Foundations of Modern Econometrics: A Unified Approach

Description

Modern economies are full of uncertainties and risk. Economics studies resource allocations in an uncertain market environment. As a generally applicable quantitative analytic tool for uncertain events, probability and statistics have been playing an important role in economic research. Econometrics is statistical analysis of economic and financial data. In the past four decades or so, economics has witnessed a so-called ""empirical revolution"" in its research paradigm, and as the main methodology in empirical studies in economics, econometrics has been playing an important role. It has become an indispensable part of training in modern economics, business and management. This book develops a coherent set of econometric theory, methods and tools for economic models. It is written as a textbook for graduate students in economics, business, management, statistics, applied mathematics, and related fields. It can also be used as a reference book on econometric theory by scholars who may be interested in both theoretical and applied econometrics. Contents Preface 1. Introduction to Econometrics 1.1 Introduction 1.2 Quantitative Features of Modern Economics 1.3 Mathematical Modeling 1.4 Empirical Validation 1.5 Illustrative Examples 1.6 Limitations of Econometric Analysis 1.7 Conclusion Exercise 1 2. General Regression Analysis 2.1 Conditional Probability Distribution 2.2 Conditional Mean and Regression Analysis 2.3 Linear Regression Modeling 2.4 Correct Model Specification for Conditional Mean 2.5 Conclusion Exercise 2 3. Classical Linear Regression Models 3.1 Framework and Assumptions 3.2 Ordinary Least Squares (OLS) Estimation 3.3 Goodness of Fit and Model Selection Criteria 3.4 Consistency and Efficiency of the OLS Estimator 3.5 Sampling Distribution of the OLS Estimator 3.6 Variance Estimation for the OLS Estimator 3.7 Hypothesis Testing 3.8 Applications 3.9 Generalized Least Squares Estimation 3.10 Conclusion Exercise 3 4. Linear Regression Models with Independent Observations 4.1 Introduction to Asymptotic Theory 4.2 Framework and Assumptions 4.3 Consistency of the OLS Estimator 4.4 Asymptotic Normality of the OLS Estimator 4.5 Asymptotic Variance Estimation 4.6 Hypothesis Testing 4.7 Testing for Conditional Homoskedasticity 4.8 Conclusion Exercise 4 5. Linear Regression Models with Dependent Observations 5.1 Introduction to Time Series Analysis 5.2 Framework and Assumptions 5.3 Consistency of the OLS Estimator 5.4 Asymptotic Normality of the OLS Estimator 5.5 Asymptotic Variance Estimation for the OLS Estimator 5.6 Hypothesis Testing 5.7 Testing for Conditional Heteroskedasticity and Autoregressive Conditional Heteroskedasticity 5.8 Testing for Serial Correlation 5.9 Conclusion Exercise 5 6. Linear Regression Models Under Conditional Heteroskedasticity and Autocorrelation 6.1 Motivation 6.2 Framework and Assumptions 6.3 Long-Run Variance-Covariance Matrix Estimation 6.4 Consistency of the OLS Estimator 6.5 Asymptotic Normality of the OLS Estimator 6.6 Hypothesis Testing 6.7 Testing Whether Long-Run Variance-Covariance Matrix Estimation Is Needed 6.8 Ornut-Cochrane Procedure 6.9 Conclusion Exercise 6 7. Instrumental Variables Regression 7.1 Motivation 7.2 Framework and Assumptions 7.3 Two-Stage Least Squares (2SLS) Estimation 7.4 Consistency of the 2SLS Estimator 7.5 Asymptotic Normality of the 2SLS Estimator 7.6 Interpretation and Estimation of Asymptotic Variance-Covariance Matrix of the 2SLS Estimator 7.7 Hypothesis Testing 7.8 Hausman’s Test 7.9 Conclusion Exercise 7 8. Generalized Method of Moments Estimation 8.1 Introduction to Method of Moments Estimation 8.2 Generalized Method of Moments (GMM) Estimation 8.3 Consistency of the GMM Estimator 8.4 Asymptotic Normality of the GMM Estimator 8.5 Asymptotic Efficiency of the GMM Estimator 8.6 Two-Stage Asymptotically Most Efficient GMM Estimation 8.7 Asymptotic Variance-Covariance Matrix Estimation 8.8 Hypothesis Testing 8.9 Model Specification Testing 8.10 Conclusion Exercise 8 9. Maximum Likelihood Estimation and Quasi-Maximum Likelihood Estimation 9.1 Motivation 9.2 Maximum Likelihood Estimation (MLE) and Quasi-MLE (QMLE) 9.3 Statistical Properties of MLE/QMLE 9.4 Correct Model Specification and Its Implications 9.5 Asymptotic Distribution of MLE 9.6 Consistent Estimation of Asymptotic Variance-Covariance Matrix of MLE 9.7 Parameter Hypothesis Testing Under Correct Model Specification 9.8 Model Misspecification for Conditional Probability Distribution and Its Implications 9.9 Asymptotic Distribution of QMLE 9.10 Asymptotic Variance Estimation of QMLE 9.11 Hypothesis Testing Under Model Misspecification 9.12 Specification Testing for Conditional Probability Distribution Model 9.13 Conclusion Exercise 9 10. Modern Econometrics: Retrospect and Prospect 10.1 Summary of Book 10.2 Assumptions of Classical Econometrics 10.3 From Normality to Nonnormality 10.4 From Independent and Identically Distributed Disturbances to Conditional Heteroskedasticity and Autocorrelation 10.5 From Linear to Nonlinear Models 10.6 From Exogeneity to Endogeneity 10.7 From Correct Model Specification to Model Misspecification 10.8 From Stationarity to Nonstationarity 10.9 From Econometric Models to Economic Theories 10.10 From Traditional Data to Big Data 10.11 Conclusion Exercise 10 Bibliography Index