Optimizing code for HAC Weight matrix generation - How to be faster?

5 views (last 30 days)
Hi everyone,
I am re-creating Newey-West procedure for a heteroskedasticity and autocorrelation consistent standard errors (HAC) from scratch.
In order to compute the sandwich matrix V(b), I need to generate the weight matrix W
Since I have to run that test thousands of times, I need to optimize the code. Right now, I have this:
%% Weight matrix generation for HAC
T = length(Dependant_Variable);
k = length(Coeff);
df = T-k;
Optimal_Lags = floor(4*(T/100)^(2/9));
Residual_correlation = Residual*Residual';
Weight_correlation = zeros(size(Residual_correlation));
for i = 1:size(Weight_correlation,1)
for j = 1:size(Weight_correlation,2)
if abs(i-j)>Optimal_Lags
Weight_correlation(i,j)=0;
else
Weight_correlation(i,j)=(T/df)*Residual_correlation(i,j)*(1-abs(j-i)/(Optimal_Lags+1));
end
end
end
Question:
How can I get the same result without using loops?
Thanks and best regards,

Accepted Answer

Alan Stevens
Alan Stevens on 18 Mar 2024
Try replacing the loops with something like this:
i = (1:size(Weight_correlation,1))';
j = 1:size(Weight_correlation,2);
c = ones(1,size(Weight_correlation,2));
r = ones(size(Weight_correlation,1),1);
lags = abs(i*c-r*j);
ind1 = lags>Optimal_Lags;
ind2 = lags<=Optimal_Lags;
Weight_correlation(ind1)=0;
Weight_correlation(ind2)=(T/df)*Residual_correlation(ind2).*(1-lags(ind2)/(Optimal_Lags+1));
Check carefully, as I can't really test it properly, not having your data.
  2 Comments
Vic
Vic on 20 Mar 2024
You are right. This does not work.
close all; clearvars;clc;
Variables = rand(10,10);
Companies = rand(10,1);
Coeff = (Variables'*Variables)\Variables'*Companies;
Expected = Variables*Coeff;
Residual = Companies-Expected;
T = length(Companies);
k = length(Coeff);
df = T-k;
Optimal_Lags = floor(4*(T/100)^(2/9));
Residual_correlation = Residual*Residual';
Weight_correlation = zeros(size(Residual_correlation));
for i = 1:size(Weight_correlation,1)
for j = 1:size(Weight_correlation,2)
if abs(i-j)>Optimal_Lags
Weight_correlation(i,j)=0;
else
Weight_correlation(i,j)=Residual_correlation(i,j)*(1-abs(j-i)/(Optimal_Lags+1));
end
end
end
i = (1:size(Weight_correlation,1))';
j = 1:size(Weight_correlation,2);
c = ones(1,size(Weight_correlation,2));
r = ones(size(Weight_correlation,1),1);
lags = abs(i*c-r*j);
ind1 = lags>Optimal_Lags;
ind2 = lags<=Optimal_Lags;
Weight_corr(ind1)=0;
Weight_corr(ind2)=(T/df)*Residual_correlation(ind2).*(1-lags(ind2)/(Optimal_Lags+1));
Check = Weight_correlation - Weight_corr;
Arrays have incompatible sizes for this operation.
Weight_correlation is (10x10) and Weight_corr is (1x100). I reshaped the outcome of this code but here is the result:
Any idea?
Alan Stevens
Alan Stevens on 20 Mar 2024
Edited: Alan Stevens on 20 Mar 2024
Try this:
Variables = rand(10,10);
Companies = rand(10,1);
Coeff = (Variables'*Variables)\Variables'*Companies;
Expected = Variables*Coeff;
Residual = Companies-Expected;
T = length(Companies);
k = length(Coeff);
df = T-k;
Optimal_Lags = floor(4*(T/100)^(2/9));
Residual_correlation = Residual*Residual';
Weight_correlation = zeros(size(Residual_correlation));
for i = 1:size(Weight_correlation,1)
for j = 1:size(Weight_correlation,2)
if abs(i-j)>Optimal_Lags
Weight_correlation(i,j)=0;
else
Weight_correlation(i,j)=Residual_correlation(i,j)*(1-abs(j-i)/(Optimal_Lags+1));
end
end
end
i = (1:size(Weight_correlation,1))';
j = 1:size(Weight_correlation,2);
c = ones(1,size(Weight_correlation,2));
r = ones(size(Weight_correlation,1),1);
lags = abs(i*c-r*j);
ind2 = lags<=Optimal_Lags;
Weight_corr = ind2.*Residual_correlation.*(1-lags/(Optimal_Lags+1));
Check = Weight_correlation - Weight_corr;
disp(Check)
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
(I note that you removed the T/df term which gave the inf values!).

Sign in to comment.

More Answers (0)

Products


Release

R2023b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!