Simple Tips About How To Deal With Heteroscedasticity

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How to deal with heteroscedasticity. Also, gujarati and porter suggested this option in their book of econometrics. The idea is to give small weights to observations associated with higher variances to shrink their squared. Lalita, use the robust cluster command in stata.

This package is quite interesting, and offers quite a lot of functions for. For all methods (manual recode, automatic recode, and writing a syntax), i end up with missing data. When there is evidence of heteroscedasticity, econometricians do one of the two things:

Use ols estimator to estimate the parameters of the model. Using gls (than ols) is the solution for your heteroscedasticity. This package is quite interesting, and offers quite a lot of functions.

Sometimes you may want an algorithmic approach to check for heteroscedasticity so that you can quantify its presence automatically and make amends. Heteroscedasticity is also caused due to omission of variables from the model. The same syntax has worked for every other variable i needed to convert but this one.