Simon Hug
Regression, missing data and more
Simon Hug is lecturer at the Department of Political Science at the
University of Geneva, Switzerland and currently visiting scholar at the
University of California, San Diego. He holds a PhD. from the University
of Michigan and taught at the University of Michigan and at the University
of Geneva. His research interests include the formation of new political
parties, the effect of institutions, and more particularly referendums and
federalism, on decision-making and conflict resolution, formal theory and
quantitative methods. His publications include articles on party
formation, green politics, referendums and research methodology in the
"European Journal of Political Research," "Public Choice," "Mobilization,"
"Comparative Political Studies," the "Journal of Conflict Resolution,"
"Party Politics," and other journals as well as in several edited volumes.
He is co-author with Stefano Bartolini and Daniele Caramani of "Political
Parties and Party Systems. A
Bibliographic Guide to the Literature on Parties and Party Systems in
Europe since 1945" (London Sage, 1998) and the author of "Altering Party
Systems. Strategic Behavior and the Emergence of New Political Parties in
Western Democracies" (Ann Arbor University of Michigan Press,
forthcoming)
The starting point of this workshop is a thorough review of the classical
linear regression. Violations of the basic assumptions behind this model
will be discused in detail, and special emphasis will be given to problems
of missing data. Extensions of the linear model and the basic non-linear
models (e.g., probit and logit) will form a second part of the workshop,
while limited dependent variables and problems of selection bias will form
the third part. The material covered will be derived and discussed in
class, and lab sessions will be used to familiarize the students with the
material in a hands-on fashion.
The main objective of the workshop is to impart a thorough knowledge of
this classical tool of empirical analysis. At then end of the workshop,
students should have a firm grasp of when this tool works and when it
fails. Both in theory and in practice they should be able to identify
possible problems and adopt the appropriate remedies.
Bibliography
Basic text/overview
- Achen, Christopher H. 1986. Statistical Analysis of
Quasi-Experiments. Berkeley University of California Press.
- Greene, William H. 1990. Econometric Analysis. New
York MacMillan Publishing Company, ch.20-21.
- Gujarati, Damodar N. 1998. Essentials of Econometrics
2nd edition. New York McGraw-Hill, ch.5-14.
- Gujarati, Damodar N. 1995. Basic Econometrics. 3rd
edition. New York McGraw-Hill, ch.9, 15, 16.
- Hanushek, Eric A.; Jackson, John E. 1977. Statistical
Methods for Social Scientists. New York Academic Press.
Remedial Reading
- Achen, Christopher H. 1982. Interpreting and Using
Regression. Beverly Hills Sage Publications.
- Gujarati, Damodar N. 1998. Essentials of Econometrics.
2nd edition. New York McGraw-Hill, ch.1-4.
- Lewis-Beck, Michael S. 1980. Applied Regression.
Beverly Hills Sage Publications.
Prerequisites
Some notions of probability theory, statistical inference, basic calculus
and matrix algebra (Achen, Gujarati and Lewis-Beck cover most of these
things, while Appendix II in Hanushek and Jackson is a concise review of
matrix algebra)