Real econometrics : the right tools to answer important questions

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Bibliographic Information

Real econometrics : the right tools to answer important questions

Michael A. Bailey

Oxford University Press, c2020

2nd ed

  • : pbk

Available at  / 7 libraries

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Note

Bibliography: p. 577-585

Includes index

Description and Table of Contents

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.

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 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

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