122 Dummy variable trap

This video explains a crucial rule for using dummy variables in econometrics. When including dummy or categorical variables in a regression, you must avoid the dummy variable trap. Failing to do so results in perfect multicollinearity, preventing the regression from being estimated. The dummy variable trap occurs when you include a dummy variable for every category, leading to a perfect linear relationship between the variables. This makes the intercept impossible to estimate as the sum of dummy variables will always equal one. The solution is to include k-1 dummy variables for a categorical variable with k categories, leaving one category as the benchmark or reference group. This principle applies to any number of categories, ensuring your regression can be correctly estimated. Visit AxiomTutoring.com and subscribe to @AxiomTutoringCourses.