Introduction to Econometrics, Global Edition, 4th edition

Published by Pearson (11 July 2025) © 2024

  • James H. Stock Harvard University
  • Mark W. Watson Princeton University
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Products list

Access details

  • Instant access once purchased
  • Offline access via app
Products list

Access details

  • Instant access once purchased
  • Offline access via app

Title overview

Introduction to Econometrics, Global Edition helps students understand how economists use data to answer important questions about the world around them. By combining econometric theory with engaging real-world applications, the text demonstrates how evidence-based analysis can be used to study economic relationships, evaluate policy decisions, and support informed decision-making. The application-driven approach ensures students first understand the economic questions being asked before learning the statistical tools needed to answer them, making econometrics both accessible and relevant.

Designed for introductory econometrics courses, the text develops students' analytical skills through practical examples, empirical research, and hands-on data analysis. Students learn how to assess the reliability of evidence, interpret regression results, and evaluate the strengths and limitations of empirical studies. Coverage extends from foundational regression techniques to modern topics such as big data, machine learning, causal inference, panel data, and time-series forecasting, providing a strong foundation for further study and professional application in economics, business, public policy, and data analytics.

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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