Final Project Assignment
Reports due by noon, Monday, May 10; Presentations on Thursday, May 13
I hope to have a conference with each group some time during the week of April 26. Please confer with your group members and determine what times would be possible for all or most group members to meet with me. I have classes MWThF 9-10 & 12-1 and MW 3-4. Most other times should be possible for me.
Data sets for those projects that require them will be distributed to team members before the end of this week.
Project Topics and Teams
Group 1: Monte Carlo study of the DF-GLS and other unit-root tests
Team members: Skye Aaron, Robert Kahn, Justin Stewart
Stock and Watson claim that the power of the DF-GLS test is more powerful than the Dickey-Fuller and Phillips-Peron test. This project will use Monte Carlo simulations to assess that claim for varying values (less than one) of the autoregressive parameter and for varying sample lengths. No data are required for this project, just a good research design and a willingness to apply basic programming skills to constructing Stata do files.
Group 2: Monte Carlo study of lag-length criteria
Team members: Cori Savaiano, Erik Swanson, Li Zha
This project uses Monte Carlo simulations to examine the behavior of various lag-length criteria, including the AIC and SBIC, in correcting determining the length of lags. It will examine varying patterns of lag coefficients: for example, gradually declining vs. abruptly truncated.
Group 3: Assessing grade inflation for a sample of colleges
Team members: Ethan Knudson, David Krueger, Suraj Pant, Nina Showell
Gradeinflation.com tracks grade inflation at a sizable sample of U.S. colleges and universities, including Reed. We have obtained grade distribution data for their sample. This project would combine these grade inflation data with other data on college characteristics (selectivity, location, size, ranking, etc.) that are easily obtained from other sources to try to figure out what factors are associated with severe grade inflation.
Group 4: Choice of majors at Reed
Team members: Tian Jiang, Kelsey Lucas, Trey Sands, Tom Verghese
It would be very useful for Reed academic departments and divisions to be able to anticipate the number of majors to be writing theses. This project uses Reed data to model the choice of major, based on admission characteristics such as SAT scores and on the courses taken and grades received in the first year. The data for this project are in Stata, but will require some manipulation to assemble into usable form.
Group 5: Returns to education for French and English speakers in Canada
Team members: Raphael Deem, Andrew Dubay, Tyrone Lee, Luis Lopez
This project uses the 2001 Canadian Census to examine whether the returns to education are different depending on native and learned languages, both in Quebec and in other provinces. The dataset is in a "text" format that can be easily input to Stata.