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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.
1 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
Course Introduction and Course Requirements
2 General Regression Analysis
2.2 Conditional Mean and Regression Analysis
2.4 Correct Model Specification for Conditional Mean
2.3 Linear Regression Modeling
2.1 Conditional Probability Distribution
3 Classical Linear Regression Models (1)
3.5 Sampling Distribution of OLS
3.3 Goodness of Fit and Model Selection Criteria
3.2 OLS Estimation
3.1 Frameworks and Assumptions
3.4 Consistency and Efficiency of OLS
3 Classical Linear Regression Models (2)
3.8 Applications
3.6 Variance Matrix Estimator for OLS
3.9 Generalized Least Squares (GLS) Estimation
3.7 Hypothesis Testing
Quiz 1
4 Linear Regression Models with I.I.D. Observations
4.4 Asymptotic Normality of OLS
4.5 Asymptotic Variance Estimator
4.6 Hypothesis Testing
4.1 Introduction to Asymptotic Theory
4.2 Framework and Assumptions and 4.3 Consistency of OLS
Linear Regression Models with Dependent Observations (1)
5.1 Introduction to Time Series Analysis
5.2 Framework and Assumptions and 5.3 Consistency of OLS
Linear Regression Models with Dependent Observations (2)
5.7 Testing for Conditional Heteroskedasticit
5.6 Hypothesis Testing
5.8 Testing for Serial Correlation
5.4 Asymptotic Normality of OLS and 5.5 Asymptotic Variance Estimator for OLS
Quiz 2
Linear Regression Models under Conditional Heteroskedasticity and Autocorrelation
6.1 Framework and Assumptions
Introduction Linear Regression Models Under Conditional Heteroskedasticity and Autocorrelation
6.5 Hypothesis Testing and 6.7 A Classical Ornut-Cochrane Procedure
6.2 Long-run Variance Estimation
6.3 Consistency of OLS and 6.4 Asymptotic Normality of OLS
Instrumental Variables Regression
7.2 Two-Stage Least Squares (2SLS) Estimation
7.7 Hausman's Test
7.1 Framework and Assumptions
7.3 Consistency of 2SLS and 7.4 Asymptotic Normality of 2SLS
7.6 Hypothesis Testing
Quiz 3
Update 5.1
5.1 Introduction to Time Series Analysis
Generalized Method of Moments Estimation
8.6 Asymptotic Variance Estimator
8.5 Asymptotic Efficiency of GMM
8.3 Consistency of GMM
8.7 Hypothesis Testing
8.2 Generalized Method of Moments (GMM) Estimation
8.8 Model Specification Testing
8.4 Asymptotic Normality of GMM
8.1 Introduction to the Method of Moments Estimation (MME)
Update Chapter4
4.7 Testing for Conditional Homoskedasticity
4.2 Frameworks and Assumptions
4.3 Consistency of OLS
4.8 Conclusion
4.4 Asymptotic Normality of OLS
4.6 Hypothesis Testing
4.5 Asymptotic Variance Estimation
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