Swiss Summer School 1999

Simon Hug
Regression, missing data and more

Simon Hug

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)

Workshop contents and objectives

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

  1. Achen, Christopher H. 1986. Statistical Analysis of Quasi-Experiments. Berkeley University of California Press.
  2. Greene, William H. 1990. Econometric Analysis. New York MacMillan Publishing Company, ch.20-21.
  3. Gujarati, Damodar N. 1998. Essentials of Econometrics 2nd edition. New York McGraw-Hill, ch.5-14.
  4. Gujarati, Damodar N. 1995. Basic Econometrics. 3rd edition. New York McGraw-Hill, ch.9, 15, 16.
  5. Hanushek, Eric A.; Jackson, John E. 1977. Statistical Methods for Social Scientists. New York Academic Press.
Remedial Reading
  1. Achen, Christopher H. 1982. Interpreting and Using Regression. Beverly Hills Sage Publications.
  2. Gujarati, Damodar N. 1998. Essentials of Econometrics. 2nd edition. New York McGraw-Hill, ch.1-4.
  3. 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)

 

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