Regression

How can I use this data to distinguish causality from correlation?




Introduction to Econometrics


The course

Stata : is the software required for the course. Any version of the program is acceptable, so if you already own a copy or get it from some other source this is fine. The cheapest is Small Stata (the student version) but you may have some issues with this since it will not handle some of our homework assignments. Library computers at UVM have the full version of Stata so you might be able to use Small Stata and do the large assignments in the library. If you prefer, order Stata/IC 12 which will handle everything we will do but is a bit more expensive. Students are able to order online with the Grad Plan ID using code BGV12. See below for recommended texts for using Stata.

Paper: The paper is an original research project with actual data. It is strongly suggested that you do some preliminary research to find an econometric model that you can update or extend. Journals, such as the American Economic Review or the Journal of Political Economy will have many relevant articles. More recent numbers, however, might be very difficult to read and understand. It is perhaps a better idea to look at older issues, even as far back as the 1950s, for simpler and more straightforward examples of applied models that could be updated. Papers will be graded on how well the research is presented, relevance of the model, the adequacy of the data set and how possible objections are anticipated and dealt with. A good paper need not be at the frontiers of econometric sophistication, but will be well researched, documented and formatted according to instructions given in class. See below for dates for first and final drafts of the paper. No late papers accepted.

Homework assignments: will be done in Excel and submitted electronically using the format: student_number.xls. The homework you submit must be your own. If two spreadsheets are found to be identical, both students will get zeros for the assignment.

Grading: Midterm exam, final exam, original research paper, homework. All worth 25 percent of the final grade.

  • Grades by nom de guerre
  • Part 1: Classical Linear Regression Model

    Jan 17-31 Reading: Chapters 4-5. Stock and Watson, Chapter 4.

    Part 2: Linear regressors with multiple regressors, hypothesis testing and nonlinear regression

    Feb 7-28: (No class Thurs 2 Feb) Reading: Chapter 6-9.

    Midterm

    Thurs 1 March in class, covering all readings and class material to date.

    Part 3: Panel Data, Binary Dependent Variable and Instrumental Variables

    Mar 13-Apr 19. Reading: Chapters 10-13.

    Paper due

    22 Apr midnight, submitted as student_number.docx

    Part 4: Time Series

    24 April-1 May Reading: Chapters 14-15.

    Final Exam

    8 May 4:30-5:45 pm. Comprehensive--1 hr and 15 minutes.

    UVM Final exam schedule-Spring 2011
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