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Real Econometrics

The Right Tools to Answer Important Questions

Second Edition

Michael Bailey

Publication Date - January 2019

ISBN: 9780190857462

656 pages
7-1/2 x 9-1/4 inches

In Stock

Focuses on the tools that contemporary economists actually use to find solutions to global problems


An engaging and practical introduction to econometrics, Real Econometrics: The Right Tools to Answer Important Questions, offers thorough coverage of the most frequently used methods of analysis. Grounded in contemporary understandings of causal inference, the text invites students to extract meaningful information about important economic policy issues from available data. Bailey's emphasis on practical applications, combined with a lively and conversational narrative and a diverse array of examples and case studies, provides students with a solid foundation in the analytical tools they will use throughout their academic and professional careers. The second edition includes new conceptual exercises, revised appendices, and additional code and guidance for R software.

New to this Edition

  • New sections, including one on time-series concepts
  • A revised appendix reviewing the basics of probability and statistics
  • New conceptual exercises to balance the majority mathematical problems
  • Additional code and guidance for R software


  • A lively and engaging narrative presents complicated topics in an informal and fun yet authoritative tone
  • A focus on the techniques economists use most often enables students to explore meaningful analysis much earlier than in other texts
  • A single unified framework, centered on the standard regression model, allows the narrative to build logically from one concept to the next
  • Examples and case studies demonstrate the application of econometric tools in concrete ways including the relationship between height and wages, education and economic growth, country music and suicides, crime and terror alerts, and alcohol use and grades
  • "Remember This" boxes at the end of each section summarize key points to help students keep track of the material that they just learned
  • A guide to computing at the end of every chapter with sample code for Stata and R
  • A wide variety of data-based exercises for each chapter--a total of 58 problems with 43 different data sets--gives students multiple opportunities to analyze real data sets on interesting topics

About the Author(s)

Michael A. Bailey is the Interim Dean of the McCourt School of Public Policy and the Colonel William J. Walsh Professor of American Government at Georgetown University. He teaches and conducts research on American politics and political economy. His work covering trade, Congress, election law and the Supreme Court, methodology, and inter-state policy competition has been published in the American Political Science Review, the American Journal of Political Science, the Journal of Politics, World Politics, the Journal of Law, Economics, and Organization, among others.


"Bailey has written an excellent and unique text that can serve students in a wide range of fields-not just economics-as well as practitioners who need a refresher or an introduction to more advanced methods. The conversational style and emphasis on motivation and intuition over mathematical detail, together with engaging examples and case studies, make this text highly accessible and a pleasure to read." --Martjin van Hasselt, University of North Carolina-Greensboro

"Real Econometrics is extremely current; accessible for undergraduate students with different backgrounds; and it includes useful code in both STATA and R." --David Vera, California State University

"A wonderful book for the price. Your students will be able to understand the materials and will actually use the material in this book." --Phillip Mixon, Troy University

Table of Contents

    List of Figures
    List of Tables
    Useful Commands for Stata
    Useful Commands for R
    Preface for Students: How This Book Can Help You Learn Econometrics
    Preface for Instructors: How to Help Your Students Learn Econometrics

    1 The Quest for Causality
    The Core Model
    Two Challenges: Randomness and Endogeneity
    CASE STUDY: Flu Shots
    CASE STUDY: Country Music and Suicides
    Randomized Experiments as the Gold Standard

    2 Stats in the Wild: Good Data Practices
    2.1 Know Our Data
    2.2 Replication
    CASE STUDY: Violent Crime in the United States
    2.3 Statistical Software

    3 Bivariate OLS: The Foundation of Econometric Analysis
    3.1 Bivariate Regression Model
    3.2 Random Variation in Coefficient Estimates
    3.3 Exogeneity and Unbiasedness
    3.4 Precision of Estimates
    3.5 Probability Limits and Consistency
    3.6 Solvable Problems: Heteroscedasticity and Correlated Errors
    3.7 Goodness of Fit
    CASE STUDY: Height and Wages
    3.8 Outliers

    4 Hypothesis Testing and Interval Estimation: Answering Research Questions
    4.1 Hypothesis Testing
    4.2 t Tests
    4.3 p Values
    4.4 Power
    4.5 Straight Talk about Hypothesis Testing
    4.6 Confidence Intervals

    5 Multivariate OLS: Where the Action Is
    5.1 Using Multivariate OLS to Fight Endogeneity
    5.2 Omitted Variable Bias
    CASE STUDY: Does Education Support Economic Growth?
    5.3 Measurement Error
    5.4 Precision and Goodness of Fit
    CASE STUDY: Institutions and Human Rights
    5.5 Model Specification

    6 Dummy Variables: Smarter Than You Think
    6.1 Using Bivariate OLS to Assess Difference of Means
    CASE STUDY: Sex Differences in Heights
    6.2 Dummy Independent Variables in Multivariate OLS
    6.3 Transforming Categorical Variables to Multiple Dummy Variables
    CASE STUDY: When Do Countries Tax Wealth?
    6.4 Interaction Variables
    CASE STUDY: Energy Efficiency

    7 Specifying Models
    7.1 Quadratic and Polynomial Models
    CASE STUDY: Global Warming
    7.2 Logged Variables
    7.3 Standardized Coefficients
    7.4 Hypothesis Testing about Multiple Coefficients
    CASE STUDY: Comparing Effects of Height Measures

    8 Using Fixed Effects to Fight Endogeneity in Panel Data and Difference-in-Difference Models
    8.1 The Problem with Pooling
    8.2 Fixed Effects Models
    8.3 Working with Fixed Effects Models
    8.4 Two-Way Fixed Effects Model
    CASE STUDY: Trade and Alliances
    8.5 Difference-in-Difference

    9 Instrumental Variables: Using Exogenous Variation to Fight Endogeneity
    9.1 2SLS Example
    9.2 Two-Stage Least Squares (2SLS)
    CASE STUDY: Emergency Care for Newborns
    9.3 Multiple Instruments
    9.4 Quasi and Weak Instruments
    9.5 Precision of 2SLS
    9.6 Simultaneous Equation Models
    CASE STUDY: Supply and Demand Curves for the Chicken Market

    10 Experiments: Dealing with Real-World Challenges
    10.1 Randomization and Balance
    CASE STUDY: Development Aid and Balancing
    10.2 Compliance and Intention-to-Treat Models
    10.3 Using 2SLS to Deal with Non-compliance
    CASE STUDY: Minneapolis Domestic Violence Experiment
    10.4 Attrition
    CASE STUDY: Health Insurance and Attrition
    10.5 Natural Experiments
    CASE STUDY: Crime and Terror Alerts 354

    11 Regression Discontinuity: Looking for Jumps in Data
    11.1 Basic RD Model
    11.2 More Flexible RD Models
    11.3 Windows and Bins
    CASE STUDY: Universal Prekindergarten
    11.4 Limitations and Diagnostics
    CASE STUDY: Alcohol and Grades

    12 Dummy Dependent Variables
    12.1 Linear Probability Model
    12.2 Using Latent Variables to Explain Observed Variables
    12.3 Probit and Logit Models
    12.4 Estimation
    12.5 Interpreting Probit and Logit Coefficients
    CASE STUDY: Econometrics in the Grocery Store
    12.6 Hypothesis Testing about Multiple Coefficients
    CASE STUDY: Civil Wars

    13 Time Series: Dealing with Stickiness over Time
    13.1 Modeling Autocorrelation
    13.2 Detecting Autocorrelation
    13.3 Fixing Autocorrelation
    CASE STUDY: Using an AR(1) Model to Study Global Temperature Changes
    13.4 Dynamic Models
    13.5 Stationarity
    CASE STUDY: Dynamic Model of Global Temperature

    14 Advanced OLS
    14.1 How to Derive the OLS Estimator and Prove Unbiasedness
    14.2 How to Derive the Equation for the Variance of O?1
    14.3 How to Derive the Omitted Variable Bias Conditions
    14.4 Anticipating the Sign of Omitted Variable Bias
    14.5 Omitted Variable Bias with Multiple Variables
    14.6 Omitted Variable Bias due to Measurement Error

    15 Advanced Panel Data
    15.1 Panel Data Models with Serially Correlated Errors
    15.2 Temporal Dependence with a Lagged Dependent Variable
    15.3 Random Effects Models

    16 Conclusion: How to Be an Econometric Realist

    Math and Probability Background
    A. Summation
    B. Expectation
    C. Variance
    D. Covariance
    E. Correlation
    F. Probability Density Functions
    G. Normal Distributions
    H. Other Useful Distributions
    I. Sampling

    Citations and Additional Notes
    Guide to Review Questions
    Photo Credits

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