Part IV. Econometric Pitfalls and Model Diagnostics
In Part III, we learned how to estimate regression models. In Part IV, we ask a more careful question:
Can we trust the regression results?
Applied econometrics is not only about producing coefficients, standard errors, and p-values. It is also about evaluating whether those results are credible. A regression table is the beginning of empirical interpretation, not the end.
This part focuses on five common problems in applied econometrics:
- heteroskedasticity
- autocorrelation
- multicollinearity
- endogeneity
- model misspecification and weak empirical credibility
Each chapter uses a simple applied example, Python code, visual diagnostics, and interpretation.
Chapters in this part
| Chapter | Topic | Main question |
|---|---|---|
| 17 | Heteroskedasticity and Robust Standard Errors | Can we trust the reported standard errors? |
| 18 | Autocorrelation in Time Series Data | Are regression errors related through time? |
| 19 | Multicollinearity | Do explanatory variables contain overlapping information? |
| 20 | Endogeneity and Instrumental Variables | Does correlation measure causation? |
| 21 | Model Diagnostics and Empirical Credibility | Is the model believable? |
Main message
Econometric problems do not automatically destroy an empirical study, but they weaken interpretation if ignored. Good empirical work combines statistical diagnostics with economic reasoning.
By the end of this part, students should be able to move beyond mechanical regression output and evaluate whether empirical results deserve to be trusted.