ECPR Winter School
University of Bamberg, Bamberg
3 - 10 March 2017

WD206 - Advanced Discrete Choice Modelling

Instructor Details

Instructor Photo

Paul Thurner

Ludwig-Maximilians-Universität München – LMU

Instructor Bio

Paul W. Thurner is Professor of Political Science at the Geschwister-Scholl-Institut für Politikwissenschaft, Ludwigs-Maximilians-Universität München (LMU), where he directs the Chair for Empirical Political Science and Policy Analysis.

His main research interest is the statistical analysis of political choice behaviour (electoral choices, strategies of negotiation, network choices).

For more than fifteen years he has provided knowledge transfer workshops on Applied Discrete Choice Modelling (Conditional Logit, Nested Logit, Random Utility Models, Mixed Logit) and on Applied Political Network Analysis.

Course Dates and Times

Monday 6 to Friday 10 March 2017
Generally classes are either 09:00-12:30 or 14:00-17:30
15 hours over 5 days

Prerequisite Knowledge

Basic knowledge of methodology and statistics is required. Be able to use Stata and to perform linear regression and binary logit. See, e.g., Kohler/Kreuter (2012): Data Analysis Using Stata, Third Edition. Stata Press.

Short Outline

This course introduces to discrete choice modelling using appropriate statistical software packages. Discrete choice models are meanwhile the work-horse in consumer research, transportation economics, and in electoral research. Starting with binary as well as multinomial logit and probit models, the core objective of this course is the application of so-called conditional logit models where both attributes of the alternatives and characteristics of decision makers are taken into account. We provide a formal as well as an intuitive outline of the models. Special attention will be given to the verbal interpretation and visualization of ‘Quantities of Interest’ (e.g., conditional choice probabilities, (cross/direct) elasticities, willingness to accept, etc.). Main application cases are electoral choices in multi-party systems. Easy-to-imitate routines will be provided by a wide range of exercises.

Long Course Outline

Discrete choice models are meanwhile the work horse in consumer research, transportation economics, and in electoral research. Conditional logit and probit models allow the researcher to use attributes of the choice alternatives as independent variables. As applied to electoral research, we are now able to specify candidates’ and parties’ policy positions and other attributes – beyond the usually used attributes of voters (sex, age, income etc). A major advantage of the underlying theory of these models is that it systematically connects demand side and supply side of electoral competition. A prominent example in electoral research is the position-taking efforts of parties/candidates on issues. In the context of the Downsian spatial theory of voting it is assumed that voters react to these positions by calculating the distance of their own position to the parties’ position. Comparing over all distances to all parties the voters are conceived to choose the party with the least distance. In the probabilistic setting of the discrete choice models, we can reformulate this expectation as follows: the lower the distance of voter i to a party j’s position in an issue k, the higher the probability that the voter chooses this party. Thus, contrary to case-specific variables like sex and age which are constant for a case, these variables vary across choice alternatives. We call them, therefore, alternative-specific variables.

When attributes of the alternatives are part of the theoretical model, theorizing as well as data handling and specification are more complex. On the theoretical side, we will outline the underlying random utility approach in order to capitalize on the enormous flexibility of these models. Their power lies especially in the consistent decision-theoretical foundation of the statistical model. Therefore, we familiarize attendants with the respective terminology and assumptions. We demonstrate how to derive substantially interesting research questions and hypotheses from this approach for electoral research in general, and for the spatial theory of voting more in particular. E.g., it will be shown how issue and / or idelogical distances (left-right dimensions) can be specified in different ways: issue-by-issue, quadratic or non-quadratic, city-block, euclidean, saliency-weighted or unweighted etc. We will also highlight features of these models so far rather neglected by the electoral research community – e.g. nested multinomial logit models, specification of alternative-specific issue and ideological distances etc. Finally, we will give an outlook to the potentials of advanced models like random coefficient (mixed logit) and rank-ordered logit models (for cases where respondents indicate a rank-order of alternatives).

As data handling is more demanding, we will spend sufficient time for acquainting participants with setting up data in the required way and to program their own hypotheses. Starting with binary logit and probit in a rather short warm-up part, we quickly turn to multinomial regression (MNL) considering individual voter attributes only. An in-depth understanding of the MNL is a prerequisite of the conditional logit model. We will provide a formal as well as an intuitive outline of these models. During the whole week, lectures and exercises in the lab will be combined. We demonstrate the application of these models mainly with Stata 12 or 13 . Therefore, Long/Freese (2014) is a good starting point for the preparation of the exercise sessions – but will be expanded substantially by using new features of Stata 13 and 14 (e.g., margins, marginsplot). Special attention will be given to the verbal interpretation and visualization of ‘Quantities of Interest’ (e.g., conditional choice probabilities, (cross/direct) elasticitiesetc.). Easy-to-imitate routines will be provided by a wide range of exercises. In the last days we also demonstrate how to specify discrete choice models with R and Limdep/Nlogit.

Basic knowledge of regression analysis and Stata is required. Participants are suggested to use their own laptops with the suggested software. Additionally, there will be extensive opportunities for students to present, discuss and develop their own projects and data sets.

Day-to-Day Schedule

1Introduction to Discrete Choice Modeling and Application CaseLecture / Introduction to Data Sets and Software
2Random Utility Theory and the Logit and Probit Model (Binary and Multinomial)Lecture: Model Formulations, Methods of Interpretation Lab:Exercises, Stata Applications
3Conditional Logit Model I Lecture: Model Formulation, Data Setup, Data Management in Stata, etc. Lab:Exercises, Stata Applications
4Conditional Logit Model II Lecture: Methods of Interpretation, Model Assumptions and Implications Lab: Exercises, Stata Applications
5Special Features: Nested and Mixed Logit, Alternative-Specific Attribute Reactions, Rank-Ordered Logit etc. Lecture: Discussion of Model Extensions Lab:Exercises, Applications in R (and Limdep/Nlogit), etc.
Day-to-Day Reading List

1Train 2009: Chapter 1 + 2, Davis/Hinich/Ordeshook 1970, , Alvarez/Nagler 1998, Thurner 2000
2Long / Freese, 2014: Chapter 3- 6, Stata Manual: ‘Margins’
3Long / Freese 2014: Chapter 8, Thurner/Eymann 2000, King et al. 2000, Stata Manual: ‘Margins’, McFadden 1974, Alvarez/Nagler 1998, Dow/ Endersby 2004
4Long/Freese 2014: Chapter 8 [2006: Chapter 7], Thurner 2000, Adams/Merrill/Grofman 2005, Mauerer/Thurner/Debus 2015, Stata Manual: ‘Margins’
5Glasgow 2001, Heiss 2002, Shikano/Herrmann/Thurner 2009,
Software Requirements

Stata 12 or 13, R

Hardware Requirements



Adams, James, Samuel Merrill, Bernard Grofman, 2005: A unified theory of party competition. A cross-national analysis integrating spatial and behavioral factors. Cambridge: Cambridge Univ. Press.

Agresti, Alan, 2002: Categorical data analysis. 2. ed., New York: Wiley-Interscience.

Alvarez, R. Michael, Jonathan Nagler, 1998: When politics and models collide: Estimating models of multiparty elections. American Journal of Political Science 42(1): 55-96.

Borooah, Vani K., 2002: Logit and Probit. Ordered and Multinomial Models. Thousand Oaks: Sage. (QASS 138).

Davis, O., M.J. Hinich, P.C. Ordeshook, 1970: An expository development of a mathematical model of the electoral process. American Political Science Review 64: 426–448.

Dow, J.K., J.W. Endersby, 2004: Multinomial probit and multinomial logit: a comparison of choice models for voting research. Electoral Studies 23 (1): 107-122.

Downs, A., 1957. An Economic Theory of Democracy. Harper & Row, New York.

Glasgow, Garrett, 2001: Mixed logit models for multiparty elections. Political Analysis 9 (2): 116-136.

Greene, William H., 2008: Econometric Analysis. 6. ed., Upper Sadle River, NJ: Prentice Hall.

Greene, William H., David A. Hensher, 2010: Modeling Ordered Choices. A Primer. Cambridge / New York: Cambridge University Press.

Heiss, Florian, 2002: Structural choice analysis with nested logit models. Stata Journal 2(3): 227-252.

Hensher, David A., John M. Rose, William H. Greene, 2005: Applied Choice Analysis. A Primer. Cambridge: Cambridge University Press.

King,G., M. Tomz, J. Wittenberg, 2000: Making the most of statistical analyses: Improving interpretation and presentation. American Journal of Political Science 44: 347–61.

Long, J. Scott, Jeremy Freese, 2006: Regression Models for Categorical Dependent Variables Using Stata, Second Edition. Stata Press.

Long, J. Scott, Jeremy Freese, 2014: Regression Models for Categorical Dependent Variables Using Stata, Third Edition. Stata Press.

Louviere, Jordan J., David A. Hensher, Joffre D. Swait, 2000: Stated Choice Methods. Analysis and Applications. Cambridge / New York: Cambridge University Press.

Manski, C.F., 1977: The structure of random utility models. Theory and Decision 8, 229–254.

McFadden, D., 1974. Conditional logit analysis of qualitative choice behaviour. In: Zarembka, P. (Ed.) Frontiers in Econometrics. Academic Press, New York.

Mauerer, Ingrid, Thurner, Paul W., and Marc Debus, 2015: Under Which Conditions do Parties Attract Voters’ Reactions to Issues? Party Varying Issue Voting in German Elections 1987-2009. West European Politics, 38(6): 1251-1273. doi: 10.1080/01402382.2015.1026562

McFadden, D., K. Train. 2000. Mixed MNL models of discrete response. Journal of Applied Econometrics 15:447–470.

McFadden, D., 1981: Econometric models of probabilistic choice. In: Manski, C., McFadden, D. (Eds.): Structural Analysis of Discrete Data. MIT Press, Cambridge, MA.

McFadden, D., 1974. Conditional logit analysis of qualitative choice behaviour. In: Zarembka, P. (Ed.) Frontiers in Econometrics. Academic Press, New York.

Shikano, Suzumu, Michael Herrmann, Paul W. Thurner, 2009: Strategic voting under proportional representation: Threshold insurance in German Elections. West European Politics 32,3: 630-652.

Singh, Shane, 2014: Linear and Quadratic Utility Loss Functions in Voting Behavior Research, Journal of Theoretical Politics, 26:1, 35-58.

Thurner, Paul W., 1998: Wählen als rationale Entscheidung. Die Modellierung von Politikreaktionen im Mehrparteiensystem. Munich: Oldenbourg. (Scientia Nova).

Thurner, Paul W., 2000: The empirical application of the spatial theory of voting in Multiparty Systems with Random Utility Models. Electoral Studies 19, 4: 493-517.

Thurner, Paul W., Angelika Eymann, 2000: Policy-Specific alienation and indifference in the calculus of voting: A simultaneous model of party choice and abstention. Public Choice 102: 51-77.

Train, Kenneth, 2009: Discrete Choice Methods with Simulation. Cambridge / New York: Cambridge University Press.

Tutz, Gerhard, 2012: Regression for Categorical Data. Cambridge / New York: Cambridge University Press.

The following other ECPR Methods School courses could be useful in combination with this one in a ‘training track .
Recommended Courses Before

Summer School Introduction to STATA Training Course Advanced Topics in Applied Regression: B08, two-week course with Levi Littvay Basics of Inferential Statistics for Political Scientists with Janez Stare Introduction to Generalised Linear Models Advanced Topics in Applied Regression: Modelling Issues

Additional Information


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed in due time.

Note from the Academic Convenors

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.

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