Stata Do File for Testing Regression Assumptions

​When conducting regression analysis in Stata, it’s crucial to check for the key assumptions that underlie the regression model. These assumptions include linearity, independence, homoscedasticity, normality, and the absence of multicollinearity. Violation of these assumptions can lead to biased or inefficient estimates.

Here’s a structured example of a Stata do-file that demonstrates how to check these assumptions:

Load dataset

use "your_dataset.dta", clear

Example: Simple linear regression model

Replace 'dependent_var', 'independent_var1', 'independent_var2', etc., with your variables

regress dependent_var independent_var1 independent_var2

Check for Linearity

Using scatter plots or added variable plots

avplot independent_var1

avplot independent_var2

Check for Independence (No Autocorrelation)

Especially important in time series data

estat bgodfrey

Check for Homoscedasticity (Constant Variance)

Using Breusch-Pagan / Cook-Weisberg test

estat hettest

Check for Normality of Residuals

Using Shapiro-Wilk test or skewness and kurtosis

predict residuals, residuals

swilk residuals

Check for Multicollinearity

Using Variance Inflation Factor (VIF)

vif

Clean up by removing the residuals variable

drop residuals

Save the modified dataset (if necessary)

save "regression_checked_dataset.dta", replace

In this script:

Replace dependent_var, independent_var1, independent_var2, etc., with the names of the variables in your regression model.

The script includes commands to check for the various assumptions. Adjust these as needed based on your model.

After checking the assumptions, the residuals variable created for the normality test is removed for data cleanliness.

Remember:

Interpreting these tests requires some statistical knowledge. For example, a high VIF indicates potential multicollinearity, and the Breusch-Pagan test indicates potential heteroskedasticity if the test statistic is significant.

Depending on the nature of your data and your specific model, there may be additional or alternative tests that are more appropriate.