time | Calls | line |
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| | 7 | function result_map = ff_ipwkbzr_fibs_vf_vecsv(varargin)
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| | 8 | %% FF_IPWKBZ_FIBS_VF_VECSV solve infinite horizon exo shock + endo asset problem
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| | 9 | % This is a modified version of
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| | 10 | % <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbzr/solve/html/ff_ipwkbzr_vf_vecsv.html
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| | 11 | % ff_ipwkbzr_vf_vecsv>, to see how this function solves the formal and
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| | 12 | % savings risky and safe asset problem with formal and informal choices,
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| | 13 | % compare the code here and from
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| | 14 | % <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbzr/solve/html/ff_ipwkbzr_vf_vecsv.html
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| | 15 | % ff_ipwkbzr_vf_vecsv> side by side.
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| | 16 | %
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| | 17 | % @param param_map container parameter container
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| | 18 | %
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| | 19 | % @param support_map container support container
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| | 20 | %
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| | 21 | % @param armt_map container container with states, choices and shocks
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| | 22 | % grids that are inputs for grid based solution algorithm
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| | 23 | %
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| | 24 | % @param func_map container container with function handles for
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| | 25 | % consumption cash-on-hand etc.
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| | 26 | %
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| | 27 | % @return result_map container contains policy function matrix, value
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| | 28 | % function matrix, iteration results, and policy function, value function
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| | 29 | % and iteration results tables.
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| | 30 | %
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| | 31 | % keys included in result_map:
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| | 32 | %
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| | 33 | % * mt_val matrix states_n by shock_n matrix of converged value function grid
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| | 34 | % * mt_pol_a matrix states_n by shock_n matrix of converged policy function grid
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| | 35 | % * ar_val_diff_norm array if bl_post = true it_iter_last by 1 val function
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| | 36 | % difference between iteration
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| | 37 | % * ar_pol_diff_norm array if bl_post = true it_iter_last by 1 policy
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| | 38 | % function difference between iterations
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| | 39 | % * mt_pol_perc_change matrix if bl_post = true it_iter_last by shock_n the
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| | 40 | % proportion of grid points at which policy function changed between
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| | 41 | % current and last iteration for each element of shock
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| | 42 | %
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| | 43 | % @example
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| | 44 | %
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| | 45 | % @include
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| | 46 | %
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| | 47 | % * <https://github.com/FanWangEcon/CodeDynaAsset/blob/master/m_ipwkbzr/paramfunc/ff_ipwkbzr_evf.m ff_ipwkbzr_evf>
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| | 48 | % * <https://github.com/FanWangEcon/CodeDynaAsset/blob/master/m_ipwkbzr/paramfunc/ffs_ipwkbzr_set_default_param.m ffs_ipwkbzr_set_default_param>
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| | 49 | % * <https://github.com/FanWangEcon/CodeDynaAsset/blob/master/m_ipwkbzr/paramfunc/ffs_ipwkbzr_get_funcgrid.m ffs_ipwkbzr_get_funcgrid>
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| | 50 | % * <https://github.com/FanWangEcon/CodeDynaAsset/blob/master/m_akz/solvepost/ff_akz_vf_post.m ff_akz_vf_post>
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| | 51 | %
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| | 52 |
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| | 53 | %% Default
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| | 54 | % * it_param_set = 1: quick test
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| | 55 | % * it_param_set = 2: benchmark run
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| | 56 | % * it_param_set = 3: benchmark profile
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| | 57 | % * it_param_set = 4: press publish button
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| | 58 |
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| | 59 | it_param_set = 4;
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| | 60 | [param_map, support_map] = ffs_ipwkbzr_fibs_set_default_param(it_param_set);
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| | 61 |
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| | 62 | % parameters can be set inside ffs_ipwkbzr_set_default_param or updated here
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| | 63 | % param_map('it_w_perc_n') = 50;
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| | 64 | % param_map('it_ak_perc_n') = param_map('it_w_perc_n');
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| | 65 | % param_map('fl_coh_interp_grid_gap') = 0.025;
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| | 66 | % param_map('it_c_interp_grid_gap') = 0.001;
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| | 67 | % param_map('fl_w_interp_grid_gap') = 0.25;
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| | 68 | % param_map('it_w_perc_n') = 100;
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| | 69 | % param_map('it_ak_perc_n') = param_map('it_w_perc_n');
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| | 70 | % param_map('fl_z_r_infbr_n') = 5;
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| | 71 | % param_map('it_z_wage_n') = 15;
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| | 72 | % param_map('it_z_n') = param_map('it_z_wage_n') * param_map('fl_z_r_infbr_n');
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| | 73 | % param_map('fl_coh_interp_grid_gap') = 0.1;
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| | 74 | % param_map('it_c_interp_grid_gap') = 10^-4;
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| | 75 | % param_map('fl_w_interp_grid_gap') = 0.1;
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| | 76 |
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| | 77 | st_param_which = 'default';
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| | 78 |
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| | 79 | if (ismember(st_param_which, ['default']))
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| | 80 |
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| | 81 | % default
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| | 82 |
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| | 83 | elseif ismember(st_param_which, ['ff_ipwkbzr_vf_vecsv', 'ff_ipwkbzrr_vf_vecsv'])
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| | 84 |
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| | 85 | param_map('fl_r_fsv') = 0.0;
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| | 86 | param_map('fl_r_fbr') = 1.000;
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| | 87 | param_map('bl_bridge') = false;
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| | 88 | param_map('it_coh_bridge_perc_n') = 1;
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| | 89 |
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| | 90 | if ismember(st_param_which, ['ff_ipwkbzr_vf_vecsv'])
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| | 91 |
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| | 92 | % ff_ipwkbzr_evf default
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| | 93 | param_map('fl_z_r_infbr_min') = 0.025;
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| | 94 | param_map('fl_z_r_infbr_max') = 0.025;
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| | 95 | param_map('fl_z_r_infbr_n') = 1;
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| | 96 |
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| | 97 | param_map('fl_r_save') = 0.025;
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| | 98 |
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| | 99 | end
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| | 100 |
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| | 101 | param_map('it_z_n') = param_map('it_z_wage_n') * param_map('fl_z_r_infbr_n');
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| | 102 |
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| | 103 | end
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| | 104 |
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| | 105 | % get armt and func map
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| | 106 | params_len = length(varargin);
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| | 107 | if params_len <= 2
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| | 108 | [armt_map, func_map] = ffs_ipwkbzr_fibs_get_funcgrid(param_map, support_map); % 1 for override
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| | 109 | default_params = {param_map support_map armt_map func_map};
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| | 110 | end
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| | 111 |
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| | 112 | %% Parse Parameters 1
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| | 113 |
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| | 114 | % if varargin only has param_map and support_map,
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| | 115 | [default_params{1:params_len}] = varargin{:};
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| | 116 | param_map = [param_map; default_params{1}];
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| | 117 | support_map = [support_map; default_params{2}];
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| | 118 | if params_len >= 1 && params_len <= 2
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| | 119 | % If override param_map, re-generate armt and func if they are not
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| | 120 | % provided
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| | 121 | [armt_map, func_map] = ffs_ipwkbzr_fibs_get_funcgrid(param_map, support_map);
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| | 122 | else
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| | 123 | armt_map = default_params{3};
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| | 124 | func_map = default_params{4};
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| | 125 | end
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| | 126 |
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| | 127 | % append function name
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| | 128 | st_func_name = 'ff_ipwkbzr_fibs_vf_vecsv';
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| | 129 | support_map('st_profile_name_main') = [st_func_name support_map('st_profile_name_main')];
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| | 130 | support_map('st_mat_name_main') = [st_func_name support_map('st_mat_name_main')];
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| | 131 | support_map('st_img_name_main') = [st_func_name support_map('st_img_name_main')];
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| | 132 |
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| | 133 | %% Parse Parameters 2, Asset Arrays
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| | 134 | % Dimensions of Various Grids: I for level grid, M for shock grid, P for
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| | 135 | % percent grid
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| | 136 | %
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| | 137 | % # ar_interp_c_grid: 1 by I^c, 1st stage consumption interpolation
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| | 138 | % # ar_interp_coh_grid: 1 by I^{coh}, 1st stage value function V(coh,z)
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| | 139 | % # ar_w_perc: 1 by P^{W=k+b}, 1st stage w \in {w_perc(coh)} choice set
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| | 140 | % # ar_w_level: 1 by I^{W=k+b}, 2nd stage k*(w,z) w grid
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| | 141 | % # ar_ak_perc: 1 by P^{k and b}, 2nd stage k \in {ask_perc(w,z)} set
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| | 142 | %
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| | 143 |
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| | 144 | params_group = values(armt_map, {...
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| | 145 | 'ar_interp_c_grid', 'ar_interp_coh_grid', ...
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| | 146 | 'ar_w_perc', 'ar_w_level', 'ar_w_level_full', 'ar_ak_perc', ...
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| | 147 | 'ar_coh_bridge_perc'});
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| | 148 | [ar_interp_c_grid, ar_interp_coh_grid, ...
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| | 149 | ar_w_perc, ar_w_level, ar_w_level_full, ar_ak_perc, ...
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| | 150 | ar_coh_bridge_perc] = params_group{:};
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| | 151 |
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| | 152 | %% Parse Parameters 2, interp_coh related matrixes
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| | 153 | % Dimensions of Various Grids: I for level grid, M for shock grid, P for
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| | 154 | % percent grid. These are grids for 1st stage solution
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| | 155 | %
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| | 156 | % # mt_interp_coh_grid_mesh_z_wage: I^{coh} by M^w
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| | 157 | % # mt_z_wage_mesh_interp_coh_grid: I^{coh} by M^w
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| | 158 | % # mt_interp_coh_grid_mesh_w_perc: I^{coh} by P^{LAM=k+b}
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| | 159 | % # mt_w_perc_mesh_interp_coh_grid: I^{coh} by P^{LAM=k+b}
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| | 160 | %
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| | 161 |
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| | 162 | params_group = values(armt_map, {...
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| | 163 | 'mt_interp_coh_grid_mesh_z_wage', ...
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| | 164 | 'mt_interp_coh_grid_mesh_w_perc', ...
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| | 165 | 'mt_z_wage_mesh_interp_coh_grid', ...
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| | 166 | 'mt_w_perc_mesh_interp_coh_grid', ...
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| | 167 | 'mt_interp_coh_grid_mesh_z', 'mt_z_mesh_interp_coh_grid' ...
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| | 168 | 'cl_mt_coh_wkb_mesh_z_r_infbr', 'mt_z_mesh_coh_wkb_seg'});
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| | 169 | [mt_interp_coh_grid_mesh_z_wage, ...
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| | 170 | mt_interp_coh_grid_mesh_w_perc, ...
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| | 171 | mt_z_wage_mesh_interp_coh_grid, ...
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| | 172 | mt_w_perc_mesh_interp_coh_grid, ...
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| | 173 | mt_interp_coh_grid_mesh_z, mt_z_mesh_interp_coh_grid, ...
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| | 174 | cl_mt_coh_wkb_mesh_z_r_infbr, mt_z_mesh_coh_wkb_seg] = params_group{:};
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| | 175 |
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| | 176 | %% Parse Parameters 3, reachable cash-on-hand
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| | 177 | % Dimensions of Various Grids: I for level grid, M for shock grid, P for
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| | 178 | % percent grid. These are grids for 1st stage solution
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| | 179 | %
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| | 180 | % # mt_coh_wkb: (I^k x I^{w+%repay} x M^r) by (M^z)
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| | 181 | % # mt_z_wage_mesh_coh_wkb: (I^k x I^{w+%repay} x M^r) by (M^z)
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| | 182 | %
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| | 183 |
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| | 184 | params_group = values(armt_map, {'mt_coh_wkb', 'mt_z_wage_mesh_coh_wkb'});
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| | 185 | [mt_coh_wkb, mt_z_wage_mesh_coh_wkb] = params_group{:};
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| | 186 |
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| | 187 | %% Parse Parameters 4, percentage of w for bridge repayment parameters
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| | 188 | %
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| | 189 | % Note, w=k'+b', where w < 0:
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| | 190 | %
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| | 191 | % mt_w_perc_mesh_interp_coh_grid = ((ar_interp_coh_grid-fl_min_mt_coh)'*ar_w_perc)' + fl_min_mt_coh;
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| | 192 | % mt_bl_w_perc_mesh_interp_coh_grid_wneg = (mt_w_perc_mesh_interp_coh_grid < 0);
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| | 193 | % mt_w_perc_mesh_interp_coh_grid_wneg = mt_w_perc_mesh_interp_coh_grid(mt_bl_w_perc_mesh_interp_coh_grid_wneg);
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| | 194 | % mt_w_perc_mesh_interp_coh_grid_wpos = mt_w_perc_mesh_interp_coh_grid(~mt_bl_w_perc_mesh_interp_coh_grid_wneg);
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| | 195 | %
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| | 196 | % And for negative w levels meshed with bridge shares:
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| | 197 | %
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| | 198 | % [mt_w_level_neg_mesh_coh_bridge_perc, mt_coh_bridge_perc_mesh_w_level_neg] = ...
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| | 199 | % ndgrid(ar_w_level_neg, ar_coh_bridge_perc);
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| | 200 | %
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| | 201 | % And for percent of w NOT going to bridge:
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| | 202 | %
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| | 203 | % mt_coh_w_perc_ratio = (1-(mt_interp_coh_grid_mesh_w_perc./mt_w_perc_mesh_interp_coh_grid));
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| | 204 | % mt_coh_w_perc_ratio(mt_interp_coh_grid_mesh_w_perc >= 0) = 1;
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| | 205 | % mt_coh_w_perc_ratio_wneg = mt_coh_w_perc_ratio(mt_bl_w_perc_mesh_interp_coh_grid_wneg);
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| | 206 | %
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| | 207 |
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| | 208 | params_group = values(armt_map, {
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| | 209 | 'mt_w_level_neg_mesh_coh_bridge_perc', 'mt_coh_bridge_perc_mesh_w_level_neg',...
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| | 210 | 'mt_bl_w_perc_mesh_interp_coh_grid_wneg', ...
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| | 211 | 'mt_w_perc_mesh_interp_coh_grid_wneg', 'mt_w_perc_mesh_interp_coh_grid_wpos', ...
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| | 212 | 'mt_coh_w_perc_ratio_wneg'});
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| | 213 | [mt_w_level_neg_mesh_coh_bridge_perc, mt_coh_bridge_perc_mesh_w_level_neg, ...
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| | 214 | mt_bl_w_perc_mesh_interp_coh_grid_wneg, ...
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| | 215 | mt_w_perc_mesh_interp_coh_grid_wneg, mt_w_perc_mesh_interp_coh_grid_wpos, ...
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| | 216 | mt_coh_w_perc_ratio_wneg] ...
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| | 217 | = params_group{:};
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| | 218 |
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| | 219 | %% Parse Parameters 6, other asset arrays
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| | 220 |
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| | 221 | params_group = values(armt_map, {'ar_z_r_infbr_mesh_wage_w1r2'});
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| | 222 | [ar_z_r_infbr_mesh_wage_w1r2] = params_group{:};
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| | 223 |
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| | 224 | params_group = values(armt_map, {'ar_a_meshk', 'ar_k_mesha'});
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| | 225 | [ar_a_meshk, ar_k_mesha] = params_group{:};
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| | 226 |
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| | 227 | %% Parse Parameters 7, Others
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| | 228 |
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| | 229 | % func_map
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| | 230 | params_group = values(func_map, {'f_util_log', 'f_util_crra', 'f_cons'});
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| | 231 | [f_util_log, f_util_crra, f_cons] = params_group{:};
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| | 232 |
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| | 233 | % param_map
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| | 234 | params_group = values(param_map, {'fl_crra', 'fl_beta', ...
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| | 235 | 'fl_nan_replace', 'fl_c_min', 'bl_bridge', 'bl_default', 'fl_default_wprime'});
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| | 236 | [fl_crra, fl_beta, fl_nan_replace, fl_c_min, bl_bridge, bl_default, fl_default_wprime] = params_group{:};
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| | 237 | params_group = values(param_map, {'it_maxiter_val', 'fl_tol_val', 'fl_tol_pol', 'it_tol_pol_nochange'});
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| | 238 | [it_maxiter_val, fl_tol_val, fl_tol_pol, it_tol_pol_nochange] = params_group{:};
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| | 239 | params_group = values(param_map, {'it_z_n', 'fl_z_r_infbr_n', 'it_z_wage_n'});
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| | 240 | [it_z_n, fl_z_r_infbr_n, it_z_wage_n] = params_group{:};
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| | 241 | params_group = values(param_map, {'st_v_coh_z_interp_method'});
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| | 242 | [st_v_coh_z_interp_method] = params_group{:};
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| | 243 |
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| | 244 | % support_map
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| | 245 | params_group = values(support_map, {'bl_profile', 'st_profile_path', ...
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| | 246 | 'st_profile_prefix', 'st_profile_name_main', 'st_profile_suffix',...
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| | 247 | 'bl_time', 'bl_display_defparam', 'bl_graph_evf', 'bl_display', 'it_display_every', 'bl_post'});
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| | 248 | [bl_profile, st_profile_path, ...
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| | 249 | st_profile_prefix, st_profile_name_main, st_profile_suffix, ...
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| | 250 | bl_time, bl_display_defparam, bl_graph_evf, bl_display, it_display_every, bl_post] = params_group{:};
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| | 251 | params_group = values(support_map, {'it_display_summmat_rowmax', 'it_display_summmat_colmax'});
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| | 252 | [it_display_summmat_rowmax, it_display_summmat_colmax] = params_group{:};
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| | 253 |
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| | 254 | %% Initialize Output Matrixes
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| | 255 |
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| | 256 | mt_val_cur = zeros(length(ar_interp_coh_grid),it_z_n);
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| | 257 | mt_val = mt_val_cur - 1;
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| | 258 | mt_pol_a = zeros(length(ar_interp_coh_grid),it_z_n);
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| | 259 | mt_pol_a_cur = mt_pol_a - 1;
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| | 260 | mt_pol_k = zeros(length(ar_interp_coh_grid),it_z_n);
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| | 261 | mt_pol_k_cur = mt_pol_k - 1;
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| | 262 | mt_pol_idx = zeros(length(ar_interp_coh_grid),it_z_n);
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| | 263 |
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| | 264 | % collect optimal borrowing formal and informal choices
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| | 265 | % mt_pol_b_with_r: cost to t+1 consumption from borrowing in t
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| | 266 | mt_pol_b_with_r = zeros(length(ar_interp_coh_grid),it_z_n);
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| | 267 | mt_pol_b_bridge = zeros(length(ar_interp_coh_grid),it_z_n);
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| | 268 | mt_pol_inf_borr_nobridge = zeros(length(ar_interp_coh_grid),it_z_n);
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| | 269 | mt_pol_for_borr = zeros(length(ar_interp_coh_grid),it_z_n);
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| | 270 | mt_pol_for_save = zeros(length(ar_interp_coh_grid),it_z_n);
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| | 271 |
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| | 272 | % We did not need these in ff_oz_vf or ff_oz_vf_vec
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| | 273 | % see
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| | 274 | % <https://fanwangecon.github.io/M4Econ/support/speed/partupdate/fs_u_c_partrepeat_main.html
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| | 275 | % fs_u_c_partrepeat_main> for why store using cells.
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| | 276 | cl_u_c_store = cell([it_z_n, 1]);
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| | 277 | cl_c_valid_idx = cell([it_z_n, 1]);
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| | 278 | cl_w_kstar_interp_z = cell([it_z_n, 1]);
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| | 279 | for it_z_i = 1:it_z_n
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| | 280 | cl_w_kstar_interp_z{it_z_i} = zeros([length(ar_w_perc), length(ar_interp_coh_grid)]) - 1;
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| | 281 | end
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| | 282 |
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| | 283 | clmt_val_wkb_interpolated = cell([fl_z_r_infbr_n, 1]);
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| | 284 |
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| | 285 | %% Initialize Convergence Conditions
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| | 286 |
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| | 287 | bl_vfi_continue = true;
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| | 288 | it_iter = 0;
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| | 289 | ar_val_diff_norm = zeros([it_maxiter_val, 1]);
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| | 290 | ar_pol_diff_norm = zeros([it_maxiter_val, 1]);
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| | 291 | mt_pol_perc_change = zeros([it_maxiter_val, it_z_n]);
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| | 292 |
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| | 293 | %% Pre-calculate u(c)
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| | 294 | % Interpolation, see
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| | 295 | % <https://fanwangecon.github.io/M4Econ/support/speed/partupdate/fs_u_c_partrepeat_main.html
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| | 296 | % fs_u_c_partrepeat_main> for why interpolate over u(c)
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| | 297 |
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| | 298 | % Evaluate
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| | 299 | if (fl_crra == 1)
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| | 300 | ar_interp_u_of_c_grid = f_util_log(ar_interp_c_grid);
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| | 301 | fl_u_cmin = f_util_log(fl_c_min);
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| | 302 | else
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| | 303 | ar_interp_u_of_c_grid = f_util_crra(ar_interp_c_grid);
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| | 304 | fl_u_cmin = f_util_crra(fl_c_min);
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| | 305 | end
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| | 306 | ar_interp_u_of_c_grid(ar_interp_c_grid <= fl_c_min) = fl_u_cmin;
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| | 307 |
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| | 308 | % Get Interpolant
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| | 309 | f_grid_interpolant_spln = griddedInterpolant(ar_interp_c_grid, ar_interp_u_of_c_grid, 'spline', 'nearest');
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| | 310 |
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| | 311 | %% Iterate Value Function
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| | 312 | % Loop solution with 4 nested loops
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| | 313 | %
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| | 314 | % # loop 1: over exogenous states
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| | 315 | % # loop 2: over endogenous states
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| | 316 | % # loop 3: over choices
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| | 317 | % # loop 4: add future utility, integration--loop over future shocks
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| | 318 | %
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| | 319 |
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| | 320 | % Start Profile
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| | 321 | if (bl_profile)
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| | 322 | close all;
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| | 323 | profile off;
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| | 324 | profile on;
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< 0.001 | 1 | 325 | end
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| | 326 |
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| | 327 | % Start Timer
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< 0.001 | 1 | 328 | if (bl_time)
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< 0.001 | 1 | 329 | tic;
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< 0.001 | 1 | 330 | end
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| | 331 |
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| | 332 | % Value Function Iteration
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< 0.001 | 1 | 333 | while bl_vfi_continue
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< 0.001 | 131 | 334 | it_iter = it_iter + 1;
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| | 335 |
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| | 336 | %% Interpolate V(coh, Z) 1: Store in Cell
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| | 337 | % Interpolate reacahble V(coh(k'(w),b'(w),zr,zw'),zw',zr')) given v(coh, z)
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| | 338 |
|
0.003 | 131 | 339 | if (strcmp(st_v_coh_z_interp_method, "method_cell"))
|
| | 340 |
|
0.148 | 131 | 341 | f_grid_interpolant_value = griddedInterpolant(...
|
| 131 | 342 | mt_z_mesh_interp_coh_grid', mt_interp_coh_grid_mesh_z', ...
|
| 131 | 343 | mt_val_cur', 'linear', 'nearest');
|
| | 344 |
|
< 0.001 | 131 | 345 | for it_z_r_infbr_ctr = 1:1:fl_z_r_infbr_n
|
| | 346 |
|
53.744 | 655 | 347 | clmt_val_wkb_interpolated{it_z_r_infbr_ctr} = ...
|
| 655 | 348 | f_grid_interpolant_value(mt_z_mesh_coh_wkb_seg,...
|
| 655 | 349 | cl_mt_coh_wkb_mesh_z_r_infbr{it_z_r_infbr_ctr});
|
0.002 | 655 | 350 | end
|
< 0.001 | 131 | 351 | end
|
| | 352 |
|
| | 353 | %% Interpolate V(coh, Z) 5: Matrix Store
|
| | 354 | % Interpolate reacahble V(coh(k'(w),b'(w),zr,zw'),zw',zr')) given v(coh, z)
|
| | 355 | % This is the same as <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbzr/solve/html/ff_ipwkbzr_vf_vecsv.html
|
| | 356 | % ff_ipwkbzr_vf_vecsv>. For the FIBS problem, the cash-on-hand
|
| | 357 | % interpolation grid stays the same, and the shock grid stays the same
|
| | 358 | % as well. The results will not be the same, for example, the coh_grid
|
| | 359 | % max is the max of reachable cash-on-hand levels (min is however just
|
| | 360 | % the borrowing bound).
|
| | 361 |
|
0.003 | 131 | 362 | if (strcmp(st_v_coh_z_interp_method, "method_mat_seg"))
|
| | 363 |
|
| | 364 | % 1. Number of W/B/K Choice Combinations
|
| | 365 | it_ak_perc_n = length(ar_ak_perc);
|
| | 366 | it_w_interp_n = length(ar_w_level_full);
|
| | 367 | it_wak_n = it_w_interp_n*it_ak_perc_n;
|
| | 368 |
|
| | 369 | % 2. Initialize V(coh(k'(w),b'(w),zr,zw'),zw',zr'))
|
| | 370 | % mt_val_wkb_interpolated is: (I^k x I^w x M^r) by (M^z x M^r)
|
| | 371 | % reachable cash-on-hand (as rows) and shocks next period given choices
|
| | 372 | % and shocks next period.
|
| | 373 | clmt_val_wkb_interpolated = zeros([it_wak_n*fl_z_r_infbr_n, it_z_n]);
|
| | 374 |
|
| | 375 | % 3. Loop over possible shocks over interest rate
|
| | 376 | for it_z_r_infbr_ctr = 1:1:fl_z_r_infbr_n
|
| | 377 |
|
| | 378 | % 4. Interpolate V(coh(k',b',z',r),z',r') for a specific r'
|
| | 379 | % v(coh,z) solved on ar_interp_coh_grid, ar_z grids, see
|
| | 380 | % ffs_ipwkbzr_get_funcgrid.m. Generate interpolant based on that, Then
|
| | 381 | % interpolate for the coh reachable levels given the k(w,z) percentage
|
| | 382 | % choice grids in the second stage of the problem.
|
| | 383 | %
|
| | 384 | % Note mt_val_cur/mt_val dimension is based on interpolant
|
| | 385 | % cash-on-hand for rows, and meshed shocks for columns. The meshed
|
| | 386 | % shock structure, see
|
| | 387 | % <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbzr/paramfunc/html/ffs_ipwkbzr_get_funcgrid.html
|
| | 388 | % ffs_ipwkbzr_get_funcgrid> for details on how the shock grids are
|
| | 389 | % formed.
|
| | 390 |
|
| | 391 | % Get current z_r_infbr from mt_val
|
| | 392 | it_mt_val_col_start = it_z_wage_n*(it_z_r_infbr_ctr-1) + 1;
|
| | 393 | it_mt_val_col_end = it_mt_val_col_start + it_z_wage_n - 1;
|
| | 394 | mt_val_cur_rcolseg = mt_val_cur(:, it_mt_val_col_start:it_mt_val_col_end);
|
| | 395 |
|
| | 396 | % Generate Interpolant for v(coh,z)
|
| | 397 | % mt_z_wage_mesh_interp_coh_grid is: (I^{coh_interp}) by (M^z)
|
| | 398 | f_grid_interpolant_value = griddedInterpolant(...
|
| | 399 | mt_z_wage_mesh_interp_coh_grid', mt_interp_coh_grid_mesh_z_wage', ...
|
| | 400 | mt_val_cur_rcolseg', 'linear', 'nearest');
|
| | 401 |
|
| | 402 | % Interpolate V(coh(k',b',z',r),z',r') for a specific r'
|
| | 403 | % mt_z_wage_mesh_coh_wkb and mt_coh_wkb are: (I^k x I^w x M^r) by (M^z)
|
| | 404 | mt_val_wkb_interpolated_seg = f_grid_interpolant_value(mt_z_wage_mesh_coh_wkb, mt_coh_wkb);
|
| | 405 | clmt_val_wkb_interpolated(:, it_mt_val_col_start:it_mt_val_col_end) = mt_val_wkb_interpolated_seg;
|
| | 406 |
|
| | 407 | end
|
| | 408 | end
|
| | 409 |
|
| | 410 | %% Solve Second Stage Problem k*(w,z)
|
| | 411 | % This is again the same as <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbzr/solve/html/ff_ipwkbzr_vf_vecsv.html
|
| | 412 | % ff_ipwkbzr_vf_vecsv>. But the output matrix sizes are different.
|
| | 413 | % Previously, they were (length(ar_w_level)) by (it_z_n). Now
|
| | 414 | % have this thing which is stored (length(ar_w_level_full)) by
|
| | 415 | % (it_z_n). _ar_w_level_full_ includes not just different levels
|
| | 416 | % of _ar_w_level_, but also repeats the elements of _ar_w_level_ that
|
| | 417 | % are < 0 by _it_coh_bridge_perc_n_ times, starting with what
|
| | 418 | % corresponds to 100 percent of w should go to cover bridge loan, until
|
| | 419 | % 0 percent for w < 0, which then proceeds to w > 0. So the last
|
| | 420 | % segment of _ar_w_level_full_ is the same as ar_w_level:
|
| | 421 | % ar_w_level_full((end-length(ar_w_level)+1):end) = ar_w_level.
|
| | 422 |
|
0.016 | 131 | 423 | support_map('bl_graph_evf') = false;
|
< 0.001 | 131 | 424 | if (it_iter == (it_maxiter_val + 1))
|
< 0.001 | 1 | 425 | support_map('bl_graph_evf') = bl_graph_evf;
|
< 0.001 | 1 | 426 | end
|
< 0.001 | 131 | 427 | bl_input_override = true;
|
12.632 | 131 | 428 | [mt_ev_condi_z_max, ~, mt_ev_condi_z_max_kp, ~] = ...
|
| 131 | 429 | ff_ipwkbzr_fibs_evf(clmt_val_wkb_interpolated, param_map, support_map, armt_map, bl_input_override);
|
| | 430 |
|
| | 431 | %% Solve First Stage Problem w*(z) given k*(w,z)
|
| | 432 | % Refer to
|
| | 433 | % <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbzr/solve/html/ff_ipwkbzr_fibs_vf_vecsv.html
|
| | 434 | % ff_ipwkbzr_fibs_vf_vecsv> where the problem was solved without formal and
|
| | 435 | % informal choices that allow for bridge loans to see line by line how
|
| | 436 | % code differ. Some of the comments from that file are not here to save
|
| | 437 | % space. Comments here address differences and are specific to formal
|
| | 438 | % and informal choices.
|
| | 439 |
|
| | 440 | % loop 1: over exogenous states
|
< 0.001 | 131 | 441 | for it_z_i = 1:it_z_n
|
| | 442 |
|
| | 443 | %% A. Interpolate FULL to get k*(coh_level, w_perc, z), b*(k,w) based on k*(coh_perc, w_level)
|
| | 444 | % additionally, Interpolate FULL EV(k*(coh_level, w_perc, z), w -
|
| | 445 | % b*|z) based on EV(k*(coh_perc, w_level))
|
| | 446 | %
|
| | 447 | % we solved the second period problem in ff_ipwkbzr_fibs_fibs_evf.m
|
| | 448 | % above. To use results, we need to interpolate in the following
|
| | 449 | % way to obtain *mt_w_kstar_interp_z* as well as
|
| | 450 | % *mt_ev_condi_z_max_interp_z*:
|
| | 451 | %
|
| | 452 | % # Interp STG1A: for $w > 0$, 1D interpolate over w level, given z
|
| | 453 | % # Interp STG1B: for $w < 0$, 2D interpolate over w level and coh
|
| | 454 | % perceng, given z
|
| | 455 | %
|
| | 456 |
|
| | 457 | % 1. Negative Elements of w grid expanded by w percentages for bridge
|
0.035 | 9825 | 458 | ar_bl_w_level_full_neg = (ar_w_level_full < 0);
|
| | 459 |
|
| | 460 | % 2. Current Positve w and negative w optimal k choices
|
0.038 | 9825 | 461 | it_wneg_mt_row = sum(ar_bl_w_level_full_neg)/length(ar_coh_bridge_perc);
|
| | 462 | % for mt_ev_condi_z_max_kp
|
0.122 | 9825 | 463 | ar_ev_condi_z_max_kp_wpos = mt_ev_condi_z_max_kp(~ar_bl_w_level_full_neg, it_z_i)';
|
0.085 | 9825 | 464 | ar_ev_condi_z_max_kp_wneg = mt_ev_condi_z_max_kp(ar_bl_w_level_full_neg, it_z_i)';
|
0.035 | 9825 | 465 | mt_ev_condi_z_max_kp_wneg = reshape(ar_ev_condi_z_max_kp_wneg, [it_wneg_mt_row, length(ar_coh_bridge_perc)]);
|
| | 466 | % for mt_ev_condi_z_max
|
0.094 | 9825 | 467 | ar_ev_condi_z_max_wpos = mt_ev_condi_z_max(~ar_bl_w_level_full_neg, it_z_i)';
|
0.080 | 9825 | 468 | ar_ev_condi_z_max_wneg = mt_ev_condi_z_max(ar_bl_w_level_full_neg, it_z_i)';
|
0.016 | 9825 | 469 | mt_ev_condi_z_max_wneg = reshape(ar_ev_condi_z_max_wneg, [it_wneg_mt_row, length(ar_coh_bridge_perc)]);
|
| | 470 |
|
| | 471 | % 2. Interp STG1A for w > 0
|
0.084 | 9825 | 472 | ar_w_level_full_pos = ar_w_level_full(~ar_bl_w_level_full_neg);
|
| | 473 | % Interpolation for mt_ev_condi_z_max_kp
|
0.413 | 9825 | 474 | f_interpolante_w_level_pos_kstar_z = griddedInterpolant(ar_w_level_full_pos, ar_ev_condi_z_max_kp_wpos, 'linear', 'nearest');
|
1.909 | 9825 | 475 | mt_w_kstar_interp_z_wpos = f_interpolante_w_level_pos_kstar_z(mt_w_perc_mesh_interp_coh_grid_wpos(:));
|
0.140 | 9825 | 476 | mt_w_astar_interp_z_wpos = mt_w_perc_mesh_interp_coh_grid_wpos(:) - mt_w_kstar_interp_z_wpos;
|
| | 477 | % Interpolation for mt_ev_condi_z_max
|
0.461 | 9825 | 478 | f_interpolante_w_level_pos_ev_z = griddedInterpolant(ar_w_level_full_pos, ar_ev_condi_z_max_wpos, 'linear', 'nearest');
|
1.642 | 9825 | 479 | mt_w_ev_interp_z_wpos = f_interpolante_w_level_pos_ev_z(mt_w_perc_mesh_interp_coh_grid_wpos(:));
|
| | 480 |
|
| | 481 | % 3. Interp STG1B for w <= 0
|
0.003 | 9825 | 482 | if (bl_bridge)
|
| | 483 | % Interpolation for mt_ev_condi_z_max_kp
|
0.669 | 9825 | 484 | f_interpolante_w_level_neg_kstar_z = griddedInterpolant(...
|
| 9825 | 485 | mt_coh_bridge_perc_mesh_w_level_neg', mt_w_level_neg_mesh_coh_bridge_perc', ...
|
| 9825 | 486 | mt_ev_condi_z_max_kp_wneg', 'linear', 'nearest');
|
3.306 | 9825 | 487 | mt_w_kstar_interp_z_wneg = f_interpolante_w_level_neg_kstar_z(mt_coh_w_perc_ratio_wneg(:), mt_w_perc_mesh_interp_coh_grid_wneg(:));
|
0.191 | 9825 | 488 | mt_w_astar_interp_z_wneg = mt_w_perc_mesh_interp_coh_grid_wneg(:) - mt_w_kstar_interp_z_wneg;
|
| | 489 | % Interpolation for mt_ev_condi_z_max
|
0.716 | 9825 | 490 | f_interpolante_w_level_neg_ev_z = griddedInterpolant(...
|
| 9825 | 491 | mt_coh_bridge_perc_mesh_w_level_neg', mt_w_level_neg_mesh_coh_bridge_perc', ...
|
| 9825 | 492 | mt_ev_condi_z_max_wneg', 'linear', 'nearest');
|
3.324 | 9825 | 493 | mt_w_ev_interp_z_wneg = f_interpolante_w_level_neg_ev_z(mt_coh_w_perc_ratio_wneg(:), mt_w_perc_mesh_interp_coh_grid_wneg(:));
|
| | 494 | else
|
| | 495 | ar_w_level_full_neg = ar_w_level_full(ar_bl_w_level_full_neg);
|
| | 496 | % Interpolation for mt_ev_condi_z_max_kp
|
| | 497 | f_interpolante_w_level_neg_kstar_z = griddedInterpolant(ar_w_level_full_neg, ar_ev_condi_z_max_kp_wneg, 'linear', 'nearest');
|
| | 498 | mt_w_kstar_interp_z_wneg = f_interpolante_w_level_neg_kstar_z(mt_w_perc_mesh_interp_coh_grid_wneg(:));
|
| | 499 | mt_w_astar_interp_z_wneg = mt_w_perc_mesh_interp_coh_grid_wneg(:) - mt_w_kstar_interp_z_wneg;
|
| | 500 | % Interpolation for mt_ev_condi_z_max
|
| | 501 | f_interpolante_w_level_neg_ev_z = griddedInterpolant(ar_w_level_full_neg, ar_ev_condi_z_max_wneg, 'linear', 'nearest');
|
| | 502 | mt_w_ev_interp_z_wneg = f_interpolante_w_level_neg_ev_z(mt_w_perc_mesh_interp_coh_grid_wneg(:));
|
0.002 | 9825 | 503 | end
|
| | 504 |
|
| | 505 | % 4. Combine positive and negative aggregate savings matrix
|
| | 506 | % check: mt_w_by_interp_coh_interp_grid vs mt_w_astar_interp_z + mt_w_kstar_interp_z
|
| | 507 | % combine for mt_ev_condi_z_max_kp
|
0.728 | 9825 | 508 | mt_w_kstar_interp_z = zeros(size(mt_bl_w_perc_mesh_interp_coh_grid_wneg));
|
2.021 | 9825 | 509 | mt_w_kstar_interp_z(~mt_bl_w_perc_mesh_interp_coh_grid_wneg) = mt_w_kstar_interp_z_wpos;
|
1.452 | 9825 | 510 | mt_w_kstar_interp_z(mt_bl_w_perc_mesh_interp_coh_grid_wneg) = mt_w_kstar_interp_z_wneg;
|
1.203 | 9825 | 511 | mt_w_astar_interp_z = zeros(size(mt_bl_w_perc_mesh_interp_coh_grid_wneg));
|
2.015 | 9825 | 512 | mt_w_astar_interp_z(~mt_bl_w_perc_mesh_interp_coh_grid_wneg) = mt_w_astar_interp_z_wpos;
|
1.403 | 9825 | 513 | mt_w_astar_interp_z(mt_bl_w_perc_mesh_interp_coh_grid_wneg) = mt_w_astar_interp_z_wneg;
|
| | 514 | % combine for mt_ev_condi_z_max
|
1.179 | 9825 | 515 | mt_ev_condi_z_max_interp_z = zeros(size(mt_bl_w_perc_mesh_interp_coh_grid_wneg));
|
2.014 | 9825 | 516 | mt_ev_condi_z_max_interp_z(~mt_bl_w_perc_mesh_interp_coh_grid_wneg) = mt_w_ev_interp_z_wpos;
|
1.431 | 9825 | 517 | mt_ev_condi_z_max_interp_z(mt_bl_w_perc_mesh_interp_coh_grid_wneg) = mt_w_ev_interp_z_wneg;
|
| | 518 |
|
| | 519 | % 5. changes in w_perc kstar choices
|
0.420 | 9825 | 520 | mt_w_kstar_diff_idx = (cl_w_kstar_interp_z{it_z_i} ~= mt_w_kstar_interp_z);
|
| | 521 |
|
| | 522 | %% B. Calculate UPDATE u(c) Update: u(c(coh_level, w_perc)) given k*_interp, b*_interp
|
1.677 | 9825 | 523 | ar_c = f_cons(mt_interp_coh_grid_mesh_w_perc(mt_w_kstar_diff_idx), ...
|
| 9825 | 524 | mt_w_astar_interp_z(mt_w_kstar_diff_idx), ...
|
| 9825 | 525 | mt_w_kstar_interp_z(mt_w_kstar_diff_idx));
|
| | 526 |
|
0.055 | 9825 | 527 | ar_it_c_valid_idx = (ar_c <= fl_c_min);
|
| | 528 | % EVAL current utility: N by N, f_util defined earlier
|
0.852 | 9825 | 529 | ar_utility_update = f_grid_interpolant_spln(ar_c);
|
| | 530 |
|
| | 531 | % Update Storage
|
0.004 | 9825 | 532 | if (it_iter == 1)
|
0.001 | 75 | 533 | cl_u_c_store{it_z_i} = reshape(ar_utility_update, [length(ar_w_perc), length(ar_interp_coh_grid)]);
|
< 0.001 | 75 | 534 | cl_c_valid_idx{it_z_i} = reshape(ar_it_c_valid_idx, [length(ar_w_perc), length(ar_interp_coh_grid)]);
|
0.002 | 9750 | 535 | else
|
0.424 | 9750 | 536 | cl_u_c_store{it_z_i}(mt_w_kstar_diff_idx) = ar_utility_update;
|
0.358 | 9750 | 537 | cl_c_valid_idx{it_z_i}(mt_w_kstar_diff_idx) = ar_it_c_valid_idx;
|
0.001 | 9825 | 538 | end
|
0.680 | 9825 | 539 | cl_w_kstar_interp_z{it_z_i} = mt_w_kstar_interp_z;
|
| | 540 |
|
| | 541 | %% D. Compute FULL U(coh_level, w_perc, z) over all w_perc
|
0.407 | 9825 | 542 | mt_utility = cl_u_c_store{it_z_i} + fl_beta*mt_ev_condi_z_max_interp_z;
|
| | 543 |
|
| | 544 | % Index update
|
| | 545 | % using the method below is much faster than index replace
|
| | 546 | % see <https://fanwangecon.github.io/M4Econ/support/speed/index/fs_subscript.html fs_subscript>
|
0.009 | 9825 | 547 | mt_it_c_valid_idx = cl_c_valid_idx{it_z_i};
|
| | 548 | % Default or Not Utility Handling
|
0.003 | 9825 | 549 | if (bl_default)
|
| | 550 | % if default: only today u(cmin), transition out next period, debt wiped out
|
0.361 | 9825 | 551 | fl_v_default = fl_u_cmin + fl_beta*f_interpolante_w_level_pos_ev_z(fl_default_wprime);
|
0.326 | 9825 | 552 | mt_utility = mt_utility.*(~mt_it_c_valid_idx) + fl_v_default*(mt_it_c_valid_idx);
|
| | 553 | else
|
| | 554 | % if default is not allowed: v = u(cmin)
|
| | 555 | mt_utility = mt_utility.*(~mt_it_c_valid_idx) + fl_nan_replace*(mt_it_c_valid_idx);
|
0.002 | 9825 | 556 | end
|
| | 557 |
|
| | 558 | % percentage algorithm does not have invalid (check to make sure
|
| | 559 | % min percent is not 0 in ffs_ipwkbzr_fibs_get_funcgrid.m)
|
| | 560 | % mt_utility = mt_utility.*(~mt_it_c_valid_idx) + fl_u_neg_c*(mt_it_c_valid_idx);
|
| | 561 |
|
| | 562 | %% E. Optimize Over Choices: max_{w_perc} U(coh_level, w_perc, z)
|
| | 563 | % Optimization: remember matlab is column major, rows must be
|
| | 564 | % choices, columns must be states
|
| | 565 | % <https://en.wikipedia.org/wiki/Row-_and_column-major_order COLUMN-MAJOR>
|
0.823 | 9825 | 566 | [ar_opti_val_z, ar_opti_idx_z] = max(mt_utility);
|
| | 567 |
|
| | 568 | % Generate Linear Opti Index
|
0.050 | 9825 | 569 | [it_choies_n, it_states_n] = size(mt_utility);
|
0.189 | 9825 | 570 | ar_add_grid = linspace(0, it_choies_n*(it_states_n-1), it_states_n);
|
0.013 | 9825 | 571 | ar_opti_linear_idx_z = ar_opti_idx_z + ar_add_grid;
|
| | 572 |
|
0.107 | 9825 | 573 | ar_opti_aprime_z = mt_w_astar_interp_z(ar_opti_linear_idx_z);
|
0.102 | 9825 | 574 | ar_opti_kprime_z = mt_w_kstar_interp_z(ar_opti_linear_idx_z);
|
0.113 | 9825 | 575 | ar_opti_c_z = f_cons(ar_interp_coh_grid, ar_opti_aprime_z, ar_opti_kprime_z);
|
| | 576 |
|
| | 577 | % Handle Default is optimal or not
|
0.002 | 9825 | 578 | if (bl_default)
|
| | 579 | % if defaulting is optimal choice, at these states, not required
|
| | 580 | % to default, non-default possible, but default could be optimal
|
0.320 | 9825 | 581 | fl_default_opti_kprime = f_interpolante_w_level_pos_kstar_z(fl_default_wprime);
|
0.053 | 9825 | 582 | ar_opti_aprime_z(ar_opti_c_z <= fl_c_min) = fl_default_wprime - fl_default_opti_kprime;
|
0.026 | 9825 | 583 | ar_opti_kprime_z(ar_opti_c_z <= fl_c_min) = fl_default_opti_kprime;
|
| | 584 | else
|
| | 585 | % if default is not allowed, then next period same state as now
|
| | 586 | % this is absorbing state, this is the limiting case, single
|
| | 587 | % state space point, lowest a and lowest shock has this.
|
| | 588 | ar_opti_aprime_z(ar_opti_c_z <= fl_c_min) = min(ar_a_meshk);
|
| | 589 | ar_opti_kprime_z(ar_opti_c_z <= fl_c_min) = min(ar_k_mesha);
|
0.001 | 9825 | 590 | end
|
| | 591 |
|
| | 592 | %% F. Store Results
|
0.047 | 9825 | 593 | mt_val(:,it_z_i) = ar_opti_val_z;
|
0.035 | 9825 | 594 | mt_pol_a(:,it_z_i) = ar_opti_aprime_z;
|
0.035 | 9825 | 595 | mt_pol_k(:,it_z_i) = ar_opti_kprime_z;
|
0.004 | 9825 | 596 | if (it_iter == (it_maxiter_val + 1))
|
< 0.001 | 75 | 597 | mt_pol_idx(:,it_z_i) = ar_opti_linear_idx_z;
|
< 0.001 | 75 | 598 | end
|
| | 599 |
|
0.004 | 9825 | 600 | end
|
| | 601 |
|
| | 602 | %% Check Tolerance and Continuation
|
| | 603 |
|
| | 604 | % Difference across iterations
|
0.380 | 131 | 605 | ar_val_diff_norm(it_iter) = norm(mt_val - mt_val_cur);
|
0.740 | 131 | 606 | ar_pol_diff_norm(it_iter) = norm(mt_pol_a - mt_pol_a_cur) + norm(mt_pol_k - mt_pol_k_cur);
|
0.020 | 131 | 607 | ar_pol_a_perc_change = sum((mt_pol_a ~= mt_pol_a_cur))/(length(ar_interp_coh_grid));
|
0.016 | 131 | 608 | ar_pol_k_perc_change = sum((mt_pol_k ~= mt_pol_k_cur))/(length(ar_interp_coh_grid));
|
0.017 | 131 | 609 | mt_pol_perc_change(it_iter, :) = mean([ar_pol_a_perc_change;ar_pol_k_perc_change]);
|
| | 610 |
|
| | 611 | % Update
|
0.008 | 131 | 612 | mt_val_cur = mt_val;
|
0.006 | 131 | 613 | mt_pol_a_cur = mt_pol_a;
|
0.006 | 131 | 614 | mt_pol_k_cur = mt_pol_k;
|
| | 615 |
|
| | 616 | % Print Iteration Results
|
< 0.001 | 131 | 617 | if (bl_display && (rem(it_iter, it_display_every)==0))
|
| | 618 | fprintf('VAL it_iter:%d, fl_diff:%d, fl_diff_pol:%d\n', ...
|
| | 619 | it_iter, ar_val_diff_norm(it_iter), ar_pol_diff_norm(it_iter));
|
| | 620 | tb_valpol_iter = array2table([mean(mt_val_cur,1);...
|
| | 621 | mean(mt_pol_a_cur,1); ...
|
| | 622 | mean(mt_pol_k_cur,1); ...
|
| | 623 | mt_val_cur(length(ar_interp_coh_grid),:); ...
|
| | 624 | mt_pol_a_cur(length(ar_interp_coh_grid),:); ...
|
| | 625 | mt_pol_k_cur(length(ar_interp_coh_grid),:)]);
|
| | 626 | tb_valpol_iter.Properties.VariableNames = strcat('z', string((1:size(mt_val_cur,2))));
|
| | 627 | tb_valpol_iter.Properties.RowNames = {'mval', 'map', 'mak', 'Hval', 'Hap', 'Hak'};
|
| | 628 | disp('mval = mean(mt_val_cur,1), average value over a')
|
| | 629 | disp('map = mean(mt_pol_a_cur,1), average choice over a')
|
| | 630 | disp('mkp = mean(mt_pol_k_cur,1), average choice over k')
|
| | 631 | disp('Hval = mt_val_cur(it_ameshk_n,:), highest a state val')
|
| | 632 | disp('Hap = mt_pol_a_cur(it_ameshk_n,:), highest a state choice')
|
| | 633 | disp('mak = mt_pol_k_cur(it_ameshk_n,:), highest k state choice')
|
| | 634 | disp(tb_valpol_iter);
|
| | 635 | end
|
| | 636 |
|
| | 637 | % Continuation Conditions:
|
| | 638 | % 1. if value function convergence criteria reached
|
| | 639 | % 2. if policy function variation over iterations is less than
|
| | 640 | % threshold
|
< 0.001 | 131 | 641 | if (it_iter == (it_maxiter_val + 1))
|
< 0.001 | 1 | 642 | bl_vfi_continue = false;
|
0.002 | 130 | 643 | elseif ((it_iter == it_maxiter_val) || ...
|
| 130 | 644 | (ar_val_diff_norm(it_iter) < fl_tol_val) || ...
|
| 130 | 645 | (sum(ar_pol_diff_norm(max(1, it_iter-it_tol_pol_nochange):it_iter)) < fl_tol_pol))
|
| | 646 | % Fix to max, run again to save results if needed
|
< 0.001 | 1 | 647 | it_iter_last = it_iter;
|
< 0.001 | 1 | 648 | it_iter = it_maxiter_val;
|
< 0.001 | 1 | 649 | end
|
| | 650 |
|
< 0.001 | 131 | 651 | end
|
| | 652 |
|
| | 653 | % End Timer
|
< 0.001 | 1 | 654 | if (bl_time)
|
< 0.001 | 1 | 655 | toc;
|
< 0.001 | 1 | 656 | end
|
| | 657 |
|
| | 658 | % End Profile
|
< 0.001 | 1 | 659 | if (bl_profile)
|
0.005 | 1 | 660 | profile off
|
| | 661 | profile viewer
|
| | 662 | st_file_name = [st_profile_prefix st_profile_name_main st_profile_suffix];
|
| | 663 | profsave(profile('info'), strcat(st_profile_path, st_file_name));
|
| | 664 | end
|
| | 665 |
|
| | 666 | %% Process Optimal Choices 1: Formal and Informal Choices
|
| | 667 |
|
| | 668 | result_map = containers.Map('KeyType','char', 'ValueType','any');
|
| | 669 | result_map('mt_val') = mt_val;
|
| | 670 | result_map('mt_pol_idx') = mt_pol_idx;
|
| | 671 |
|
| | 672 | % Find optimal Formal Informal Choices. Could have saved earlier, but was
|
| | 673 | % wasteful of resources
|
| | 674 | for it_z_i = 1:it_z_n
|
| | 675 | for it_coh_interp_j = 1:length(ar_interp_coh_grid)
|
| | 676 |
|
| | 677 | fl_coh = mt_interp_coh_grid_mesh_z(it_coh_interp_j, it_z_i);
|
| | 678 | fl_a_opti = mt_pol_a(it_coh_interp_j, it_z_i);
|
| | 679 |
|
| | 680 | fl_z_r_infbr = ar_z_r_infbr_mesh_wage_w1r2(it_z_i);
|
| | 681 | param_map('fl_r_inf') = fl_z_r_infbr;
|
| | 682 |
|
| | 683 | % call formal and informal function.
|
| | 684 | [fl_max_c, fl_opti_b_bridge, fl_opti_inf_borr_nobridge, fl_opti_for_borr, fl_opti_for_save] = ...
|
| | 685 | ffs_fibs_min_c_cost_bridge(fl_a_opti, fl_coh, ...
|
| | 686 | param_map, support_map, armt_map, func_map, bl_input_override);
|
| | 687 |
|
| | 688 | % store savings and borrowing formal and inf optimal choices
|
| | 689 | mt_pol_b_with_r(it_coh_interp_j,it_z_i) = fl_max_c;
|
| | 690 | mt_pol_b_bridge(it_coh_interp_j,it_z_i) = fl_opti_b_bridge;
|
| | 691 | mt_pol_inf_borr_nobridge(it_coh_interp_j,it_z_i) = fl_opti_inf_borr_nobridge;
|
| | 692 | mt_pol_for_borr(it_coh_interp_j,it_z_i) = fl_opti_for_borr;
|
| | 693 | mt_pol_for_save(it_coh_interp_j,it_z_i) = fl_opti_for_save;
|
| | 694 |
|
| | 695 | end
|
| | 696 | end
|
| | 697 |
|
| | 698 | %% Process Optimal Choices 2: Store a, k, c, coh Results
|
| | 699 | %
|
| | 700 | % # *mt_interp_coh_grid_mesh_z*: Cash-on-hand period _t_.
|
| | 701 | % # *mt_pol_a*: Safe asset choice, principles only for ipwkbzr_fibs
|
| | 702 | % # *cl_mt_pol_a_principleonly*: mt_pol_a is stored in
|
| | 703 | % cl_mt_pol_a_principle only. This is a shortcut because we need to keep
|
| | 704 | % cl_mt_pol_a for mt_pol_b_with_r for the _ds_ code.
|
| | 705 | % # *cl_mt_pol_a*: stores _mt_pol_b_with_r_ which has principles and
|
| | 706 | % interest rates, to be used with _ds_ code.
|
| | 707 | % # *mt_pol_a*: Safe asset choice, principles only for ipwkbzr_fibs
|
| | 708 | % # *cl_mt_pol_k*: Risky asset choice, principles only for ipwkbzr_fibs
|
| | 709 | % # *cl_mt_pol_c*: Consumption in _t_ given choices.
|
| | 710 | % # *cl_pol_b_with_r*: Consumption cost of _mt_pol_a_ in _t+1_, given the
|
| | 711 | % formal and informal choices that are optimal to minimize this consumption
|
| | 712 | % cost.
|
| | 713 | %
|
| | 714 |
|
| | 715 | result_map('cl_mt_coh') = {mt_interp_coh_grid_mesh_z, zeros(1)};
|
| | 716 | result_map('cl_mt_pol_k') = {mt_pol_k, zeros(1)};
|
| | 717 | mt_c = f_cons(mt_interp_coh_grid_mesh_z, mt_pol_a, mt_pol_k);
|
| | 718 | mt_c(mt_c <= fl_c_min) = fl_c_min;
|
| | 719 | result_map('cl_mt_pol_c') = {mt_c, zeros(1)};
|
| | 720 |
|
| | 721 | result_map('cl_mt_pol_a') = {mt_pol_b_with_r, zeros(1)};
|
| | 722 | result_map('cl_mt_pol_a_principleonly') = {mt_pol_a, zeros(1)};
|
| | 723 |
|
| | 724 | %% Process Optimal Choices 3: Store Formal and Informal Choices
|
| | 725 | result_map('cl_mt_pol_b_bridge') = {mt_pol_b_bridge, zeros(1)};
|
| | 726 | result_map('cl_mt_pol_inf_borr_nobridge') = {mt_pol_inf_borr_nobridge, zeros(1)};
|
| | 727 | result_map('cl_mt_pol_for_borr') = {mt_pol_for_borr, zeros(1)};
|
| | 728 | result_map('cl_mt_pol_for_save') = {mt_pol_for_save, zeros(1)};
|
| | 729 |
|
| | 730 | % Get Discrete Choice Outcomes
|
| | 731 | result_map = ffs_fibs_identify_discrete(result_map, bl_input_override);
|
| | 732 | result_map('cl_mt_it_for_only_nbdg') = {result_map('mt_it_for_only_nbdg'), zeros(1)};
|
| | 733 | result_map('cl_mt_it_inf_only_nbdg') = {result_map('mt_it_inf_only_nbdg'), zeros(1)};
|
| | 734 | result_map('cl_mt_it_frin_brr_nbdg') = {result_map('mt_it_frin_brr_nbdg'), zeros(1)};
|
| | 735 | result_map('cl_mt_it_fr_brrsv_nbdg') = {result_map('mt_it_fr_brrsv_nbdg'), zeros(1)};
|
| | 736 | result_map('cl_mt_it_frmsavng_only') = {result_map('mt_it_frmsavng_only'), zeros(1)};
|
| | 737 |
|
| | 738 | %% Process Optimal Choices 4: List of Variable Names to be processed by distributional codes
|
| | 739 | % this list is needed for the ds codes to generate distribution,
|
| | 740 | % distributional statistcs will be computed for elements in the list here.
|
| | 741 |
|
| | 742 | result_map('ar_st_pol_names') = ...
|
| | 743 | ["cl_mt_coh", "cl_mt_pol_a", "cl_mt_pol_k", "cl_mt_pol_c", "cl_mt_pol_a_principleonly", ...
|
| | 744 | "cl_mt_pol_b_bridge", "cl_mt_pol_inf_borr_nobridge", "cl_mt_pol_for_borr", "cl_mt_pol_for_save", ...
|
| | 745 | "cl_mt_it_for_only_nbdg", "cl_mt_it_inf_only_nbdg", "cl_mt_it_frin_brr_nbdg", ...
|
| | 746 | "cl_mt_it_fr_brrsv_nbdg", "cl_mt_it_frmsavng_only"];
|
| | 747 |
|
| | 748 | %% Post Solution Graph and Table Generation
|
| | 749 |
|
| | 750 | if (bl_post)
|
| | 751 | bl_input_override = true;
|
| | 752 | result_map('ar_val_diff_norm') = ar_val_diff_norm(1:it_iter_last);
|
| | 753 | result_map('ar_pol_diff_norm') = ar_pol_diff_norm(1:it_iter_last);
|
| | 754 | result_map('mt_pol_perc_change') = mt_pol_perc_change(1:it_iter_last, :);
|
| | 755 |
|
| | 756 | armt_map('mt_coh_wkb_ori') = mt_coh_wkb;
|
| | 757 | armt_map('ar_a_meshk_ori') = ar_a_meshk;
|
| | 758 | armt_map('ar_k_mesha_ori') = ar_k_mesha;
|
| | 759 |
|
| | 760 | armt_map('mt_coh_wkb') = mt_interp_coh_grid_mesh_z;
|
| | 761 | armt_map('it_ameshk_n') = length(ar_interp_coh_grid);
|
| | 762 | armt_map('ar_a_meshk') = mt_interp_coh_grid_mesh_z(:,1);
|
| | 763 | armt_map('ar_k_mesha') = zeros(size(mt_interp_coh_grid_mesh_z(:,1)) + 0);
|
| | 764 |
|
| | 765 | % Standard AZ graphs
|
| | 766 | result_map = ff_akz_vf_post(param_map, support_map, armt_map, func_map, result_map, bl_input_override);
|
| | 767 |
|
| | 768 | % Graphs for results_map with FIBS contents
|
| | 769 | armt_map('ar_a') = ar_interp_coh_grid;
|
| | 770 | result_map = ff_az_fibs_vf_post(param_map, support_map, armt_map, func_map, result_map, bl_input_override);
|
| | 771 |
|
| | 772 | end
|
| | 773 |
|
| | 774 | %% Display Various Containers
|
| | 775 |
|
| | 776 | if (bl_display_defparam)
|
| | 777 |
|
| | 778 | %% Display 1 support_map
|
| | 779 | fft_container_map_display(support_map, it_display_summmat_rowmax, it_display_summmat_colmax);
|
| | 780 |
|
| | 781 | %% Display 2 armt_map
|
| | 782 | fft_container_map_display(armt_map, it_display_summmat_rowmax, it_display_summmat_colmax);
|
| | 783 |
|
| | 784 | %% Display 3 param_map
|
| | 785 | fft_container_map_display(param_map, it_display_summmat_rowmax, it_display_summmat_colmax);
|
| | 786 |
|
| | 787 | %% Display 4 func_map
|
| | 788 | fft_container_map_display(func_map, it_display_summmat_rowmax, it_display_summmat_colmax);
|
| | 789 |
|
| | 790 | %% Display 5 result_map
|
| | 791 | fft_container_map_display(result_map, it_display_summmat_rowmax, it_display_summmat_colmax);
|
| | 792 |
|
| | 793 | end
|
| | 794 |
|
| | 795 | end
|
Other subfunctions in this file are not included in this listing.