Go to the MLX, M, PDF, or HTML version of this file. Go back to fan’s MEconTools Toolbox (bookdown), Matlab Code Examples Repository (bookdown), or Math for Econ with Matlab Repository (bookdown).
Examples](https://fanwangecon.github.io/M4Econ/), or** Dynamic Asset This is the example vignette for function: ff_disc_rand_var_mass2outcomes from the MEconTools Package. This function generates sorted discrete random variable from state-space joint distribution.
Call the function with defaults.
ff_disc_rand_var_mass2outcomes();
INPUT f(a,z): mt_dist_bystates
0.0289 0.0465 0.0228 0.0036 0.0001
0.0241 0.0930 0.0857 0.0241 0.0015
0.0080 0.0744 0.1285 0.0643 0.0074
0.0013 0.0297 0.0964 0.0857 0.0186
0.0001 0.0059 0.0361 0.0571 0.0232
0.0000 0.0005 0.0054 0.0152 0.0116
INPUT y(a,z): mt_choice_bystates
-5 -4 -5 -4 -4
-3 -2 -3 -2 -3
-1 -1 -1 0 0
1 1 2 3 1
4 3 3 4 3
5 6 5 6 6
OUTPUT f(y): ar_choice_prob_byY
0.0518
0.0502
0.1113
0.1171
0.2109
0.0717
0.0497
0.0964
0.1510
0.0572
0.0054
0.0273
OUTPUT f(y,z): mt_choice_prob_byYZ
0.0289 0 0.0228 0 0
0 0.0465 0 0.0036 0.0001
0.0241 0 0.0857 0 0.0015
0 0.0930 0 0.0241 0
0.0080 0.0744 0.1285 0 0
0 0 0 0.0643 0.0074
0.0013 0.0297 0 0 0.0186
0 0 0.0964 0 0
0 0.0059 0.0361 0.0857 0.0232
0.0001 0 0 0.0571 0
0.0000 0 0.0054 0 0
0 0.0005 0 0.0152 0.0116
OUTPUT f(y,a): mt_choice_prob_byYA
0.0518 0 0 0 0 0
0.0502 0 0 0 0 0
0 0.1113 0 0 0 0
0 0.1171 0 0 0 0
0 0 0.2109 0 0 0
0 0 0.0717 0 0 0
0 0 0 0.0497 0 0
0 0 0 0.0964 0 0
0 0 0 0.0857 0.0653 0
0 0 0 0 0.0572 0
0 0 0 0 0 0.0054
0 0 0 0 0 0.0273
OUTPUT f(y) and y in table: tb_choice_drv_cur_byY
binomtestOutcomes probMassFunction
_________________ ________________
-5 0.051764
-4 0.050217
-3 0.11126
-2 0.11706
-1 0.21092
0 0.071696
1 0.049682
2 0.096388
3 0.15102
4 0.057231
5 0.0054256
6 0.027329 Over some (a,z) states that is 2 by 2, matrix or vectorized inputs identical results.
% Set Parameters
st_y_name = 'consumption';
% consumption matrix: c(a,z)
mt_c_of_s = [1,2;3,1];
% stationary mass over assets adn shocks: f(a,z)
mt_f_of_s = rand(size(mt_c_of_s));
mt_f_of_s = mt_f_of_s/sum(mt_f_of_s, 'all');
% Call Function
[ar_f_of_y, ar_y_unique_sorted] = ...
ff_disc_rand_var_mass2outcomes(st_y_name, mt_c_of_s, mt_f_of_s);
% print
disp([ar_f_of_y ar_y_unique_sorted]);
0.4039 1.0000
0.2971 2.0000
0.2990 3.0000Same as before, but now inputs are single column:
% Call Function
[ar_f_of_y, ar_y_unique_sorted] = ...
ff_disc_rand_var_mass2outcomes(st_y_name, mt_c_of_s(:), mt_f_of_s);
disp([ar_f_of_y ar_y_unique_sorted]);
0.4039 1.0000
0.2971 2.0000
0.2990 3.0000Same inputs as before, but now, also output additional conditional statistis, f(y, a), where a is the row state variable for f(a,z). For conditional statistics, must provide matrix based inputs.
% Set Parameters
st_y_name = 'consumption';
% consumption matrix: c(a,z)
mt_c_of_s = [1,2,0.5;
3,1,2.0];
% stationary mass over assets adn shocks: f(a,z)
mt_f_of_s = rand(size(mt_c_of_s));
mt_f_of_s = mt_f_of_s/sum(mt_f_of_s, 'all');
% Call Function
[ar_f_of_y, ar_y_unique_sorted, mt_f_of_y_srow, mt_f_of_y_scol] = ...
ff_disc_rand_var_mass2outcomes(st_y_name, mt_c_of_s, mt_f_of_s);
% print
disp([ar_f_of_y ar_y_unique_sorted]);
0.2695 0.5000
0.3765 1.0000
0.2649 2.0000
0.0891 3.0000
disp(mt_f_of_y_srow);
0.2695 0
0.1215 0.2550
0.1217 0.1432
0 0.0891
disp(mt_f_of_y_scol);
0 0 0.2695
0.1215 0.2550 0
0 0.1217 0.1432
0.0891 0 0