Introductory Econometrics: Description, Prediction, and Causality
Third Edition
This textbook was prepared for an Introductory Econometrics course at the University of Missouri. It focuses on statistical description, prediction, and “causality,” including both structural model parameters and treatment effects. Description and prediction (forecasting) with time series are also covered. You will learn to think probabilistically, understand prediction and causality, judge whether various assumptions hold true in real-world examples, and apply econometric methods in R. Each chapter features learning objectives, numerous examples, discussion questions, empirical exercises in R and Stata, and accompanying YouTube videos linked from the author's website. Students have responded very positively with anonymous evaluation comments like:
- "You can tell Dr. Dave has a real passion for teaching. He's excellent at explaining complex topics through videos and his textbook"
- "There were so many examples that were helpful when covering the material"
- "Dr. Kaplan's expertise of the subject matter shined through...still remained digestible for everyone; it was truly...superb"
- "Just thought it was a great textbook...explained concepts really well"
The author is an Associate Professor whose research also focuses on econometrics, providing new methodology to help us learn more from our data.