spContent=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.
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.
—— Instructors
About this course
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 course develops a coherent set of econometric theory, methods and tools for economic models. This course will be useful for graduate students from economics, business, management, statistics, applied mathematics, data science and related fields.
Syllabus
Introduction to Econometrics
1.1 General Methodology of Modern Economic Research
1.2 Roles of Econometrics
1.3 Illustrative Examples
1.4 Limitations of Econometric Analysis
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
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 Efficiency of OLS
3.5 Sampling Distribution of OLS
3.6 Variance Matrix Estimator for OLS
3.7 Hypothesis Testing
3.8 Applications
3.9 Generalized Least Squares (GLS) Estimation
3.10 Conclusion
Linear Regression Models with I.I.D. Observations
4.1 Motivation
4.2 Introduction to Asymptotic Theory
4.3 Framework and Assumptions
4.4 Consistency of OLS
4.5 Asymptotic Normality of OLS
4.6 Asymptotic Variance Estimator for OLS
4.7 Hypothesis Testing
4.8 Testing for Conditional Homoskedasticity
4.9 Empirical Applications
4.10 Conclusion
Linear Regression Models with Dependent Observations
5.1 Introduction to 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
5.6 Hypothesis Testing
5.7 Testing for Conditional Heteroskedasticity and Autoregressive Conditional Heteroskedasticity
5.8 Testing for Serial Correlation
5.9 Conclusion
Linear Regression Models under Conditional Heteroskedasticity and Autocorrelation
6.1 Motivating Examples
6.2 Framework and Assumptions
6.3 Long-run Variance Estimation
6.4 Consistency of OLS
6.5 Asymptotic Normality of OLS
6.6 Hypothesis Testing
6.7 Testing Whether Long-run Variance Estimation Is Needed
6.8 A Classical Ornut-Cochrane Procedure
6.9 Empirical Applications
6.10 Conclusion
Instrumental Variables Regression
7.1 Motivating Examples
7.2 Framework and Assumptions
7.3 Two-Stage Least Squares (2SLS)
Estimation
7.4 Consistency of 2SLS
7.5 Asymptotic Normality of 2SLS
7.6 Interpretation and Estimation of the 2SLS Asymptotic Variance
7.7 Hypothesis Testing
7.8 Hausman’s Test
7.9 Empirical Applications
7.10 Conclusion
Generalized Method of Moments Estimation
8.1 Introduction to the Method of Moments Estimation
8.2 Generalized Method of Moments (GMM) Estimation
8.3 Consistency of GMM
8.4 Asymptotic Normality of GMM
8.5 Asymptotic Efficiency of GMM
8.6 Optimality of Two-stage GMM Estimation
8.7 Asymptotic Variance Estimation
8.8 Hypothesis Testing
8.9 Model Specification Testing
8.10 Empirical Applications
8.11 Conclusion
Maximum Likelihood Estimation and Quasi-Maximum Likelihood Estimation
9.1 Motivating Examples
9.2 Maximum Likelihood Estimation (MLE) and Quasi-MLE
9.3 Consistency of MLE/QMLE
9.4 Implication of Correct Model Specification for Conditional Distribution
9.5 Asymptotic Distribution of MLE
9.6 Asymptotic Variance Estimation of MLE
9.7 MLE-based Hypothesis Testing Under Correct Model Specification
9.8 Implication of Model Misspecification for Conditional Distribution
9.9 Asymptotic Distribution of QMLE
9.10 Asymptotic Variance Estimation of QMLE
9.11 QMLE-based Hypothesis Testing Under Model Misspecification
9.12 Model Specification Testing
9.13 Empirical Applications
9.14 Conclusion
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