When the error variance differs across the two groups, we can obtain a heteroskedasticity-robust Chow test by including a dummy variable distinguishing the two groups along with interactions between that dummy variable and all other explanatory variables.

Question

When the error variance differs across the two groups, we can obtain a heteroskedasticity-robust Chow test by including a dummy variable distinguishing the two groups along with interactions between that dummy variable and all other explanatory variables.​

Quizplus · Accepted Answer

This statement is true. The Chow test is typically used to test for structural breaks or differences in regression relationships between two or more mutually exclusive subsamples. However, when the error variance is not constant across these subsamples (i.e., the error term is heteroskedastic), the standard Chow test may not be appropriate, and a modified version that accounts for this heteroskedasticity should be used. One way to do this is by including a dummy variable that distinguishes the two subsamples and interaction terms between this dummy and all other explanatory variables in the regression. This approach produces a Chow test that is robust to heteroskedasticity in the error term.

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