Applied Advanced Econometrics

Course Syllabus

Fall 2013, TTH 6:40-8:30pm (CH-307)
Prof. K.-P. Lin (CH-241G, 725-3931)
Office Hours: TTH 3:30-4:30pm or by Appointment

This course covers advanced topics related to methodological issues in econometrics, with emphases on computation intensive methods and applications. Because most of econometric computation is build upon the matrix programming, MATA in Stata will be introduced and used throughout the course.

This course assumes background knowledge of linear econometric models, and study the nonlinear models and optimization techniques. The purpose of this course is to prepare students with a broad coverage of econometric methods and applications capable of doing independent research project. In addition to economic theory, knowledge of mathematics, statistics, and basic econometrics is required. Experience of computer programming is helpful but not necessary.



Course Topics

Lecture notes will be updated and available online for download during class in progress.

Nonlinear EconometricsSpatial Econometrics
  • Introduction to Spatial Econometric Analysis
    • Nonparametric Treatment of Spatial Dependence
    • Parametric Representation of Spatial Dependence
  • Spatial Linear Econometric Models
    • Spatial Heterogenous Models
    • Spatial Autoregressive Models
    • Spatial Error Component Models
  • Hypothesis Testing of Spatial Dependence
    • Test for Spatial Heterogeneity
    • Test for Spatial Autocorrelation
  • Spatial Econometric Model Estimation
    • Least Squares: OLS, IV/2SLS
    • Generalized Method of Moments: GMM
    • Quasi-Maximum Likelihood: QML
  • Using SPPACK: Stata module for cross-section spatial-autoregressive models (D. Drukker, H. Peng, I. Prucha, R. Raciborski)
  • Lecture Notes: 1, 2, 3, 4, 5

Course Expectation

This course consists of lectures, readings, homework, presentation (or project), and final exam. Each student is expected to present the assigned readings on a specific topic. The presentation may be subsituted by an approved course project. In addition, there are 4-5 homeworks (once every two weeks in average). Doing homework is very important not only to understand the theoretical concepts but also to learn structural and efficient computing techniques for econometric estimation and inference. The time schedule and grade distribution are as follows:

Copyright© Kuan-Pin Lin
(Last updated: 12/24/2013)