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_simu_stats from the MEconTools Package. This is a gate-way function that computes mean, percentiles, covariance etc between several variables.
Call the function with defaults.
ff_simu_stats();
xxx tb_outcomes: all stats xxx
OriginalVariableNames cl_mt_pol_a cl_mt_pol_c
______________________ ___________ ___________
{'mean' } -0.11081 8.8423
{'sd' } 4.1239 6.5845
{'coefofvar' } -37.215 0.74466
{'min' } -7 -6.3772
{'max' } 9 21.786
{'pYis0' } 0.064259 0
{'pYls0' } 0.54867 0.027329
{'pYgr0' } 0.38707 0.97267
{'pYisMINY' } 0.051764 0.015232
{'pYisMAXY' } 0.027329 0.046484
{'p1' } -7 -6.3772
{'p10' } -6 0.27238
{'p25' } -3 5.2138
{'p50' } -1 6.5321
{'p75' } 3 13.799
{'p90' } 5 16.887
{'p99' } 9 21.786
{'fl_cov_cl_mt_pol_a'} 17.007 -22.084
{'fl_cor_cl_mt_pol_a'} 1 -0.81327
{'fl_cov_cl_mt_pol_c'} -22.084 43.356
{'fl_cor_cl_mt_pol_c'} -0.81327 1
{'fracByP1' } 3.2699 -0.010985
{'fracByP10' } 5.9889 -0.013362
{'fracByP25' } 14.165 0.041007
{'fracByP50' } 16.208 0.1893
{'fracByP75' } 12.702 0.59539
{'fracByP90' } 6.6611 0.8307
{'fracByP99' } 1 1 Over some (a,z) states that is 3 by 3, c matrix, generate all stats
% Set Parameters
mt_x_of_s = [1, 2, 3.0;...
3, 1, 1.5;...
4, 3, 2.0];
mt_y_of_s = [2, -10, 9.0;...
5, 1.1,3.0;...
1, 3, -1.5];
mt_z_of_s = [1.1, 2,3.3;...
2.3, 1,1.5;...
4, 2.5,2.0];
mp_cl_mt_xyz_of_s = containers.Map('KeyType','char', 'ValueType','any');
mp_cl_mt_xyz_of_s('cl_mt_x_of_s') = {mt_x_of_s, zeros(1)};
mp_cl_mt_xyz_of_s('cl_mt_y_of_s') = {mt_y_of_s, zeros(1)};
mp_cl_mt_xyz_of_s('cl_mt_z_of_s') = {mt_z_of_s, zeros(1)};
mp_cl_mt_xyz_of_s('ar_st_y_name') = ["cl_mt_x_of_s", "cl_mt_y_of_s", "cl_mt_z_of_s"];
% Mass
rng(123);
mt_f_of_s = rand(size(mt_x_of_s));
mt_f_of_s = mt_f_of_s/sum(mt_f_of_s, 'all');
% Call Function
mp_cl_mt_xyz_of_s_out = ff_simu_stats(mt_f_of_s, mp_cl_mt_xyz_of_s);
xxx tb_outcomes: all stats xxx
OriginalVariableNames cl_mt_x_of_s cl_mt_y_of_s cl_mt_z_of_s
_______________________ ____________ ____________ ____________
{'mean' } 2.0763 1.9323 2.0668
{'sd' } 0.9071 5.2239 0.9042
{'coefofvar' } 0.43688 2.7034 0.43749
{'min' } 1 -10 1
{'max' } 4 9 4
{'pYis0' } 0 0 0
{'pYls0' } 0 0.20441 0
{'pYgr0' } 1 0.79559 1
{'pYisMINY' } 0.28039 0.10917 0.14247
{'pYisMAXY' } 0.044922 0.19422 0.044922
{'p1' } 1 -10 1
{'p10' } 1 -10 1
{'p25' } 1 1.1 1.1
{'p50' } 2 2 2
{'p75' } 3 5 2.5
{'p90' } 3 9 3.3
{'p99' } 4 9 4
{'fl_cov_cl_mt_x_of_s'} 0.82282 1.589 0.78646
{'fl_cor_cl_mt_x_of_s'} 1 0.33534 0.95887
{'fl_cov_cl_mt_y_of_s'} 1.589 27.289 1.8353
{'fl_cor_cl_mt_y_of_s'} 0.33534 1 0.38856
{'fl_cov_cl_mt_z_of_s'} 0.78646 1.8353 0.81758
{'fl_cor_cl_mt_z_of_s'} 0.95887 0.38856 1
{'fracByP1' } 0.13504 -0.56498 0.068934
{'fracByP10' } 0.13504 -0.56498 0.068934
{'fracByP25' } 0.13504 -0.53456 0.14234
{'fracByP50' } 0.42991 -0.39181 0.43856
{'fracByP75' } 0.91346 0.095425 0.60296
{'fracByP90' } 0.91346 1 0.91306
{'fracByP99' } 1 1 1 Same as before, but now inputs are single column, should have identical results:
% Array Inputs
mp_cl_ar_xyz_of_s = containers.Map('KeyType','char', 'ValueType','any');
mp_cl_mt_xyz_of_s('cl_mt_x_of_s') = {mt_x_of_s(:), zeros(1)};
mp_cl_mt_xyz_of_s('cl_mt_y_of_s') = {mt_y_of_s(:), zeros(1)};
mp_cl_mt_xyz_of_s('cl_mt_z_of_s') = {mt_z_of_s(:), zeros(1)};
mp_cl_mt_xyz_of_s('ar_st_y_name') = ["cl_mt_x_of_s", "cl_mt_y_of_s", "cl_mt_z_of_s"];
% Call Function
mp_cl_mt_xyz_of_s_out = ff_simu_stats(mt_f_of_s(:), mp_cl_mt_xyz_of_s);
xxx tb_outcomes: all stats xxx
OriginalVariableNames cl_mt_x_of_s cl_mt_y_of_s cl_mt_z_of_s
_______________________ ____________ ____________ ____________
{'mean' } 2.0763 1.9323 2.0668
{'sd' } 0.9071 5.2239 0.9042
{'coefofvar' } 0.43688 2.7034 0.43749
{'min' } 1 -10 1
{'max' } 4 9 4
{'pYis0' } 0 0 0
{'pYls0' } 0 0.20441 0
{'pYgr0' } 1 0.79559 1
{'pYisMINY' } 0.28039 0.10917 0.14247
{'pYisMAXY' } 0.044922 0.19422 0.044922
{'p1' } 1 -10 1
{'p10' } 1 -10 1
{'p25' } 1 1.1 1.1
{'p50' } 2 2 2
{'p75' } 3 5 2.5
{'p90' } 3 9 3.3
{'p99' } 4 9 4
{'fl_cov_cl_mt_x_of_s'} 0.82282 1.589 0.78646
{'fl_cor_cl_mt_x_of_s'} 1 0.33534 0.95887
{'fl_cov_cl_mt_y_of_s'} 1.589 27.289 1.8353
{'fl_cor_cl_mt_y_of_s'} 0.33534 1 0.38856
{'fl_cov_cl_mt_z_of_s'} 0.78646 1.8353 0.81758
{'fl_cor_cl_mt_z_of_s'} 0.95887 0.38856 1
{'fracByP1' } 0.13504 -0.56498 0.068934
{'fracByP10' } 0.13504 -0.56498 0.068934
{'fracByP25' } 0.13504 -0.53456 0.14234
{'fracByP50' } 0.42991 -0.39181 0.43856
{'fracByP75' } 0.91346 0.095425 0.60296
{'fracByP90' } 0.91346 1 0.91306
{'fracByP99' } 1 1 1 The Same As before, but now control which percentiles and other details to display.
% Array Inputs
mp_cl_ar_xyz_of_s = containers.Map('KeyType','char', 'ValueType','any');
mp_cl_ar_xyz_of_s('cl_ar_x_of_s') = {mt_x_of_s(:), zeros(1)};
mp_cl_ar_xyz_of_s('cl_ar_z_of_s') = {mt_z_of_s(:), zeros(1)};
mp_cl_ar_xyz_of_s('ar_st_y_name') = ["cl_ar_x_of_s", "cl_ar_z_of_s"];
% controls
mp_support = containers.Map('KeyType','char', 'ValueType','any');
mp_support('bl_display_detail') = false;
mp_support('bl_display_final') = true;
mp_support('bl_display_drvm2outcomes') = false;
mp_support('ar_fl_percentiles') = [25 50 75];
mp_support('bl_display_drvstats') = true;
mp_support('bl_display_drvm2covcor') = false;
% Call Function
mp_cl_mt_xyz_of_s_out = ff_simu_stats(mt_f_of_s(:), mp_cl_ar_xyz_of_s, mp_support);
----------------------------------------
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Summary Statistics for: cl_ar_x_of_s
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
----------------------------------------
fl_choice_mean
2.0763
fl_choice_sd
0.9071
fl_choice_coefofvar
0.4369
fl_choice_prob_zero
0
fl_choice_prob_below_zero
0
fl_choice_prob_above_zero
1
fl_choice_prob_max
0.0449
tb_disc_cumu
cl_ar_x_of_sDiscreteVal cl_ar_x_of_sDiscreteValProbMass CDF cumsumFrac
_______________________ _______________________________ ______ __________
1 0.28039 28.039 0.13504
1.5 0.13561 41.6 0.23301
2 0.20441 62.041 0.42991
3 0.33466 95.508 0.91346
4 0.044922 100 1
cl_ar_x_of_sDiscreteVal cl_ar_x_of_sDiscreteValProbMass CDF cumsumFrac
_______________________ _______________________________ ______ __________
1 0.28039 28.039 0.13504
1.5 0.13561 41.6 0.23301
2 0.20441 62.041 0.42991
3 0.33466 95.508 0.91346
4 0.044922 100 1
tb_prob_drv
percentiles cl_ar_x_of_sDiscreteValPercentileValues fracOfSumHeldBelowThisPercentile
___________ _______________________________________ ________________________________
25 1 0.13504
50 2 0.42991
75 3 0.91346
----------------------------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
Summary Statistics for: cl_ar_z_of_s
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
----------------------------------------
fl_choice_mean
2.0668
fl_choice_sd
0.9042
fl_choice_coefofvar
0.4375
fl_choice_prob_zero
0
fl_choice_prob_below_zero
0
fl_choice_prob_above_zero
1
fl_choice_prob_max
0.0449
tb_disc_cumu
cl_ar_z_of_sDiscreteVal cl_ar_z_of_sDiscreteValProbMass CDF cumsumFrac
_______________________ _______________________________ ______ __________
1 0.14247 14.247 0.068934
1.1 0.13792 28.039 0.14234
1.5 0.13561 41.6 0.24076
2 0.20441 62.041 0.43856
2.3 0.056663 67.708 0.50162
2.5 0.083786 76.086 0.60296
3.3 0.19422 95.508 0.91306
4 0.044922 100 1
cl_ar_z_of_sDiscreteVal cl_ar_z_of_sDiscreteValProbMass CDF cumsumFrac
_______________________ _______________________________ ______ __________
1 0.14247 14.247 0.068934
1.1 0.13792 28.039 0.14234
1.5 0.13561 41.6 0.24076
2 0.20441 62.041 0.43856
2.3 0.056663 67.708 0.50162
2.5 0.083786 76.086 0.60296
3.3 0.19422 95.508 0.91306
4 0.044922 100 1
tb_prob_drv
percentiles cl_ar_z_of_sDiscreteValPercentileValues fracOfSumHeldBelowThisPercentile
___________ _______________________________________ ________________________________
25 1.1 0.14234
50 2 0.43856
75 2.5 0.60296
xxx tb_outcomes: all stats xxx
OriginalVariableNames cl_ar_x_of_s cl_ar_z_of_s
_______________________ ____________ ____________
{'mean' } 2.0763 2.0668
{'sd' } 0.9071 0.9042
{'coefofvar' } 0.43688 0.43749
{'min' } 1 1
{'max' } 4 4
{'pYis0' } 0 0
{'pYls0' } 0 0
{'pYgr0' } 1 1
{'pYisMINY' } 0.28039 0.14247
{'pYisMAXY' } 0.044922 0.044922
{'p25' } 1 1.1
{'p50' } 2 2
{'p75' } 3 2.5
{'fl_cov_cl_ar_x_of_s'} 0.82282 0.78646
{'fl_cor_cl_ar_x_of_s'} 1 0.95887
{'fl_cov_cl_ar_z_of_s'} 0.78646 0.81758
{'fl_cor_cl_ar_z_of_s'} 0.95887 1
{'fracByP25' } 0.13504 0.14234
{'fracByP50' } 0.42991 0.43856
{'fracByP75' } 0.91346 0.60296