0.69049 1.2391 2.3904 1.4929 1.6544 1.9693 2.5658 3.7569
2 0.0097656 0.17988 0.30127 0.56634 1.1379 2.3074 0.30913 0.43773 0.69898 1.2467 2.3963 1.5001 1.661 1.9751 2.5716 3.7617
3 0.078125 0.24566 0.36575 0.62833 1.1912 2.3483 0.35831 0.48892 0.75085 1.2958 2.4355 1.5378 1.7008 2.0126 2.6092 3.7946
4 0.26367 0.38648 0.50942 0.76949 1.318 2.4533 0.4699 0.5923 0.85458 1.4064 2.5337 1.6186 1.7834 2.0939 2.7005 3.8807
5 0.625 0.58919 0.70456 0.96359 1.5071 2.6337 0.64619 0.76283 1.031 1.5783 2.7116 1.7678 1.9286 2.2426 2.8567 4.0424
6 1.2207 0.85722 0.97603 1.2247 1.7675 2.9054 0.9039 1.0159 1.2745 1.8309 2.9736 2.0124 2.1733 2.4764 3.1003 4.2945
7 2.1094 1.2268 1.3395 1.5846 2.1362 3.2884 1.2668 1.3754 1.6207 2.21 3.3577 2.3746 2.5243 2.8282 3.4654 4.6739
8 3.3496 1.7279 1.837 2.0808 2.6338 3.8076 1.7706 1.8764 2.1198 2.7171 3.864 2.854 3.0061 3.3138 3.9611 5.1618
9 5 2.3875 2.4944 2.7359 3.2775 4.4904 2.4289 2.5327 2.7733 3.3366 4.5412 3.5199 3.6601 3.9585 4.6003 5.8351
10 7.1191 3.2081 3.3133 3.5534 4.0911 5.3563 3.2473 3.35 3.5876 4.127 5.4144 4.3576 4.5101 4.8127 5.4191 6.7283
11 9.7656 4.2601 4.3643 4.6042 5.1402 6.4149 4.2913 4.3947 4.6296 5.1656 6.4897 5.344 5.5106 5.8143 6.4421 7.7753
12 12.998 5.5783 5.6843 5.9251 6.4581 7.684 5.6225 5.7276 5.965 6.4951 7.7847 6.6877 6.8281 7.1356 7.7609 9.1086
13 16.875 7.0733 7.1787 7.4234 7.9594 9.1154 7.1181 7.2243 7.4664 7.9965 9.1977 8.1993 8.3316 8.6084 9.2321 10.549
14 21.455 8.823 8.9302 9.1737 9.7243 10.857 8.8568 8.9633 9.206 9.7494 10.894 9.9861 10.116 10.382 10.972 12.283
15 26.797 10.964 11.073 11.324 11.882 13.013 10.999 11.111 11.359 11.912 13.041 12.17 12.314 12.587 13.168 14.405
16 32.959 13.433 13.538 13.787 14.36 15.511 13.467 13.574 13.828 14.395 15.537 14.629 14.778 15.065 15.658 16.878
17 40 16.233 16.337 16.576 17.149 18.335 16.278 16.383 16.621 17.198 18.372 17.418 17.566 17.854 18.466 19.687
18 47.979 19.402 19.505 19.741 20.291 21.516 19.449 19.553 19.793 20.346 21.562 20.498 20.656 20.947 21.555 22.82
19 56.953 22.81 22.914 23.156 23.707 24.93 22.858 22.963 23.205 23.757 24.979 24.053 24.227 24.532 25.148 26.431
20 66.982 26.645 26.747 26.982 27.54 28.75 26.687 26.791 27.03 27.585 28.798 27.981 28.16 28.477 29.111 30.407
21 78.125 31.292 31.394 31.626 32.165 33.377 31.319 31.422 31.656 32.201 33.412 32.313 32.494 32.816 33.459 34.776
22 90.439 36.234 36.338 36.573 37.11 38.307 36.274 36.379 36.614 37.152 38.354 37.199 37.38 37.704 38.354 39.691
23 103.98 41.468 41.572 41.808 42.355 43.535 41.509 41.615 41.854 42.4 43.585 42.494 42.672 42.998 43.651 45.004
24 118.82 47.317 47.421 47.656 48.207 49.4 47.362 47.467 47.704 48.254 49.447 48.317 48.483 48.8 49.456 50.824
25 135 53.752 53.857 54.095 54.642 55.842 53.796 53.902 54.143 54.689 55.891 54.721 54.845 55.109 55.682 56.909 Graph Mean Values:
mp_support_graph('cl_st_graph_title') = {'MEAN(value(a,z)), a=x, z=color'};
mp_support_graph('cl_st_ytitle') = {'MEAN(value(a,z))'};
ff_graph_grid((tb_az_v{1:end, 3:end})', ar_st_eta_HS_grid, agrid, mp_support_graph);
Graph Mean Savings Choices:
mp_support_graph('cl_st_graph_title') = {'MEAN(APRIME(a,z)), a=x, z=color'};
mp_support_graph('cl_st_ytitle') = {'MEAN(APRIME(a,z))'};
ff_graph_grid((tb_az_ap{1:end, 3:end})', ar_st_eta_HS_grid, agrid, mp_support_graph);
Graph Mean Consumption:
mp_support_graph('cl_st_graph_title') = {'MEAN(C(a,z)), a=x, z=color'};
mp_support_graph('cl_st_ytitle') = {'MEAN(C(a,z))'};
ff_graph_grid((tb_az_c{1:end, 3:end})', ar_st_eta_HS_grid, agrid, mp_support_graph);
1.4 Analyze Kids and Marriage and Age
Aggregating over education, savings, and shocks, what are the differential effects of Marriage and Age.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
ar_row_grid = ["k0M0", "K1M0", "K2M0", "k0M1", "K1M1", "K2M1"];
mp_support_graph('cl_st_xtitle') = {'Age'};
mp_support_graph('st_legend_loc') = 'best';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('st_rounding') = '6.2f'; % format shock legend
mp_support_graph('cl_scatter_shapes') = { 'o', 'd' ,'s', 'o', 'd', 's'};
mp_support_graph('cl_colors') = {'red', 'red', 'red', 'blue', 'blue', 'blue'};MEAN(VAL(KM,J)), MEAN(AP(KM,J)), MEAN(C(KM,J))
Tabulate value and policies:
% Set
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
ar_permute = [2,3,4,1,6,5];
% Value Function
tb_az_v = ff_summ_nd_array("MEAN(VAL(KM,J))", V_VFI, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(VAL(KM,J)) xxxxxxxxxxxxxxxxxxxxxxxxxxx
group kids marry mean_age_19 mean_age_22 mean_age_27 mean_age_32 mean_age_37 mean_age_42 mean_age_47 mean_age_52 mean_age_57 mean_age_62 mean_age_67 mean_age_72 mean_age_77 mean_age_82 mean_age_87 mean_age_92 mean_age_97 mean_age_100
_____ ____ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 1 0 -4.7384 -4.2839 -3.9125 -3.6403 -3.4202 -3.2286 -3.0466 -2.8601 -2.6595 -2.4491 -2.2399 -2.0731 -2.3629 -1.8637 -1.4355 -1.116 -0.89281 -0.68658
2 2 0 -6.2307 -5.5732 -5.014 -4.5943 -4.2483 -3.9542 -3.6887 -3.434 -3.1782 -2.9257 -2.6801 -2.4926 -2.8794 -2.3137 -1.8328 -1.4683 -1.2096 -0.97097
3 3 0 -6.9818 -6.3368 -5.7685 -5.3334 -4.9708 -4.6532 -4.3595 -4.0736 -3.7846 -3.4985 -3.2208 -3.0125 -3.5228 -2.8558 -2.2747 -1.8248 -1.5002 -1.1892
4 1 1 -3.4759 -3.1424 -2.8538 -2.6272 -2.4359 -2.2663 -2.1075 -1.9513 -1.7931 -1.6374 -1.4932 -1.3847 -1.4858 -1.1944 -0.94216 -0.75023 -0.61798 -0.49489
5 2 1 -4.325 -3.9032 -3.5196 -3.2101 -2.9428 -2.7083 -2.4943 -2.2914 -2.0936 -1.9053 -1.7315 -1.5985 -1.6948 -1.3759 -1.0987 -0.88795 -0.74204 -0.60546
6 3 1 -4.7282 -4.3225 -3.9432 -3.6317 -3.3597 -3.1133 -2.8816 -2.6564 -2.4324 -2.2154 -2.0111 -1.8459 -1.9256 -1.5703 -1.2589 -1.0197 -0.85265 -0.69356
% Aprime Choice
tb_az_ap = ff_summ_nd_array("MEAN(AP(KM,J))", ap_VFI, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(AP(KM,J)) xxxxxxxxxxxxxxxxxxxxxxxxxxx
group kids marry mean_age_19 mean_age_22 mean_age_27 mean_age_32 mean_age_37 mean_age_42 mean_age_47 mean_age_52 mean_age_57 mean_age_62 mean_age_67 mean_age_72 mean_age_77 mean_age_82 mean_age_87 mean_age_92 mean_age_97 mean_age_100
_____ ____ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 1 0 34.931 34.726 34.665 34.554 34.362 34.077 33.682 33.147 32.422 31.5 30.819 29.713 26.817 24.458 21.039 16.761 11.058 0
2 2 0 34.603 34.334 34.198 33.995 33.692 33.286 32.762 32.089 31.289 30.736 29.885 28.361 25.892 23.172 19.841 15.524 10.196 0
3 3 0 34.187 33.968 33.877 33.705 33.427 33.033 32.51 31.83 31.11 30.61 29.726 28.203 25.828 23.157 19.895 15.696 10.38 0
4 1 1 35.713 35.611 35.7 35.727 35.66 35.493 35.215 34.799 34.189 33.405 32.672 31.512 27.898 25.399 21.82 17.365 11.474 0
5 2 1 35.368 35.246 35.284 35.242 35.101 34.849 34.477 33.956 33.237 32.553 31.755 30.453 27.335 24.621 21.078 16.723 10.908 0
6 3 1 34.903 34.81 34.86 34.834 34.7 34.447 34.074 33.542 32.826 32.188 31.388 30.042 27.293 24.589 21.089 16.779 11.002 0
% Consumption Choices
tb_az_c = ff_summ_nd_array("MEAN(C(KM,J))", cons_VFI, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(C(KM,J)) xxxxxxxxxxxxxxxxxxxxxxxxxxx
group kids marry mean_age_19 mean_age_22 mean_age_27 mean_age_32 mean_age_37 mean_age_42 mean_age_47 mean_age_52 mean_age_57 mean_age_62 mean_age_67 mean_age_72 mean_age_77 mean_age_82 mean_age_87 mean_age_92 mean_age_97 mean_age_100
_____ ____ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 1 0 6.8531 7.1729 7.4988 7.8167 8.1435 8.4993 8.9181 9.4437 10.134 10.995 11.582 12.551 14.27 16.629 20.048 24.326 30.029 41.087
2 2 0 7.182 7.5653 7.9659 8.3756 8.813 9.2907 9.8382 10.502 11.267 11.759 12.516 13.903 15.195 17.915 21.247 25.563 30.891 41.087
3 3 0 7.5973 7.931 8.2872 8.6657 9.0783 9.5438 10.091 10.761 11.445 11.885 12.675 14.061 15.259 17.93 21.192 25.392 30.707 41.087
4 1 1 7.7992 8.182 8.5624 8.9321 9.311 9.7148 10.172 10.725 11.436 12.283 13.029 14.136 15.519 18.092 21.74 26.258 32.205 43.729
5 2 1 7.879 8.2553 8.6555 9.0645 9.4908 9.9535 10.481 11.116 11.915 12.643 13.437 14.674 16.094 18.883 22.494 26.913 32.784 43.743
6 3 1 8.1608 8.4911 8.8566 9.2297 9.6299 10.077 10.589 11.219 12 12.669 13.454 14.725 16.228 19.01 22.581 26.956 32.792 43.847 Graph Mean Values:
mp_support_graph('cl_st_graph_title') = {'MEAN(value(KM,J)), a=age, z=kids+marry'};
mp_support_graph('cl_st_ytitle') = {'MEAN(value(KM,J))'};
ff_graph_grid((tb_az_v{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Savings Choices:
mp_support_graph('cl_st_graph_title') = {'MEAN(APRIME(KM,J)), a=x, z=color'};
mp_support_graph('cl_st_ytitle') = {'MEAN(APRIME(KM,J))'};
ff_graph_grid((tb_az_ap{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Consumption:
mp_support_graph('cl_st_graph_title') = {'MEAN(C(KM,J)), a=x, z=color'};
mp_support_graph('cl_st_ytitle') = {'MEAN(C(KM,J))'};
ff_graph_grid((tb_az_c{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
1.5 Analyze Education and Marriage and Age
Aggregating over education, savings, and shocks, what are the differential effects of Marriage and Age.
% Generate some Data
mp_support_graph = containers.Map('KeyType', 'char', 'ValueType', 'any');
ar_row_grid = ["E0M0", "E1M0", "E0M1", "E1M1"];
mp_support_graph('cl_st_xtitle') = {'Age'};
mp_support_graph('st_legend_loc') = 'best';
mp_support_graph('bl_graph_logy') = true; % do not log
mp_support_graph('st_rounding') = '6.2f'; % format shock legend
mp_support_graph('cl_scatter_shapes') = {'*', 'p', '*','p' };
mp_support_graph('cl_colors') = {'red', 'red', 'blue', 'blue'};MEAN(VAL(EKM,J)), MEAN(AP(EKM,J)), MEAN(C(EKM,J))
Tabulate value and policies:
% Set
% NaN(n_jgrid,n_agrid,n_etagrid,n_educgrid,n_marriedgrid,n_kidsgrid);
ar_permute = [2,3,6,1,4,5];
% Value Function
tb_az_v = ff_summ_nd_array("MEAN(VAL(EKM,J))", V_VFI, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(VAL(EKM,J)) xxxxxxxxxxxxxxxxxxxxxxxxxxx
group edu marry mean_age_19 mean_age_22 mean_age_27 mean_age_32 mean_age_37 mean_age_42 mean_age_47 mean_age_52 mean_age_57 mean_age_62 mean_age_67 mean_age_72 mean_age_77 mean_age_82 mean_age_87 mean_age_92 mean_age_97 mean_age_100
_____ ___ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 0 0 -6.4015 -5.8666 -5.3879 -4.9966 -4.6557 -4.3525 -4.0711 -3.7972 -3.5191 -3.2399 -2.9631 -2.7398 -3.0999 -2.4877 -1.9609 -1.561 -1.2765 -1.0093
2 1 0 -5.5658 -4.9294 -4.4088 -4.0487 -3.7705 -3.5382 -3.3254 -3.1146 -2.8958 -2.6757 -2.4641 -2.3123 -2.7435 -2.201 -1.7345 -1.3784 -1.1253 -0.88856
3 0 1 -4.4764 -4.1029 -3.7581 -3.4622 -3.1968 -2.9557 -2.7306 -2.5141 -2.3006 -2.0946 -1.9015 -1.7476 -1.819 -1.475 -1.1758 -0.94656 -0.78798 -0.63847
4 1 1 -3.8764 -3.4759 -3.1196 -2.8504 -2.6288 -2.4363 -2.2583 -2.0853 -1.9122 -1.7442 -1.5889 -1.4719 -1.5851 -1.2854 -1.024 -0.82536 -0.68713 -0.55747
% Aprime Choice
tb_az_ap = ff_summ_nd_array("MEAN(AP(EKM,J))", ap_VFI, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(AP(EKM,J)) xxxxxxxxxxxxxxxxxxxxxxxxxxx
group edu marry mean_age_19 mean_age_22 mean_age_27 mean_age_32 mean_age_37 mean_age_42 mean_age_47 mean_age_52 mean_age_57 mean_age_62 mean_age_67 mean_age_72 mean_age_77 mean_age_82 mean_age_87 mean_age_92 mean_age_97 mean_age_100
_____ ___ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 0 0 34.682 34.444 34.272 34.048 33.753 33.374 32.888 32.256 31.512 30.851 30.02 28.595 26.17 23.589 20.254 15.987 10.542 0
2 1 0 34.465 34.241 34.222 34.121 33.901 33.556 33.081 32.455 31.703 31.046 30.267 28.923 26.188 23.603 20.262 16 10.548 0
3 0 1 35.363 35.234 35.193 35.099 34.934 34.686 34.331 33.831 33.132 32.407 31.616 30.323 27.362 24.751 21.229 16.874 11.069 0
4 1 1 35.293 35.21 35.37 35.437 35.374 35.174 34.846 34.367 33.703 33.023 32.261 31.015 27.656 24.989 21.429 17.037 11.188 0
% Consumption Choices
tb_az_c = ff_summ_nd_array("MEAN(C(EKM,J))", cons_VFI, true, ["mean"], 3, 1, cl_mp_datasetdesc, ar_permute);
xxx MEAN(C(EKM,J)) xxxxxxxxxxxxxxxxxxxxxxxxxxx
group edu marry mean_age_19 mean_age_22 mean_age_27 mean_age_32 mean_age_37 mean_age_42 mean_age_47 mean_age_52 mean_age_57 mean_age_62 mean_age_67 mean_age_72 mean_age_77 mean_age_82 mean_age_87 mean_age_92 mean_age_97 mean_age_100
_____ ___ _____ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ____________
1 0 0 7.1022 7.4087 7.7357 8.0845 8.4713 8.9105 9.427 10.062 10.782 11.389 12.136 13.445 14.9 17.48 20.815 25.082 30.528 41.07
2 1 0 7.3195 7.7041 8.0988 8.4875 8.8852 9.312 9.8044 10.409 11.116 11.703 12.379 13.565 14.917 17.502 20.843 25.105 30.557 41.105
3 0 1 7.7587 8.0743 8.4083 8.7618 9.1489 9.5822 10.086 10.701 11.48 12.245 13.031 14.27 15.805 18.483 22.066 26.477 32.333 43.447
4 1 1 8.134 8.5446 8.9747 9.3891 9.8055 10.248 10.742 11.338 12.087 12.819 13.583 14.752 16.089 18.84 22.478 26.941 32.854 44.099 Graph Mean Values:
mp_support_graph('cl_st_graph_title') = {'MEAN(value(EM,J)), a=age, z=kids+marry'};
mp_support_graph('cl_st_ytitle') = {'MEAN(value(EM,J))'};
ff_graph_grid((tb_az_v{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Savings Choices:
mp_support_graph('cl_st_graph_title') = {'MEAN(APRIME(EK,J)), a=x, z=color'};
mp_support_graph('cl_st_ytitle') = {'MEAN(APRIME(EK,J))'};
ff_graph_grid((tb_az_ap{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
Graph Mean Consumption:
mp_support_graph('cl_st_graph_title') = {'MEAN(C(EK,J)), a=x, z=color'};
mp_support_graph('cl_st_ytitle') = {'MEAN(C(EK,J))'};
ff_graph_grid((tb_az_c{1:end, 4:end}), ar_row_grid, age_grid, mp_support_graph);
