Introduction to Econometrics, Global Edition, 4th edition

Published by Pearson (July 11, 2025) © 2024

  • James H. Stock Harvard University
  • Mark W. Watson Princeton University
eTextbook in Pearson+

Details

  • A print edition
Products list

Details

  • A print edition
Products list

Details

  • A print edition

Title overview

Learn more about modern Econometrics with this comprehensive introduction to the field.

Introduction to Econometrics, 4th Edition, Global Edition is the ultimate introductory guide that connects modern theory with motivating, engaging applications.

The latest edition maintains the focus on currency with attention to empirical analysis, incorporating real-world questions and data by using results directly relevant to the applications.

Sharing a variety of resources and tools to help your understanding and critical thinking of the topics introduced, this industry-leading text will help you acquire a sophisticated knowledge of this fascinating subject.

Table of contents

PART I: INTRODUCTION AND REVIEW

  1. Economic Questions and Data
  2. Review of Probability
  3. Review of Statistics

PART II: FUNDAMENTALS OF REGRESSION ANALYSIS

  1. Linear Regression with One Regressor
  2. Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
  3. Linear Regression with Multiple Regressors
  4. Hypothesis Tests and Confidence Intervals in Multiple Regression
  5. Nonlinear Regression Functions
  6. Assessing Studies Based on Multiple Regression

PART III: FURTHER TOPICS IN REGRESSION ANALYSIS

  1. Regression with Panel Data
  2. Regression with a Binary Dependent Variable
  3. Instrumental Variables Regression
  4. Experiments and Quasi-Experiments
  5. Prediction with Many Regressors and Big Data

PART IV: REGRESSION ANALYSIS OF ECONOMIC TIME SERIES DATA

  1. Introduction to Time Series Regression and Forecasting
  2. Estimation of Dynamic Causal Effects
  3. Additional Topics in Time Series Regression

PART V: THE ECONOMIC THEORY OF REGRESSION ANALYSIS

  1. The Theory of Linear Regression with One Regressor
  2. The Theory of Multiple Regression

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