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There is a 2D matrix, the rows and columns are state variables (savings levels and shocks) for storage and graphing purposes, convert the 2D matrix where each row is a savings level and each column is a shock level to a 2D table where the first column records savings state, second column the level of shocks, and the third column stores the optimal policy or value at that particular combination of savings level and shock level.
First, generate a random 2D matrix:
% Create a 3D Array
it_z_n = 3;
it_a_n = 5;
% shock savings and shock array
ar_a = linspace(0.1, 50, it_a_n);
ar_z = linspace(-3, 3, it_z_n);
% function of a and z
mt_f_a_z = ar_a' + exp(ar_z);
% Display
disp(mt_f_a_z);
0.1498 1.1000 20.1855
12.6248 13.5750 32.6605
25.0998 26.0500 45.1355
37.5748 38.5250 57.6105
50.0498 51.0000 70.0855Second, from linear index to row and column index:
% Row and Column index for each matrix value
% Only keep non-NAN values
ar_id_isnan = isnan(mt_f_a_z);
[ar_a_idx, ar_z_idx] = ind2sub(size(mt_f_a_z), find(~ar_id_isnan));
% Display
disp([ar_a_idx, ar_a(ar_a_idx)', ar_z_idx, ar_z(ar_z_idx)']);
1.0000 0.1000 1.0000 -3.0000
2.0000 12.5750 1.0000 -3.0000
3.0000 25.0500 1.0000 -3.0000
4.0000 37.5250 1.0000 -3.0000
5.0000 50.0000 1.0000 -3.0000
1.0000 0.1000 2.0000 0
2.0000 12.5750 2.0000 0
3.0000 25.0500 2.0000 0
4.0000 37.5250 2.0000 0
5.0000 50.0000 2.0000 0
1.0000 0.1000 3.0000 3.0000
2.0000 12.5750 3.0000 3.0000
3.0000 25.0500 3.0000 3.0000
4.0000 37.5250 3.0000 3.0000
5.0000 50.0000 3.0000 3.0000Third, generate a 2d matrix in "table" format:
% Index and values
mt_policy_long = [ar_a_idx, ar_a(ar_a_idx)', ar_z_idx, ar_z(ar_z_idx)', mt_f_a_z(~ar_id_isnan)];
% Sort by a and z
mt_policy_long = sortrows(mt_policy_long, [1,3]);Fourth, generate a Table with Column names:
% Create Table
tb_policy_long = array2table(mt_policy_long);
cl_col_names_a = {'a_idx', 'a_val', 'z_idx', 'z_val', 'pol_at_a_z'};
tb_policy_long.Properties.VariableNames = cl_col_names_a;
disp(tb_policy_long);
a_idx a_val z_idx z_val pol_at_a_z
_____ ______ _____ _____ __________
1 0.1 1 -3 0.14979
1 0.1 2 0 1.1
1 0.1 3 3 20.186
2 12.575 1 -3 12.625
2 12.575 2 0 13.575
2 12.575 3 3 32.661
3 25.05 1 -3 25.1
3 25.05 2 0 26.05
3 25.05 3 3 45.136
4 37.525 1 -3 37.575
4 37.525 2 0 38.525
4 37.525 3 3 57.611
5 50 1 -3 50.05
5 50 2 0 51
5 50 3 3 70.086 Continue with the previous exercise, but now we have more than 2 state variables.
Create a multidimensional Array with Many NaN Values. For example, we could have a dynamic lifecycle model with three endogenous varaibles, years of education accumulated, years of experiencesin blue and white collar jobs. By age 22, after starting to work at age 16, there are different possible combinations of G (schooling), X1 (white-collar), and X2 (blue-collar) jobs. These are exclusive choices in each year, so at age 16, assume that G = 0, X1 = 0 and X2 = 0. At age 16, they can choose to stay at home, school, or X1, or X2, exclusively. G, X1, X2 accumulate over time.
For each age, we can create multi-dimensional arrays with equal dimension for G, X1 and X2, to record consumption, value, etc at each element of the possible state-space. However, that matrix could have a lot of empty values.
In the example below, also has a X3 (military category).
% random number
rng(123);
% Max age means number of
MAX_YRS_POST16 = 3;
% store all
cl_EV = cell(MAX_YRS_POST16,1);
% Loop 1, solve BACKWARD
for it_yrs_post16=MAX_YRS_POST16:-1:1
% Store some results, the matrix below includes all possible
% state-space elements
mn_ev_at_gx123 = NaN(it_yrs_post16, it_yrs_post16, it_yrs_post16, it_yrs_post16);
% Loops 2, possibles Years attained so far as well as experiences
for G=0:1:(it_yrs_post16-1)
for X1=0:1:(it_yrs_post16-1-G)
for X2=0:1:(it_yrs_post16-1-G-X1)
for X3=0:1:(it_yrs_post16-1-G-X1-X2)
% Double checkAre these combinations feasible?
if (G+X1+X2+X3 <= it_yrs_post16)
% just plug in a random number
mn_ev_at_gx123(G+1, X1+1, X2+1, X3+1) = rand();
end
end
end
end
end
% store matrixes
cl_EV{it_yrs_post16} = mn_ev_at_gx123;
end
% Display Results
celldisp(cl_EV);
cl_EV{1} =
0.6344
cl_EV{2} =
(:,:,1,1) =
0.7380 0.5316
0.5318 NaN
(:,:,2,1) =
0.1755 NaN
NaN NaN
(:,:,1,2) =
0.1825 NaN
NaN NaN
(:,:,2,2) =
NaN NaN
NaN NaN
cl_EV{3} =
(:,:,1,1) =
0.6965 0.9808 0.3921
0.3432 0.0597 NaN
0.3980 NaN NaN
(:,:,2,1) =
0.5513 0.4809 NaN
0.4386 NaN NaN
NaN NaN NaN
(:,:,3,1) =
0.4231 NaN NaN
NaN NaN NaN
NaN NaN NaN
(:,:,1,2) =
0.2861 0.6848 NaN
0.7290 NaN NaN
NaN NaN NaN
(:,:,2,2) =
0.7195 NaN NaN
NaN NaN NaN
NaN NaN NaN
(:,:,3,2) =
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
(:,:,1,3) =
0.2269 NaN NaN
NaN NaN NaN
NaN NaN NaN
(:,:,2,3) =
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
(:,:,3,3) =
NaN NaN NaN
NaN NaN NaN
NaN NaN NaNWe can generate a 2-dimensional matrix, what we can consider as a Table, with the information stored in the structures earlier. In this example, we can drop the NaN values. This matrix will be much larger in size due to explicitly storing X1, X2, X3 and G values then the ND array when most values are not NaN. But this output matrix can be much more easily interpretable and readable. When there are many many NaNs in the ND array, this matrix could be much smaller in size.
First, convert each element of the cell array above to a 2D matrix (with the same number of columns), then stack resulting matrixes together to form one big table.
% Create a 2D Array
for it_yrs_post16=MAX_YRS_POST16:-1:1
% Get matrix at cell element
mn_ev_at_gx123 = cl_EV{it_yrs_post16};
% flaten multi-dimensional matrix
ar_ev_at_gx123_flat = mn_ev_at_gx123(:);
% find nan values
ar_id_isnan = isnan(ar_ev_at_gx123_flat);
% obtain dimension-specific index for nan positions
[id_G, id_X1, id_X2, id_X3] = ind2sub(size(mn_ev_at_gx123), find(~ar_id_isnan));
% generate 2-dimensional matrix (table)
mt_ev_at_gx123 = [it_yrs_post16 + zeros(size(id_G)), ...
(id_G-1), (id_X1-1), (id_X2-1), (id_X3-1), ...
ar_ev_at_gx123_flat(~ar_id_isnan)];
% stack results
if (it_yrs_post16 == MAX_YRS_POST16)
mt_ev_at_gx123_all = mt_ev_at_gx123;
else
mt_ev_at_gx123_all = [mt_ev_at_gx123_all; mt_ev_at_gx123];
end
end
% Sort
mt_ev_at_gx123_all = sortrows(mt_ev_at_gx123_all, [1,2,3,4]);
% Create Table
tb_ev_at_gx123_all = array2table(mt_ev_at_gx123_all);
cl_col_names_a = {'YRS_POST16', 'G', 'X1', 'X2', 'X3', 'EV'};
tb_ev_at_gx123_all.Properties.VariableNames = cl_col_names_a;
disp(tb_ev_at_gx123_all);
YRS_POST16 G X1 X2 X3 EV
__________ _ __ __ __ ________
1 0 0 0 0 0.6344
2 0 0 0 0 0.738
2 0 0 0 1 0.18249
2 0 0 1 0 0.17545
2 0 1 0 0 0.53155
2 1 0 0 0 0.53183
3 0 0 0 0 0.69647
3 0 0 0 1 0.28614
3 0 0 0 2 0.22685
3 0 0 1 0 0.55131
3 0 0 1 1 0.71947
3 0 0 2 0 0.42311
3 0 1 0 0 0.98076
3 0 1 0 1 0.68483
3 0 1 1 0 0.48093
3 0 2 0 0 0.39212
3 1 0 0 0 0.34318
3 1 0 0 1 0.72905
3 1 0 1 0 0.43857
3 1 1 0 0 0.059678
3 2 0 0 0 0.39804There are three parameters, quadratic of preference, height preference, and reference points preference. Mesh three vectors together with ndgrid. Then generate a flat table with the index of the parameters as well as the values of the parameters.
% Generate Arrays
[it_quadc, it_linh, it_refh] = deal(2, 2, 2);
ar_fl_quadc = linspace(-0.01, -0.001, it_quadc);
ar_fl_linh = linspace(0.01, 0.05, it_linh);
ar_fl_refh = linspace(-0.01, -0.05, it_refh);
% ndgrid mesh together
[mn_fl_quadc, ~] = ndgrid(ar_fl_quadc, ar_fl_linh, ar_fl_refh);
% combine
[ar_it_quadc_idx, ar_it_linh_idx, ar_it_refh_idx] = ind2sub(size(mn_fl_quadc), find(mn_fl_quadc));
% Index and values
mt_paramsmesh_long = [ar_it_quadc_idx, ar_fl_quadc(ar_it_quadc_idx)', ...
ar_it_linh_idx, ar_fl_linh(ar_it_linh_idx)', ...
ar_it_refh_idx, ar_fl_refh(ar_it_refh_idx)'];
% Sort by a and z
mt_paramsmesh_long = sortrows(mt_paramsmesh_long, [1,3, 5]);Generate a table with Column names:
% Create Table
tb_paramsmesh_long = array2table(mt_paramsmesh_long);
cl_col_names_a = {'quadc_idx', 'quadc_val', 'linh_idx', 'linh_val', 'refh_idx', 'rehfh_val'};
tb_paramsmesh_long.Properties.VariableNames = cl_col_names_a;
disp(tb_paramsmesh_long);
quadc_idx quadc_val linh_idx linh_val refh_idx rehfh_val
_________ _________ ________ ________ ________ _________
1 -0.01 1 0.01 1 -0.01
1 -0.01 1 0.01 2 -0.05
1 -0.01 2 0.05 1 -0.01
1 -0.01 2 0.05 2 -0.05
2 -0.001 1 0.01 1 -0.01
2 -0.001 1 0.01 2 -0.05
2 -0.001 2 0.05 1 -0.01
2 -0.001 2 0.05 2 -0.05