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Full 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 andconversational 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 Rsoftware.
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 CausalityThe Core Model Two Challenges: Randomness and Endogeneity CASE STUDY: Flu ShotsCASE STUDY: Country Music and SuicidesRandomized Experiments as the Gold Standard 2 Stats in the Wild: Good Data Practices2.1 Know Our Data2.2 ReplicationCASE STUDY: Violent Crime in the United States2.3 Statistical SoftwareI The OLS FRAMEWORK3 Bivariate OLS: The Foundation of Econometric Analysis3.1 Bivariate Regression Model3.2 Random Variation in Coefficient Estimates3.3 Exogeneity and Unbiasedness3.4 Precision of Estimates3.5 Probability Limits and Consistency3.6 Solvable Problems: Heteroscedasticity and Correlated Errors3.7 Goodness of FitCASE STUDY: Height and Wages3.8 Outliers 4 Hypothesis Testing and Interval Estimation: Answering Research Questions4.1 Hypothesis Testing4.2 t Tests4.3 p Values4.4 Power4.5 Straight Talk about Hypothesis Testing4.6 Confidence Intervals5 Multivariate OLS: Where the Action Is5.1 Using Multivariate OLS to Fight Endogeneity5.2 Omitted Variable BiasCASE STUDY: Does Education Support Economic Growth?5.3 Measurement Error5.4 Precision and Goodness of FitCASE STUDY: Institutions and Human Rights5.5 Model Specification6 Dummy Variables: Smarter Than You Think6.1 Using Bivariate OLS to Assess Difference of MeansCASE STUDY: Sex Differences in Heights6.2 Dummy Independent Variables in Multivariate OLS6.3 Transforming Categorical Variables to Multiple Dummy VariablesCASE STUDY: When Do Countries Tax Wealth?6.4 Interaction VariablesCASE STUDY: Energy Efficiency7 Specifying Models7.1 Quadratic and Polynomial ModelsCASE STUDY: Global Warming7.2 Logged Variables7.3 Standardized Coefficients7.4 Hypothesis Testing about Multiple CoefficientsCASE STUDY: Comparing Effects of Height MeasuresII THE CONTEMPORARY ECONOMETRIC TOOLKIT8 Using Fixed Effects to Fight Endogeneity in Panel Data and Difference-in-Difference Models8.1 The Problem with Pooling8.2 Fixed Effects Models8.3 Working with Fixed Effects Models8.4 Two-Way Fixed Effects ModelCASE STUDY: Trade and Alliances8.5 Difference-in-Difference9 Instrumental Variables: Using Exogenous Variation to Fight Endogeneity9.1 2SLS Example9.2 Two-Stage Least Squares (2SLS) CASE STUDY: Emergency Care for Newborns9.3 Multiple Instruments9.4 Quasi and Weak Instruments9.5 Precision of 2SLS9.6 Simultaneous Equation ModelsCASE STUDY: Supply and Demand Curves for the Chicken Market10 Experiments: Dealing with Real-World Challenges10.1 Randomization and BalanceCASE STUDY: Development Aid and Balancing10.2 Compliance and Intention-to-Treat Models10.3 Using 2SLS to Deal with Non-complianceCASE STUDY: Minneapolis Domestic Violence Experiment10.4 AttritionCASE STUDY: Health Insurance and Attrition10.5 Natural ExperimentsCASE STUDY: Crime and Terror Alerts 35411 Regression Discontinuity: Looking for Jumps in Data 11.1 Basic RD Model11.2 More Flexible RD Models11.3 Windows and BinsCASE STUDY: Universal Prekindergarten11.4 Limitations and DiagnosticsCASE STUDY: Alcohol and GradesIII LIMITED DEPENDENT VARIABLES12 Dummy Dependent Variables12.1 Linear Probability Model12.2 Using Latent Variables to Explain Observed Variables12.3 Probit and Logit Models 12.4 Estimation12.5 Interpreting Probit and Logit CoefficientsCASE STUDY: Econometrics in the Grocery Store12.6 Hypothesis Testing about Multiple Coefficients CASE STUDY: Civil WarsIV ADVANCED MATERIAL13 Time Series: Dealing with Stickiness over Time13.1 Modeling Autocorrelation13.2 Detecting Autocorrelation13.3 Fixing AutocorrelationCASE STUDY: Using an AR(1) Model to Study Global Temperature Changes13.4 Dynamic Models13.5 StationarityCASE STUDY: Dynamic Model of Global Temperature 14 Advanced OLS14.1 How to Derive the OLS Estimator and Prove Unbiasedness14.2 How to Derive the Equation for the Variance of O?1 14.3 How to Derive the Omitted Variable Bias Conditions14.4 Anticipating the Sign of Omitted Variable Bias14.5 Omitted Variable Bias with Multiple Variables14.6 Omitted Variable Bias due to Measurement Error15 Advanced Panel Data15.1 Panel Data Models with Serially Correlated Errors15.2 Temporal Dependence with a Lagged Dependent Variable15.3 Random Effects Models 16 Conclusion: How to Be an Econometric RealistAPPENDICESMath and Probability BackgroundA. SummationB. ExpectationC. VarianceD. CovarianceE. CorrelationF. Probability Density FunctionsG. Normal DistributionsH. Other Useful DistributionsI. SamplingCitations and Additional NotesGuide to Review QuestionsBibliographyPhoto CreditsGlossaryIndex
