2005-2006 ITV Course Schedule
Fall:
"Panel Data Analysis" - Brian Gaines
Wednesdays: 1:30-3:30 CST
Schedule: Oct. 12, 19, 26 Nov. 9, 16, 30 Dec. 7 (Off Nov. 2 and 23)
Email: bjgaines@uiuc.edu
Winter:
"Time Series, Introduction" - Jan Box-Steffensmeier, John Freeman and Jon Pevehouse
Fridays: 11:00-1:00 CST
Schedule: Jan. 20, 27, Feb. 3, 10, 17, 24 Mar. 3
Email: steffensmeier.2@osu.edu
Email: freeman@polisci.umn.edu
Email: pevehouse@polisci.wisc.edu
Spring:
"Time Series, Advanced" - Jan Box-Steffensmeier, John Freeman and Jon Pevehouse
Fridays: 11:00-1:00 CST
Schedule: Mar. 10, 17, 31 April 7, 14, 28 May 5 (Off Mar. 24 and April 21)
Email: steffensmeier.2@osu.edu
Email: freeman@polisci.umn.edu
Email: pevehouse@polisci.wisc.edu
Course Descriptions
Panel Data Analysis
Instructor: Brian Gaines, University of Illinois Urbana-Champaign
Email: bjgaines@uiuc.edu
Syllabus: pdf version
Times: 1:30 - 3:30 CST, Wednesdays
Autumn Schedule:
Oct. 12, 19, 26
Nov. 9, 16, 30
Dec. 7
Description:
This course will introduce the very large and rapidly growing statistical literature on analyzing data in which observations are a cross section of some units (e.g. individuals, countries, states, firms) over multiple time periods. I will use "panel data" in a broad sense to mean any data spanning multiple dimensions, usually (but not necessarily) time and space. I will not explicitly restrict attention to cases in which we have much larger Ns than Ts, as is sometimes done. In general terms, the advantage to having time and space variance is that one can avoid inferring that inter-personal differences across units are equivalent to inter-temporal differences within units. For instance, in a cross-section if we find that age is a significant predictor of voting, we often infer that this result implies a forecast that all members of the relevant population will vote with increasing probability as they age. From a time series on just one unit, we might make the companion inference: that an observed life-cycle change in one unit implies across-age variation in a population that is heterogeneous in age in any given time period. With data that vary on both dimensions, one can better disentangle inter-unit and inter-temporal variance, and thus, in many contexts, adjudicate between rival behavioral theories. The increasing availability of large data sets and powerful computers has made panel models more and more prominent in social science data analysis.
None of the ITV classes assume any specialized skills or prerequisites beyond familiarity with classic regression and a degree of comfort with matrix calculations. In a perfect world, one might study both maximum likelihood estimation and time series analysis in advance of panel data models, but I will not assume that students have followed that particular sequence. I’ll briefly review some results in matrix algebra early in the course to setup exposition of models thereafter, so familiarity with matrix computations will be helpful ("familiarity with" need not be read as "mastery of".)
In seven weeks, we cannot possibly cover even a full introductory text, let alone the whole field, so I must emphasize the first word in the title: this course should be regarded as only a first step. We will mainly work out of Baltagi’s Econometric Analysis of Panel Data 3rd Edition (Wiley, 2005), but I’ll also borrow from Cheng Hsiao’s Analysis of Panel Data (Cambridge, 2003) and Longitudinal and Panel Data by Edward Frees (Cambridge, 2004). (The 3rd ed. of Baltagi is quite new and goes for about $80—getting a used copy of the 2nd ed. instead shouldn’t be disastrous. Hsaio’s book is a much better deal, at about $30, and also a fine reference. The Frees book goes for about $40.) A few political science applications will be added, and we’ll deal with computing issues at least briefly. However, the course will probably, given its brevity and introductory nature, emphasize theory at the expense of practice.
- Wed 12.10: Introduction, Preliminaries, Analysis of Variance
- Wed. 19.10: ANCOVA, Fixed Effects
- Wed. 26.10: Random Effects
- Wed. 09.11: Hypothesis Tests
- Wed. 16.11: Heteroskedasticity, Serial Correlation
- Wed. 30.11: Dynamic Models
- Wed. 07.12: TBD (possible guest lecture)
-- Wed. 02.11: NO CLASS
-- Wed. 23.11: NO CLASS (THANKSGIVING BREAK)
An additional caveat is that this will be only the second time I’ve given this class, and I’ll be continuing to experiment in what to cover, level of detail, and so on.
The evaluands will be a couple of problem sets plus a short paper. The paper is meant to be a research note, a short (7-12 pages) work that is built around a panel data analysis. My intention is to give students carte blanche on topic so that everyone can work on a project that will be useful to them beyond fulfilling a course requirement. As the class proceeds, each student should keep me abreast of their plans, their success is obtaining the necessary data, and so on.
We’ll definitely spend some time in later sessions discussing applications and articles, and those will either be readily accessible (e.g. at JSTOR) or circulated in advance. Lecture notes will generally be available in advance of the class, though I may sometimes post items after the lecture in question as well. The precise URL will be forthcoming, but it will be connected to the main ITV webstite:
Time Series Analysis
Instructors:
Jan Box-Steffensmeier, The Ohio State University
email: steffensmeier.2@osu.edu
John Freeman, University of Minnesota
email: freeman@polisci.umn.edu
Jon Pevehouse, University of Wisconsin
email: pevehous@polisci.wisc.edu
Syllabus: Download the syllabus
Times: 11:00-1:00 CST, Fridays
Winter Schedule:
January 20, 27
February 3, 10, 17, 24
March 3
Spring Schedule:
March 10, 17, 31
April 7, 14, 28
May 5
View Course Website
Description:
This course considers statistical techniques to evaluate social processes occurring through time. The course introduces students to time series methods and to the applications of these methods in political science. After a brief review of the calculus of finite differences and other estimation techniques, we study stationary ARMA models. In the next section of the course, we examine a number of important topics in time series analysis including "reduced form" methods (granger causality and vector autogression), unit root tests, near-integration, fractional integration, cointegration, and error correction models. Time series regression is also discussed (including pooling cross-sectional and time series data). We learn not only how to construct these models but also how to use them in policy analysis.
We expect students to have a firm grounding in probability and regression analysis and to bring to the course some interesting questions about the dynamics of political processes. The emphasis throughout the course will be on application, rather than on statistical theory. However, the focus of most lectures will be statistical theory. Homework will revolve as much as possible around the time series you are interested in understanding. To that end, students will need to gather time serial data for analysis during the first week of class (this data need not be used throughout the term, though that would make your life easier). The length of the series should be at least 40 time points; longer series are better than shorter ones.
This is the first part of a fourteen-week seminar team-taught by Professors John Freeman, Janet Box-Steffensmeier, and Jon Pevehouse. Students are strongly encouraged to take both parts of the course.
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