Regression

Read these first

1. 01Heteroskedasticity-Robust Standard Errors

   When some observations are far noisier than others, your regression coefficient is still right but its error bar is a lie. Robust standard errors let every point report its own noisiness instead of forcing one average on all of them.

2. 02Omitted Variable Bias

   Leave something out of a regression and the coefficient you did estimate quietly absorbs its influence. The result is not noisy but systematically wrong, and collecting more data only makes you more confident in the wrong number.

3. 03The Classical Linear Regression Assumptions

   A regression always hands you numbers. The assumptions decide whether those numbers mean anything, some protect the estimate itself, others only protect the error bars, and knowing which is which tells you what to fix.

4. 04Fama-MacBeth Regression

   A two-pass method for testing whether a stock characteristic earns a return premium: run one cross-sectional regression per month, then treat the sequence of monthly slopes as an ordinary time series and t-test its average. It exists because pooling every stock-month into one regression produces standard errors that are far too small.

5. 05Generalized Linear Models

   The recipe for fitting a straight-line-style model to outcomes that aren't shaped like a bell curve, counts, probabilities, yes/no events, by bending the line through a link function before it meets the data.

6. 06Panel Data and Fixed Effects

   A way to strip out everything permanent and unobservable about each stock, trader, or country before you estimate the relationship you actually care about, by comparing each unit only to its own history.

Then the rest

1. 07Regression to the Mean
2. 08Clustered Standard Errors
3. 09Dummy Variables and Interaction Terms
4. 10Elastic Net Regularization
5. 11Information Criteria: AIC and BIC
6. 12Leverage and Influential Observations
7. 13Mixed-Effects Models
8. 14Newey-West HAC Standard Errors
9. 15Poisson and Negative Binomial Regression
10. 16Polynomial and Spline Regression
11. 17Prediction Intervals Versus Confidence Intervals
12. 18Probit Versus Logit Models
13. 19Residual Diagnostics and Diagnostic Plots
14. 20Structural Breaks and the Chow Test
15. 21The Frisch-Waugh-Lovell Theorem
16. 22Debiased Lasso and Post-Selection Inference
17. 23Principal Components Regression
18. 24Random Effects and the Hausman Test
19. 25Survival Analysis and the Cox Proportional Hazards Model
20. 26The Heckman Selection Correction
21. 27Weak Instruments and First-Stage Diagnostics