Applied Advanced Econometrics
Course Syllabus
Fall 2013, TTH 6:408:30pm (CH307)
Prof. K.P. Lin (CH241G, 7253931)
Office Hours: TTH 3:304:30pm 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.
Texts
 W. H. Greene,
Econometric Analysis, 7th ed., Prentice Hall, 2011.
 J. M. Wooldridge,
Econometric Analysis of Cross Section and Panel Data, 2nd ed., The MIP Press, 2010.
 A. Colin Cameron and Pravin K. Trivedi,
Microecometrics: Methods and Applications, Cambridge University Press, 2005 (Recommended).
Software
 Stata 12 or 13, StataCorp, 2011.
Stata is available in the Economics Lab (CH230). A version of Small Stata may be used for the class.
You can order it through Stata Course GradPlan here
(Contact your instructor for the required GradPlan ID).
 C. F. Braum,
An Introduction to Modern Econometrics Using Stata, Stata Press, 2006.
 C. F. Braum,
An Introduction to Stata Programming, Stata Press, 2009.
 A. Colin Cameron and Pravin K. Trivedi,
Microecometrics Using Stata, Revised Edition, Stata Press, 2010.
Course Topics
Lecture notes will be updated and available online for download during class in progress.
Nonlinear Econometrics  Spatial 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
 QuasiMaximum Likelihood: QML
 Using SPPACK: Stata module for crosssection spatialautoregressive 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 45 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©
KuanPin Lin
(Last updated: 12/24/2013)