Change script to apply on a different dataset.

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Saptorshee Chakraborty 2018-7-30

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https://ww2.mathworks.cn/matlabcentral/answers/412786-change-script-to-apply-on-a-different-dataset

关闭： MATLAB Answer Bot 2021-8-20

Hello, I want to apply a particular econometric methodology for my own research purpose. The script for the methodology has been made public by the author. How can I change it to apply it to my own data.

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Rik 2018-8-1

It is always best to include all relevant information in your question. That way everybody can help you instead of only the people you send your data by email. (just as a note: don't send me the files by email, it's more efficient to upload them here yourself)

Saptorshee Chakraborty 2018-8-1

在 MATLAB Online 中打开

Sorry,

The code:

clear global p T N d pi; IC_needed = 1; cvx_solver mosek load('balancedPanelX1995.mat') X = [lagsaving, cpi, interest, gdp]; y = saving;

p = size(X, 2); T = 15; N = 56; d = 2; % number of IVs

K_max = 5; lamb.grid = 10; lamb.min = .2; lamb.max = 2.0; lamb_const = lamb.min * (lamb.max / lamb.min ).^( ( (1:lamb.grid) - 1) /( lamb.grid -1 ) ); % the constant for lambda. very important!!

lambda = lamb_const* T^(-1/3); numlam = length(lambda);

tol = 0.0001; R = 80; W = 1;

%% scale the dependent variable and its lag term [Dy, DX, Z ] = IV_generate(y, X); std_yi = std(Dy); Dy = bsxfun(@rdivide, Dy, std_yi) ; DX = bsxfun(@rdivide, DX, std(DX, 1)); Z(:,:,3:5) = DX(:,:,2:4);

beta_hat0 = zeros(N, p); for i = 1:N DXi = permute( DX(:,i,:), [1 3 2]); Zi = permute( Z(:,i,:), [1 3 2]); Dyi = Dy(:, i); beta_hat0(i,:) = pinv(DXi' * (Zi * Zi') * DXi) * (DXi' * (Zi * Zi') * Dyi); %initial value end beta_hat00 = beta_hat0;

T = 13; ds = dataset( kron( (1:N)', ones(T,1) ), repmat( (1:T)', [N 1]),... reshape(Dy, [N*T 1]), reshape(DX, [N*T size(DX,3)] ), reshape(Z, [N*T size(Z,3)]) ); ds.Properties.VarNames = {'N' 'T' 'y' 'X', 'Z'};

%% if IC_needed == 1 IC_value = zeros(K_max, numlam); IC = IC_PGMM(ds, ones(N,1)); % K = 1 case IC_value(1, :) = log( IC/(N*T) );

    for ll = 1:numlam
        disp(ll)
        for K =  2:K_max
            lam = lambda(ll)*var(ds.y);
            [b_K, hat.a] = PGMM_est(Dy, DX, Z, beta_hat0, W, K, lam, R, tol);
            [~, hat.b, ~ , group] = report_b( T, b_K, hat.a, K );
            sum(group)
            IC_kk = zeros(1,K);
            for  kk = 1:K
                IC_kk(kk) = IC_PGMM(ds, group(:,kk) );
            end
            IC_value(K,ll) = log( sum(IC_kk)/(N*T) ) 
        end
    end
    pen =  (2/3)*(N*T)^(-.5)* p*(1:K_max);
    IC_final = IC_value + repmat( pen, [numlam, 1])';
    [~, l_hat] = min(IC_final(2,:));
end

%% the information criterion selects % K = 2 and lambda(8)

K = 2; l_hat lam = lambda(l_hat)*var(ds.y);

[b_K, hat.a] = PGMM_est(Dy, DX, Z, beta_hat0, W, K, lam, R, tol); [~, hat.b, ~ , group] = report_b( T, b_K, hat.a, K ); H = hat_IC_PGMM( ds, hat.b, hat.a, K, group); sum(group)

est_post_lasso = zeros(p,6); pi = 0.05; for kk = 1:K this_group = group(:,kk); dat = ds; a_hat = hat.a(kk,:)';

    index = 1:N;
    g_index = index(this_group);
    g_data = dat( ismember(dat.N, g_index), : ); % group-specific data
    ky = g_data.y;
    kX = g_data.X;
    kZ = g_data.Z;
    Nk = sum(this_group);
    XZZX = (kX' * kZ) * pinv(kZ' * kZ) * (kZ' * kX)/(Nk*T)+ridge(pi, N, T);
    XZZy = (kX' * kZ) * pinv(kZ' * kZ) * (kZ' * ky)/(Nk*T);
    post_a = (pinv(XZZX) * XZZy)';
    [vari] = var_post_GMM(T, post_a', ky, kX, kZ, pi); % no ridge used
    se = sqrt(diag(vari));
    est_post_lasso( :, (3 * ( kk-1 ) + 1) :3 * kk ) = [post_a; se'; post_a./se' ]';

end

est_post_lasso = mat2dataset( est_post_lasso, 'VarNames', ... {'g1_coef', 'g1_sd', 'g1_t', 'g2_coef', 'g2_sd', 'g2_t'}); disp(est_post_lasso)

load('country56.mat') country(group(:,1)) country(group(:,2)) sum(group)

%% pooled estimation % when the two groups are put together, we found the IV's is weak % this is the heterogeneity induced weak IV. % we add a positive value to the matrix to stablize the inverse, as in % ridge regression

XZZX = ds.X' * ds.Z * pinv(ds.Z' * ds.Z) * ds.Z' * ds.X/(N*T) + ridge(pi, N, T); XZZy = ds.X' * ds.Z * pinv(ds.Z' * ds.Z) * ds.Z' * ds.y/(N*T) ;

alpha1 = XZZX \ XZZy;

[vari] = var_post_GMM(T, alpha1, ds.y, ds.X, ds.Z,pi); se = sqrt(diag(vari)); [alpha1, se]

group_PGMM = group; save('group_PGMM.mat', 'group_PGMM');

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Change script to apply on a different dataset.

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Change script to apply on a different dataset.

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