Learning Goals
Econometrics
Overview
In Econometrics, students learn to test economic theory and models using empirical data. Students will learn how to construct econometric models from the economic model, and how to estimate the model’s parameters. Econometrics is largely based on the probability and statistics theory taught in the statistics prerequisite for the course. Students will learn introductory regression analysis starting from the ordinary least squares (OLS) estimation method, which is based on the classical linear regression assumptions. The Gauss-Markov Theorem is introduced. Students also learn generalized least squares (GLS) estimation methods when classical linear regression assumptions are violated or relaxed. Instrumental variables (IV) methods, panel data techniques, and time series econometrics are also presented.
Students will be introduced to statistical software packages used to estimate regression models, such as Stata, SPSS, and SAS.
Specific Learning Goals
1. Difference between economic model and econometric model a. Introducing unobserved random error terms into the economic model to make it an econometric model. b. Difference between population and random sample to estimate the unknown population parameters. This should have been introduced in the pre-requisite Economic Statistics, and needs a brief refresh of the concepts. c. Linear regression model and estimating model parameters. 2. Classical linear regression assumptions a. Define the linear regression model based on the classical linear regression assumptions. b. Homoskedasticity and no autocorrelation. c. Normal distribution of the random error term. 3. Ordinary least squares (OLS) estimation a. Intuition behind the OLS estimation and derivation of OLS estimates. b. Statistical properties of OLS estimators. c. Finite sample properties of OLS estimators. d. Gauss-Markov Theorem: Best Linear Unbiased Estimators (BLUE). e. Asymptotic properties of OLS estimators based on the Law of Large Numbers and the Central Limit theorem. 4. Statistical inferences a. Hypothesis testing and confidence interval construction for the population parameters based on the finite sample properties of OLS estimators. This section is heavily based on material from the pre-requisite Economic Statistics. b. Asymptotic tests and confidence intervals. 5. Extensions to the linear regression model a. Regression model specification and its tests. b. Dummy variable regression. 6. Generalized least squares (GLS) estimation a. Heteroskedasticity b. Autocorrelation c. Robust standard error estimation. 7. Instrumental variable (IV) regression a. The endogenous regressor problem 8. Time-series regression a. Stationary series b. Weak exogeneity and asymptotically independent errors 9. Panel regression model a. Pooled regression estimation: Simple panel regression model b. Fixed effect model estimation for a simple panel regression model c. Difference-in-difference estimation 10. Computer skills: At the end of this course, students should be proficient in not only econometric theory, but also empirical estimation of the model using statistical software. This will greatly enhance students’ job market prospects and better prepare them for graduate degree programs.