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Front Matter

Preface
About the Author
How to Cite
Acknowledgments

Introduction

Motivation: Uses of Regression
From Theory to Evidence

The Conditional Expectation Function (CEF)

From Averages to a Model

Regression Mechanics I: Simple Regression

The Simple Linear Regression Model
Logarithmic Transformations
Unbiasedness and Variance of OLS
Hypothesis Testing in Simple Regression
Power and Optimal Sample Size for Regression Coefficient

Regression Mechanics II: Multiple Regression

Interpretation, Properties, and Specification
Categorical Variables and Interactions

Regression Mechanics III: Classification Models

Categorical Dependent Variables: Logit and Probit

Regression Mechanics IV: Error Structure & Robust Inference

Heteroscedasticity

Causal Inference

Selection Bias and Randomized Controlled Experiments
Differences-in-Differences
Causal Identification with DAGs
Instrumental Variables: Identification and LATE
Instrumental Variables: Two-Stage Least Squares and Weak Instruments
The Synthetic Control Method

Back Matter

References

Index

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