I am new in MATLAB and have performed a robust linear regression with the 2 commands:
ds = dataset('XLSFile','C:\...\data.xlsx','ReadObsNames',true);
mdl = LinearModel.fit(ds,'linear','RobustOpts','on');
The standard errors (SE) shown in the property "Coefficients", are these the heteroskedasticity robust standard errors? If not, how can I modify my commands such that I get the robust standard errors?
The output is robust to outliers and are not heteroskedasticity consistent estimates.
If that is what you are interested in, please check out the HAC command in the Econometrics Toolbox:
Sorry but I misunderstood the example. I'm a completely new user of MATLAB and both using it and understanding the documentation pages are difficult here in the beginning. But getting better every day :)
Just to be sure, the degrees of freedom = number of observations - number of estimated parameters. Last term (Number of estimated parameters) does that include the intercept?
That's a statistics question (along with how to compute tstats and pvalue)
I don't know what your application is but you should get hold of some statistics material to convince yourself before applying anything I mentioned.
If you want to get better with MATLAB, check out the Getting Started guide:
http://www.mathworks.com/help/matlab/getting-started-with-matlab.html
Yes, I am interested in estimates and standard errors which are both outlier robust AND heteroskedasticity consistent. From the robust regression, I get the outlier robust estimates and outlier robust standard errors, if I understand correctly, right?
In order to get estimates and standard errors which are also heteroskedasticity consistent, I have checked out http://www.mathworks.com/help/econ/hac.html but it says here that: "...returns robust covariance estimates for ordinary least squares (OLS) coefficient estimates". Then I guess that I cannot use this command as I do not have the ordinary least squares (OLS) coefficient estimates but the robust regression estimates (as I have used robust regression). Isn't that true?
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