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Lecture Notes on ADVANCED ECONOMETRICS Lecture Notes on ADVANCED ECONOMETRICS Yongmiao Hong Department of Economics, Department of Statistical Science and Center for Financial Engineering Cornell University Email: [email protected] FALL 2006 c 2006 Yongmiao Hong. All rights reserved. Tables of Contents Chapter 1 Introduction to Econometrics 1.1 Quantitative Features of Modern Economics 1.2 Mathematical Modeling 1.3 Econometric Analysis 1.4 Motivating Examples Chapter 2 General Regression Analysis 2.1 Conditional Probability Distribution 2.2 Regression Analysis 2.3 Linear Regression Modeling 2.4 Correct Model Speci…cation Chapter 3 Classical Linear Regression Models 3.1 Framework and Assumptions 3.2 OLS Estimation 3.3 Goodness of Fit and Model Selection Criteria 3.4 Consistency and E¢ ciency of OLS 3.5 Sampling Distribution of OLS 3.6 Variance Matrix Estimator for OLS 3.7 Hypothesis Testing 3.8 Generalized Least Squares (GLS) Estimation 3.9 Empirical Applications 3.9.1 Foreign Applications 3.9.2 Chinese Applications Chapter 4 Linear Regression Models with I.I.D. Observations 4.1 Introduction to Asymptotic Analysis 4.2 Framework and Assumptions 4.3 Consistency of OLS 4.4 Asymptotic Normality of OLS 4.5 Asymptotic Variance Estimator for OLS 4.5.1 The Case of Conditional Homoskedasticity 4.5.2 The Case of Conditional Heteroskedasticity 4.6 Hypothesis Testing 4.7 Testing for Conditional Homoskedasticity 4.8 Empirical Applications to Cross-Sectional Data 4.8.1 Foreign Applications 4.8.2 Chinese Applications 0 Chapter 5 Linear Regression Models with Dependent Observations 5.1 Basic Concepts in Time Series Analysis 5.2 Framework and Assumptions 5.3 Consistency of OLS 5.4 Asymptotic Normality of OLS 5.5 Asymptotic Variance Estimator for OLS 5.5.1 The Case of Conditional Homoskedasticity 5.5.2 The Case of Conditional Heteroskedasticity 5.6 Hypothesis Testing 5.7 Testing for Serial Correlation 5.8 Empirical Applications to Time Series Data 5.8.1 Foreign Applications 5.8.2 Chinese Applications Chapter 6 Linear Regression Models with Heteroskedastic and Autocorrelated Disturbances 6.1 Framework, Assumptions and Motivating Examples 6.2 Long-run Variance Estimation 6.3 Consistency of OLS 6.4 Asymptotic Normality of OLS 6.5 Hypothesis Testing 6.6 Empirical Applications 6.6.1 Foreign Applications 6.6.2 Chinese Applications Chapter 7 Instrumental Variable Regressions 7.1 Framework, Assumptions, and Motivating Examples 7.2 Two-Stage Least Squares (2SLS) Estimation 7.3 Consistency of 2SLS 7.4 Asymptotic Normality of 2SLS 7.5 Interpretation and Estimation of the 2SLS Asymptotic Variance 7.6 Hypothesis Testing 7.7 Empirical Applications 7.7.1 Foreign Applications 7.7.2 Chinese Applications Chapter 8 Generalized Method of Moments Estimation 8.1 Method of Moments Estimation 8.2 Generalized Method of Moments (GMM) Estimation 8.3 Consistency of GMM 1 8.4 Asymptotic Normality of GMM 8.5 Asymptotic E¢ ciency of GMM 8.6 Asymptotic Variance Estimation 8.7 Hypothesis Testing 8.8 Model Speci…cation Testing 8.9 Empirical Applications 8.9.1 Foreign Applications 8.9.2 Chinese Applications Chapter 9 Maximum Likelihood Estimation and Quasi-Maximum Likelihood Estimation 9.1 Motivating Examples 9.2 Maximum Likelihood Estimation (MLE) and Quasi-MLE 9.3 Asymptotic Properties of MLE/QMLE 9.3.1 Consistency 9.3.2 Asymptotic Normality 9.3.3 E¢ ciency 9.4 MLE-based Hypothesis Testing 9.4.1 Wald Test 9.4.2 Lagrange Multiplier Test 9.4.3 Likelihood Ratio Test 9.5 QMLE-based Hypothesis Testing 9.5.1 Impact of Model Misspeci…cation 9.5.2 Wald Test 9.5.3 Lagarnge Multiplier Test 9.6 Model Speci…cation Testing 9.7 Empirical Applications 3.7.1 Foreign Applications 3.7.2 Chinese Applications Chapter 10 Conclusion Appendix A Introduction to Linear Algebra Appendix B Asymptotic Tools in Econometrics References Preface Modern economies are full of uncertainties and risk. Economics studies resource allocations in an uncertain market environment. As a generally applicable analytic tool for uncertain events, probability and statistics have been playing an important role in economic research. Econometrics is statistical analysis of economic and …nancial data. It has become an integral of training in modern economics and business. This book develops a coherent set of econometric theory and methods for economic models. It is written for an advanced econometrics course for doctoral students in economics, business and management. Chapter 1 is a general introduction to econometrics. It …rst describes the two most important features of modern economics, namely mathematical modeling and empirical veri…cation, and then discusses the role of econometrics as a methodology in empirical studies. A few motivating economic motivated examples are given to illustrate how econometrics can be used in empirical studies. Finally, it points out the limitations of econometrics and economics due to the fact that an economy is not a repeatedly controlled experiment. Assumptions and careful interpretations are needed when conducting empirical studies in economics and …nance. Chapter 2 introduces a general regression analysis. Regression analysis is modeling, esti- mation, inference, and speci…cation analysis of the conditional mean of economic variables of interest given a set of explanatory variables. It is most widely applied in economics. Among other things, this chapter interprets the mean squared error and its optimizer, which lays down the probability-theoretic foundation for least squares estimation. In particular, it provides an interpretation for the least squares estimator and its relationship with the true model parameter. Chapter 3 introduces the classical linear regression analysis. A set of classical assumptions are given and discussed, and conventional statistical procedures for estimation, inference, and hypothesis testing are introduced. We also discuss the generalized least squares estimation as an e¢ cient estimation method of a linear regression model when the variance-covariance matrix is known up to a constant. In particular, the generalized least squares estimation is embedded as an ordinary least squares estimation of a suitably transformed regression model via conditional variance scaling. The subsequent chapters 4–7 are the generalizations of classical linear regression analysis when various classical assumptions fail. Chapter 4 …rst relaxes the normality and conditional homoskedasticity assumptions, two key conditions assumed in classical linear regression modeling. An large sample theoretic approach is taken. For simplicity, it is assumed that the observed data is generated from an independent and identically distributed random sample. It is shown that while the …nite distributional theory is no longer valid, the classical statistical procedures are still 1 approximately applicable when the sample size is large, if conditional homoskedasticity holds. In contrast, if the data displays conditional heteroskedasticity holds, classical statistical procedures are not applicable even for large samples. In contrast, heteroskedasticity-robust procedures are called for. Chapter 5 extends the linear regression theory to time series data. First, it introduces a variety of basic concepts in time series analysis. Then it shows that the large sample theory for i.i.d. random samples carries over to stationary ergodic time series data if the regression error follows a martingale di¤erence sequence. Chapter 6 extends the large sample theory to a very general case where there exist conditional heteroskedasticity and autocorrelation. In this case, the classical regression theory cannot be used, and a long-run variance-covariance estimator is called for to validate statistical inferences. Chapter 7 is the instrumental variable estimation for linear regression models, where the regression error is correlated with the regressors. This can arise due to measurement errors and simultaneous equation biases. Two stage-least squares estimation method and related statistical inference procedures are fully exploited. Chapter 8 introduces the generalized method of moments, which is a popular estimation method for possibly nonlinear econometric models characterized as a moment condition. Indeed, most economic theories, such as rational expectations, can be formulated by a moment condi- tion. The generalized method of moments is particularly suitable to estimate model parameters contained in the moment condition. Chapter 9 introduces the maximum likelihood estimation and the quasi-maximum likelihood estimation methods for conditional probability models and other nonlinear econometric models. Chapter 10 concludes the book by summarizing the main econometric theory and methods covered in this book, and pointing out directions for further buildup in econometrics. The appendix contains a relatively comprehensive review of asymptotic analytic tools that are needed for this course. By going through this book, students will learn how to do asymptotic analysis for econometric models. Such skills are useful not only for those students who intend to work on theoretical econometrics, but also for those who intend to work on applied subjects in economics because with such analytic skills, students will be able to understand more specialized or more advanced econometrics textbooks. This book is based on my lecture notes taught at Cornell University, Renmin University of China, Shandong University, Shanghai Jiao Tong University, Tsinghua University, and Xiamen University, where the graduate students provide rather detailed comments on my lecture notes. 2 CHAPTER
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