Concordia UniversityDepartment of EconomicsECON 681: Econometric Theory IIAssignment 3: Due on 05/04/2023Winter 2023–20/03/2023Question 1: (Checking the robustness of quantile...

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Answered 4 days AfterMar 31, 2023

Answer To: Concordia UniversityDepartment of EconomicsECON 681: Econometric Theory IIAssignment 3: Due on...

Aditi answered on Apr 05 2023
40 Votes
Answer
1.
Step1:
n = 1000; % sample size
df = 5; % degrees of freedom
X = randn(n,1); % X is normally distributed
Y = trnd(df,n,1); % Y
is t-distributed
Step2:
% OLS estimate
X_OLS = [ones(n,1), X];
beta_OLS = X_OLS\Y;
beta2_OLS = beta_OLS(2);
% LAD estimate
fun = @(beta2) sum(abs(Y - [ones(n,1), X]*[1; beta2]));
beta2_LAD = fminsearch(fun, beta2_OLS, optimset('MaxFunEvals', 10000, 'MaxIter', 2000));
Step3:
MC = 10000; % number of MC simulations
beta2_true = 2; % true value of beta2
% preallocate memory for bias and RMSE
bias_OLS = zeros(MC,1);
rmse_OLS = zeros(MC,1);
bias_LAD = zeros(MC,1);
rmse_LAD = zeros(MC,1);
for i = 1:MC
% generate data
X = randn(n,1);
Y = trnd(df,n,1);

% OLS estimate
X_OLS = [ones(n,1), X];
beta_OLS = X_OLS\Y;
beta2_OLS = beta_OLS(2);

% LAD estimate
fun = @(beta2) sum(abs(Y - [ones(n,1), X]*[1; beta2]));
beta2_LAD = fminsearch(fun, beta2_OLS, optimset('MaxFunEvals', 10000, 'MaxIter', 2000));

% compute bias and RMSE
bias_OLS(i) = beta2_OLS - beta2_true;
rmse_OLS(i) = (beta2_OLS - beta2_true)^2;
bias_LAD(i) = beta2_LAD - beta2_true;
rmse_LAD(i) = (beta2_LAD - beta2_true)^2;
end
% calculate simulated bias and RMSE
sim_bias_OLS = mean(bias_OLS);
sim_rmse_OLS = sqrt(mean(rmse_OLS));
sim_bias_LAD = mean(bias_LAD);
sim_rmse_LAD = sqrt(mean(rmse_LAD));
Step4:
df_vec =...
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