Podcast
Questions and Answers
How might the researcher test for the robustness of the regression results using manual and automated methods?
How might the researcher test for the robustness of the regression results using manual and automated methods?
The researcher could test for the robustness of the regression results by conducting manual tests such as a White test to check for heteroskedasticity and a Breusch-Pagan test to check for autocorrelation. The researcher could also use automated methods such as the R commands “lmtest::bptest” and “lmtest::archtest”.
What steps would the researcher need to take to remedy the finding that the static regression results are not reliable?
What steps would the researcher need to take to remedy the finding that the static regression results are not reliable?
If the researcher finds that the static regression results are not reliable, they could take several steps to remedy the situation. These steps could include using a different model, such as a dynamic model, using different data, using a different estimation technique, or using more variables in the model.
What are the advantages of using an alternative regression model to the static model, and how can the coefficient estimates of the alternative model be interpreted?
What are the advantages of using an alternative regression model to the static model, and how can the coefficient estimates of the alternative model be interpreted?
An alternative regression model to the static model could have several advantages. For example, a dynamic regression model could account for the time-varying nature of the data, while a panel data model could account for differences between units. The coefficient estimates of the alternative model can be interpreted in the same way as for the static model, but with the addition of the dynamic and/or panel factors.
