# Probit regressions: Newey-West adjustment and pseudo R-squared?

7 views (last 30 days)
denden on 24 Feb 2014
Answered: andres on 17 Jun 2020
I am performing probit regressions using the glmfit code in conjunction with the probit link.
In linear regressions it is common to adjust the standard errors following the procedure suggested by Newey and West. I have seen several papers on probit regressions that use the Newey-West adjustment and I would like to adjust my model as well.
Do you know how I could do this adjustment in Matlab for my probit model?
- Pseudo R-squared
One of the measures of goodness of fit is a pseudo R-squared as proposed by Estrella (1998).
Estrella R-squared = 1 - [ log L(u) / log L(c) ] ^ [ - (2 / n) * log L(c) ]
where L(u) is the maximized unconstrained log-likelihood value and L(c) the maximized constrained one (the null hypothesis says all coefficients except for the constant are equal to zero).
Theory suggests that the Estrella R-squared should not be negative in in-sample regressions (degrees of freedom etc.). However, I receive negative R-squareds which is why I assume that there might be something wrong with my distribution link and the parameters of this distribution.
I am using the following formula in Matlab (assumption: standard normal distribution):
LLU0 = sum(log(pdf('Normal',yhatU0,0,1))); %log likelihood (unrestricted)
LLR0 = sum(log(pdf('Normal',yhatR0,0,1))); %log likelihood (restricted)
Estrella0 = 1-(LLU0/LLR0)^(-(2/obsx)*LLR0);
Do you have any suggestions how I could fix this problem? How would the code look like in Matlab?
Thank you!

andres on 17 Jun 2020
Hi. Did you figure this out finally? I'm dealing with the same situation now.

### Community Treasure Hunt

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

Start Hunting!

Translated by