Robust OLS standard errors (Newey-West)

Question

I am running a simple OLS regression with HAC adjustment (i.e. Heteroschedasticity and Autocorrelation adjustment) using the following function in hac() in matlab.

My regression is simple in that I am regressing against a vector of ones only:

$Y = \alpha + \epsilon$

In Matlab, the implementation is as follows (this is only to show how it is implemented, but the variables are not the true ones I use in my real example):

Y = rand(500,1);
X = ones(500, 1);  
hac(X, Y, 'intercept', false, 'weights','BT','display','full')

However, for my real variable Y, I get the error that matrix is not positive definite. I am not sure why that would be at all, since I am regressing against one variable only, which is constant 1!

On the other hand, when I run a simple OLS, then there is no error. Something must be happening when the HAC adjustment is done.

Any help would be appreciated. Thank you!

Answer 1

Is it possible that the "meat" of your sandwich is 0 because you include all residuals in HAC with equal weights?

That is, if that is case, in

$\hat V= n(X'X)^{-1}(X'\hat\Omega X)(X'X)^{-1}$

we have that

$$nX'\hat\Omega X = \left(\sum_t\hat u_t\right)^2,$$

which is zero by the FOC of OLS $\sum_t\hat u_t=0$ when, as is the case here, the regression has a constant.