We use various econometric methods to solve problems and explore causal relationships in social sciences by bridging theoretical models and empirical analyses from quantitative perspectives. In modern social sciences, it has become increasingly important to examine quantitative relations between variables, probe the causality between them, predict their trends, and evaluate the effects of relevant policies. This course provides an introduction to basic econometric methods used in social sciences for students who are interested in empirical and quantitative analyses in various fields of social sciences, especially in the fields of economics, finance and management. Students are assumed to have taken prerequisite courses such as calculus, linear algebra and statistics. A prior course in introductory econometrics is helpful, but not required.
Overview
Syllabus
- Chapter 1 Deriving the OLS estimates for the simple regression model
- 1.1 Deriving the OLS estimates for the simple regression model
- 1.2 Properties of OLS
- Chapter 2 Mechanics of OLS for the multiple regression model
- 2.1 Mechanics of OLS for the multiple regression model
- 2.2 A "partialling out" interpretation
- 2.3 Goodness-of-fit
- 2.4 Unbiasedness of OLS
- 2.5 Sampling variances of the OLS slope estimators
- Chapter 3 Sampling distributions of the OLS estimators
- 3.1 Sampling distributions of the OLS estimators
- 3.2 Testing hypotheses about a single population parameter
- 3.3 Testing multiple linear restrictions
- Chapter 4 Consistency
- 4.1 Consistency
- 4.2 Asymptotic normality of OLS
- Chapter 5 Incorporating nonlinearities into the linear model
- 5.1 Incorporating nonlinearities into the linear model
- 5.2 Controlling for too many factors in regression analysis
- Chapter 6 Separate dummy independent variables
- 6.1 Separate dummy independent variables
- 6.2 Multiple categories and ordinal information
- 6.3 Interactions among dummy variables
- Chapter 7 Homoskedasticity versus heteroskedasticity
- 7.1 Homoskedasticity versus heteroskedasticity
- 7.2 Heteroskedasticity-robust inference
- Chapter 8 Functional form misspecification
- 8.1 Functional form misspecification
- 8.2 Using proxy variables
- Chapter 9 Static and FDL models
- 9.1 Static and FDL models
- 9.2 Unbiasedness of OLS in time series
- 9.3 Consistency of OLS in time series
- Chapter 10 Time dummy variables
- 10.1 Time dummy variables
- 10.2 Difference in differences
- 10.3 Two-period panel data
- Chapter 11 IV for a simple regression
- 11.1 IV for a simple regression
- 11.2 IV for the multiple model
- 11.3 Two-stage least squares
- Chapter 12 Introduction
- 12.1 Introduction
- 12.2 The Solow model
- 12.3 Dynamic panel data model
- 12.4 Estimation issues and data
- 12.5 Estimation results
- 12.6 Inclusion of human capital
- 12.7 A tentative analysis of the estimated region effects
- 12.8 Conclusion
- Final Exam
Taught by
Jiang Yanqing
