EC 570 Econometrics

Course Syllabus

Fall 2016, 4:40 - 6:30pm TTH (CH-307)
Prof. K.-P. Lin (CH 241-G, 725-3931)
Office Hours: 3:30-4:30 TTH & by appointment
(Last updated: 11/28/2016)

This series of graduate level econometrics courses is designed to teach students with basic quantitative and computer skills for economic modeling, analysis and application. This course discusses basic econometric techniques and applications, while the next sequence EC 571 covers more advanced topics.

Prerequisites

EC 480 Mathematical Economics and EC 469 Introduction to Econometrics provide the quantitative fundamentals for this courses. Basic knowledge of calculus, matrix algebra, statistical inference and probability theory are required (e.g., MTH 251, 252, 261; STAT 243, 244). Familiar with computer programming and econometric packages will be useful but not necessary. The programming language and packages in R will be used throughout the course.

Texts and Software

Required: W. H. Greene, Econometric Analysis, 7th ed., Pearson Education, 2012.
Download and install R and RStudio.
Recommended:
1. CRAN Task View: Econometrics
2. Roger D. Peng, R Programming for Data Sciences, Learnpub.com, 2015.
3. Grant V. Farnsworth, Econometrics in R, 2008.
4. Christian Kleiber and Achim Zeileis, Applied Econometrics with R, Springer-Verlag, New York, 2008.
5. Vikram Dayal, An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing, Springer Briefs in Economics, Springer (India), 2015.
6. Florian Heiss, Using R for Introductory Econometrics, CreatSpace, 2016.
7. A. Colin Cameron and Pravin K. Trivedi, Microecometrics: Methods and Applications, Part V, Models for Panel Data, Cambridge University Press, 2005.
8. J. M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, 2nd ed., The MIP Press, 2010.

Course Topics

Lecture notes and examples will be updated and available for download during class in progress.
Notes: R, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11.
Examples (Greene's): F1.1, F2.1, F2.2, F3.1, F4.4, F5.2,
Examples: 0.1, 0.2, 0.3, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.1, 2.2, 2.3, 3.1, 3.2

Classical Linear Regression Model
1. Least Squares Estimation
2. Small Sample Theory
3. Large Sample Theory
Applications
1. Dummy Variable and Structural Change
2. Model Comparison, Evaluation, and Selection
Generalized Linear Regression Model
1. Heteroscedasticity
2. Autocorrelation

Course Expectation

For this course, there are two (2) tests: midterm and final. In addition, there are 4-5 homeworks (once every two weeks in average). Also there is a course project due at the end of term. The time schedule and grade distribution are as follows:

Midterm November 3 (Week-6, Thursday), in class (30%)

Final December 6 (Week-11, Tuesday) (30%)

Project December 6 (Week-11, Tuesday) (20%)

Homework Due every 2 weeks (20%)

Homework

Homework 1: Due Week-3, 10-13
Homework 2: Due Week-5, 10-27
Homework 3: Due Week-8, 11-17
Homework 4: Due Week-10, 12-1

Guideline on Writing a Course Project

For assistance in writing a course project or paper, please check out the writing resources and workshops at PSU Writing Center.

Format

5-10 pages typed (double-space and wide margins).
The model presented has to be an original econometric model.
The format of the paper should follow a standard journal article closely.
Supporting data and computer program printout have to be included, but not counted for the page number.

Introduction and brief discussion of the main results.
Full explanation of estimation, hypothesis testings, and model specifications.
Detailed interpretation of the model and its policy implication, if any.
Extensions could be taken up in EC 571 next term.
References (including data sources).

Grade and Deadlines

The project is evaluated based on its originality, creativity, and consistency with the format and content requirements described above.
Project proposal (1 page typed): Week-7, November 7 or earlier.
Project due: December 6 (Tuesday).