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Mixed Logit Models for Multiparty Elections

Published online by Cambridge University Press:  04 January 2017

Garrett Glasgow*
Affiliation:
Department of Political Science, University of California, Santa Barbara, Santa Barbara, CA 93106. e-mail: [email protected]://www.polsci.ucsb.edu/faculty/glasgow

Abstract

Mixed logit (MXL) is a general discrete choice model thus far unexamined in the study of multicandidate and multiparty elections. Mixed logit assumes that the unobserved portions of utility are a mixture of an IID extreme value term and another multivariate distribution selected by the researcher. This general specification allows MXL to avoid imposing the independence of irrelevant alternatives (IIA) property on the choice probabilities. Further, MXL is a flexible tool for examining heterogeneity in voter behavior through random-coefficients specifications. MXL is a more general discrete choice model than multinomial probit (MNP) in several respects, and can be applied to a wider variety of questions about voting behavior than MNP. An empirical example using data from the 1987 British General Election demonstrates the utility of MXL in the study of multicandidate and multiparty elections.

Type
Research Article
Copyright
Copyright © 2001 by the Society for Political Methodology 

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References

Algers, S., Bergström, P., Dalhberg, M., and Dillén, J. L. (1998). “Mixed Logit Estimation of the Value of Travel Time,” Unpublished manuscript.Google Scholar
Alvarez, R. M., and Nagler, J. (1995). “Economics, Issues, and the Perot Candidacy: Voter Choice in the 1992 Presidential Election.” American Journal of Political Science 39(3): 714744.Google Scholar
Alvarez, R. M., and Nagler, J. (1998a). “When Politics and Models Collide: Estimating Models of Multiparty Elections.” American Journal of Political Science 42(1): 5596.Google Scholar
10Prime numbers are used to define Halton sequences, since the Halton sequence for a nonprime number will divide the unit interval in the same way as the Halton sequences based on the prime numbers that constitute the nonprime number.Google Scholar
Alvarez, R.M., and Nagler, J. (1998b). “Economics, Entitlements, and Social Issues: Voter Choice in the 1996 Presidential Election.” American Journal of Political Science 42(4): 13491363.CrossRefGoogle Scholar
Alvarez, R. M., Bowler, S., and Nagler, J. (2000). “Issues, Economics, and the Dynamics of Multi-Party Elections: The British 1987 General Election.” American Political Science Review 94(1): 131149.Google Scholar
Bhat, C. R. (1998a). “Accommodating Variations in Responsiveness to Level-of-Service Measures in Travel Mode Choice Modeling.” Transportation Research A 32(7): 495507.Google Scholar
Bhat, C. R. (1998b). “Accommodating Flexible Substitution Patterns in Multi-Dimensional Choice Modeling: Formulation and Application to Travel Mode and Departure Time Choice.” Transportation Research B 32(7): 455466.CrossRefGoogle Scholar
Bhat, C. R. (1999). “Quasi-Random Maximum Simulated Likelihood Estimation of the Mixed Multinomial Logit Model.” Transportation Research (in press).Google Scholar
Brownstone, D., and Train, K. (1999). “Forecasting New Product Penetration with Flexible Substitution Patterns.” Journal of Econometrics 89(1): 109129.Google Scholar
Crewe, I. (1992). “The 1987 General Election.” In Issues and Controversies in British Electoral Behavior, eds. Denver, D. and Hands, G. London: Harvester Wheatsheaf, pp. 343354.Google Scholar
Crewe, I. (1983). “The Electorate: Partisan Dealignment Ten Years On.” West European Politics 6(4): 183215.Google Scholar
Garrett, G. (1992). “The Political Consequences of Thatcherism.” Political Behavior 14(4): 361382.Google Scholar
Hajivassiliou, V. A., and Ruud, P. A. (1994). “Classical Estimation Methods for LDV Models Using Simulation.” In Handbook of Econometrics, Vol. 4. eds. Engle, R. F. and McFadden, D. L. New York: North Holland, pp. 23832441.Google Scholar
Hausman, J. A., and Wise, D. A. (1978). “A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences.” Econometrica 46(2): 403426.Google Scholar
Heath, A. F., Jowell, R. M., and Curtice, J. K. (1985). How Britain Votes. New York: Pergamon Press.Google Scholar
Heath, A. F., Jowell, R. M., and Curtice, J. K. (1989). British Election Study, 1987. A Computer File. Colchester: ESRC Data Archive.Google Scholar
Horowitz, J. L. (1991). “Reconsidering the Multinomial Probit Model.” Transportation Research B 25(6): 433438.Google Scholar
Jain, D. C., Vilcassim, N. J., and Chintagunta, P. K. (1994). “A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data.” Journal of Business and Economic Statistics 12(3): 317328.Google Scholar
Lacy, D., and Burden, B. (1999). “The Vote-Stealing and Turnout Effects of Ross Perot in the 1992 U.S. Presidential Election.” American Journal of Political Science 43(1): 233255.CrossRefGoogle Scholar
Lacy, D., and Burden, B. (2000). “The Vote-Stealing and Turnout Effects of Third-Party Candidates in U.S. Presidential Elections, 1968–1996,” Unpublished manuscript.Google Scholar
Lawrence, E. D. (1997). “Simulated Maximum Likelihood via the GHK Simulator: An Application to the 1988 Democratic Super Tuesday Primary,” Unpublished manuscript.Google Scholar
Lee, L. (1992). “On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Choice Models.” Econometric Theory 8: 518552.CrossRefGoogle Scholar
Maddala, G. S. (1983). Limited-Dependent and Qualitative Variables in Econometrics. New York: Cambridge University Press.Google Scholar
McFadden, D. (1984). “Econometric Analysis of Qualitative Response Models.” In Handbook of Econometrics, II, eds. Griliches, Z. and Intriligator, M. Amsterdam: North-Holland, pp. 13951457.Google Scholar
McFadden, D., and Train, K. (2000). “Mixed MNL Models for Discrete Response.” Journal of Applied Econometrics (in press).3.0.CO;2-1>CrossRefGoogle Scholar
Pulzer, P. G. (1967). Political Representation and Elections in Britain. London: Allen and Unwin.Google Scholar
Quinn, K. M., Martin, A. D., and Whitford, A. B. (1999). “Voter Choice in a Multi-Party Democracy: A Test of Competing Theories and Models.” American Journal of Political Science 43(4): 12311247.CrossRefGoogle Scholar
Revelt, D., and Train, K. (1998). “Mixed Logit with Repeated Choices: Households’ Choices of Appliance Efficiency Level.” The Review of Economics and Statistics 80(4): 647657.Google Scholar
Rivers, D. (1988). “Heterogeneity in Models of Electoral Choice.” American Journal of Political Science 32(3): 737757.Google Scholar
Sarlvik, B., and Crewe, I. (1983). Decade of Dealignment: The Conservative Victory of 1979 and Electoral Trends in the 1970s. Cambridge: Cambridge University Press.Google Scholar
Schofield, N., Martin, A. D., Quinn, K. M., and Whitford, A. B. (1998). “Multiparty Electoral Competition in The Netherlands and Germany: A Model Based on Multinomial Probit.” Public Choice 97(3): 257293.Google Scholar
Train, K. (1998). “Recreation Demand Models with Taste Differences over People.” Land Economics 74(2): 230239.Google Scholar
Train, K. (1999). “Halton Sequences for Mixed Logit,” Unpublished manuscript.Google Scholar