Home » Faculty and Staff » I-Ming Chiu » Econometrics

Econometrics

Rutgers University
The State University of New Jersey
Department of Economics – CCAS
Summer 2014

Class Information

Course Title: Econometrics (index#05179)
Economics 50:220:322/Section H6

Instructor: Dr. I-Ming Chiu

Office: ARMITAGE 328
Phone: (856) 225 6012

E-mail address: ichiu@camden.rutgers.edu

Class Meeting: BSB 336.  6:00-9:40 PM (Tuesday & Thursday, 07/07~08/13)

Office Hours: 4:30-5:30 PM, Tuesday & Thursday or by appointment

Course Description:

Econometrics is a branch of economics. It applies mathematical and statistical methods to explore and quantify the relationship between economic variables. The class will begin with a brief review of set theory, function, random variable, commonly used probability distributions (discrete and continuous), statistical inference, and then cover the essential part of this class: regression model. In addition to introducing students to the theoretical parts of the regression model, the main focus is to show students how to apply the model using economic data. The ultimate goal of this class is to equip students with analytical ability to explain and forecast economic phenomena. With these quantitative skills in hand students will become more competitive in the job market.

Textbook (required; either one):

  • Roberto Pedace, Econometrics for Dummies, Wiley, 2013 (available at bookstore).
  • R. L. Thomas, Modern Econometrics: An Introduction, Longman U.K., 1997.

Other References (recommended):

  • Michael W. Trosset, An Introduction to Statistical Inference and Its Applications with R, CRC Press, 2009.
  • Jared P. Lander, R for Everyone: Advanced Analytics and Graphics, Pearson, 2014 (available at bookstore).
  • David Poole, Linear Algebra: A Modern Introduction, 3rd Edition, Brooks/Cole, Cengage Learning, 2011.

Computing:

All of the computations will be done using an open source statistical software R. The other software package SAS/IML will also be introduced as an alternative to deal with matrix operations for data analysis. R can be downloaded at http://www.r-project.org.

Class Material:

Handouts, readings, data, and homework assignments will be posted on Sakai website.

Useful Websites:

http://www.dummies.com/how-to/education-languages/Economics/Econometrics.html (Econometrics for Dummies textbook companion website)

http://www.ats.ucla.edu/stat/ (Learn a variety of statistical software packages and statistical methods from UCLA web site)

Economic Data:

http://www.federalreserve.gov/econresdata/statisticsdata.htm (the Federal Reserve)

http://finance.yahoo.com  (Yahoo Finance Section)

http://www.bea.gov  (Bureau of Economic Analysis)

Grading:                     Contribution to Final Grade

– Attendance                                                         5%

– Take-home problems                                         50%

– Midterm Exam (1)                                             20%

– Final Exam (or Project)                                      25%

– Participation (extra credit)                                   5%

Grading Policy:

Term grades will be based on the final distribution of the above grading weights.

Exam Preparation:

The exam questions will be drawn from three sources: (i) homework assignments, (ii) course lectures, and (iii) reading material.

Class Participation:

Class attendance is essential for learning achievement. When missing a class, it would cost you more time to learn on your own. I strongly recommend the following steps for your successful learning: (1) attend every class and take notes; (2) review everything you learn from the class immediately, never put it off; (3) ask questions and participate in class discussions.

Academic conduct:

Make up exams will be given only upon prior notice. I request prior knowledge of any expected absence from an exam. If this is not feasible, you can document a valid reason for missing the exam. Unexcused absence on any exam will result in a grade of zero. Dishonesty in seeking an excused absence or in the examination process will result in a grade of zero on the exam involved and in university discipline.

Course Outline:

Topic 1

Probability Review

Topic 2

Statistical Inference Review

Topic 3

Matrix Operations

Topic 4

Econometrics & Statistical Learning

Topic 5

Simple Linear Regression Model

Midterm Exam

Date: TBA

Topic 6

Multiple Linear Regression Model

Topic 7

Dummy Variable Regression Model

Topic 8

Transformations

Topic 9

Regression Diagnostics

Topic 10

Logit and Probit Model

Final Exam

Last Day (08/12) of the Class

Notice: there are five R lab sessions.