Real Econometrics
The Right Tools to Answer Important Questions
2nd Edition
Will be published approx. in May 2019
Book
Paperback/Softback
656 pages
978-0-19-085746-2 (ISBN)
Description
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.
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.
Reviews / Votes
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 * This text covers the basics of the modern (reduced-form & frequentist) toolbox in the context of real-world applications. The second half of the text is a very good introduction to fixing problems identified with the OLS model identified in the first half." * James Bland, The University of Toledo * 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 * Real Econometrics is a user-friendly and application-oriented textbook for basic econometrics; quite comparable to Gujarati's Essential Econometrics." * Kwang Soo Cheong, Johns Hopkins University *More details
Edition
2nd Revised edition
Language
English
Place of publication
New York
United States
Target group
College/higher education
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 23 mm
Weight
998 gr
ISBN-13
978-0-19-085746-2 (9780190857462)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
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.
Journal of Political Science, the Journal of Politics, World Politics, the Journal of Law, Economics, and Organization, among others.
Content
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 Acknowledgments 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 I The OLS FRAMEWORK
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 II THE CONTEMPORARY ECONOMETRIC TOOLKIT
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 III LIMITED DEPENDENT VARIABLES
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 IV ADVANCED MATERIAL
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 APPENDICES
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
Bibliography
Photo Credits
Glossary
Index
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 I The OLS FRAMEWORK
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 II THE CONTEMPORARY ECONOMETRIC TOOLKIT
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 III LIMITED DEPENDENT VARIABLES
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 IV ADVANCED MATERIAL
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 APPENDICES
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
Bibliography
Photo Credits
Glossary
Index