Contents
What does heteroskedasticity robust mean?
“Robust” standard errors is a technique to obtain unbiased standard errors of OLS coefficients under heteroscedasticity. “Robust” standard errors are usually larger than conventional standard errors. However, this is not always the case.
Is autocorrelation good or bad in regression?
In this context, autocorrelation on the residuals is ‘bad’, because it means you are not modeling the correlation between datapoints well enough. The main reason why people don’t difference the series is because they actually want to model the underlying process as it is.
What are the consequences of heteroskedasticity in autocorrelation?
Consequences of Heteroskedasticity. First, note that we do not need the homoskedasticity asssumption to show the unbiasedness of OLS. Thus, OLS is still unbiased. However, the homoskedasticity assumption is needed to show the e¢ ciency of OLS. Hence, OLS is not BLUE any longer. The variances of the OLS estimators are biased in this case.
What is the weight of a robust regression?
All observations not shown above have a weight of 1. In OLS regression, all cases have a weight of 1. Hence, the more cases in the robust regression that have a weight close to one, the closer the results of the OLS and robust regressions. Next, let’s run the same model, but using the bisquare weighting function.
When to use robust regression in OLS regression?
When comparing the results of a regular OLS regression and a robust regression, if the results are very different, you will most likely want to use the results from the robust regression. Large differences suggest that the model parameters are being highly influenced by outliers. Different functions have advantages and drawbacks.
Is the OLS estimator still unbiased under autocorrelation?
Assuming all other assumptions remain to hold, under the condition of autocorrelation, the OLS estimator is still unbiased. the OLS is not BLUE any more. The usual OLS standard errors and test statitics are no longer valid.