Introduction to Heteroscedasticity and How to Correct It in Stata Using Functional Forms

In this video, Dr. Ngozi ADEYELE, PhD, Founder Crunch Econometrix, discusses heteroscedasticity and how to correct it in Stata using functional forms.

Heteroscedasticity is a statistical problem that occurs when the variance of the residuals (the errors between the actual values and the values predicted by a model) is not constant. This can lead to biased and inefficient estimates of the model’s parameters.

The video starts with an introduction to heteroscedasticity. The speaker then explains how to detect heteroscedasticity using the Breusch-Pagan test. Once heteroscedasticity is detected, the speaker explains how to correct it using functional forms.

Functional forms are different ways of transforming the variables in a model. By transforming the variables, it may be possible to correct for heteroscedasticity.

The speaker then demonstrates how to use functional forms in Stata to correct for heteroscedasticity. He uses the log-level, log-log, and level-log functional forms.

The speaker concludes the video by summarizing the key points. He emphasizes that heteroscedasticity is a common problem that can lead to biased and inefficient estimates of the model’s parameters. He also emphasizes that functional forms are a simple and effective way to correct for heteroscedasticity.

Here are some additional points from the video:

• The speaker recommends using the estat het command in Stata to test for heteroscedasticity.
• The speaker recommends using the log-level, log-log, and level-log functional forms to correct for heteroscedasticity.
• The speaker notes that functional forms are not always the best way to correct for heteroscedasticity. In some cases, it may be better to use generalized least squares (GLS) or weighted least squares (WLS).

I hope this article is helpful!

Credit: To Dr. Ngozi ADEYELE, PhD. Founder Crunch Econometrix