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Maximum Likelihood Estimation of Models with Beta-Distributed Dependent Variables

Published online by Cambridge University Press:  04 January 2017

Philip Paolino
Philip Paolino*
Affiliation:
Department of Government, The University of Texas at Austin, 536 Burdine Hall, Austin, TX 78712-1087. e-mail: [email protected]://www.la.utexas.edu/∼ppaolino
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Abstract

Research in political science is often concerned with modeling dependent variables that are proportions. Proportions are relevant in a wide variety of substantive areas, including elections, the bureaucracy, and interest groups. Yet because most researchers rely upon an approach, OLS, that does not recognize key aspects of proportions, the conclusions we reach from normal models may not provide the best understanding of phenomena of interest in these areas. In this paper, I use Monte Carlo simulations to show that maximum likelihood estimation of these data using the beta distribution may provide more accurate and more precise results. I then present empirical analyses illustrating some of these differences.

Type
Research Article
Information
Political Analysis , Volume 9 , Issue 4 , 2001 , pp. 325 - 346
Copyright
Copyright © 2001 by the Society for Political Methodology 

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References

Achen, Christopher H., and Phillips Shively, W. 1995. Cross-Level Inference. Chicago: University of Chicago Press.Google Scholar
Alvarez, R. Michael, and Brehm, John. 1995. “American Ambivalence Towards Abortion Policy: Development of a Heteroskedastic Probit Model of Competing Values.” American Journal of Political Science 39(4): 1026–1055.CrossRefGoogle Scholar
Alvarez, R. Michael, and Brehm, John. 1997. “Are Americans Ambivalent Toward Racial Policies?” American Journal of Political Science 41(2): 345–374.CrossRefGoogle Scholar
Atkeson, Lonna Rae. 1998. “Divisive Primaries and General Election Outcomes: Another Look at Presidential Campaigns.” American Journal of Political Science 42(1): 256–271.CrossRefGoogle Scholar
Beck, Nathaniel, and Katz, Jonathan N. 1995. “What to Do (and Not to Do) with Time-Series-Cross-Section Data in Comparative Politics.” American Political Science Review 89: 634–647.Google Scholar
Brehm, John, and Gates, Scott. 1993. “Donut Shops and Speed Traps: Evaluating Models of Supervision on Police Behavior.” American Journal of Political Science 37(2): 555–581.Google Scholar
Delli, Carpini, Michael, X., and Keeter, Scott. 1996. What American Know About Politics and Why It Matters. New Haven, CT: Yale University Press.Google Scholar
Franklin, Charles H. 1991. “Eschewing Obsfucation? Campaigns and the Perception of U.S. Senate Incumbents.” American Political Science Review 85(4): 1193–1214.Google Scholar
Gentle, James E. 1998. Random Number Generation and Monte Carlo Methods. New York: Springer-Verlag.Google Scholar
Haider-Markel, Donald P., and Meier, Kenneth J. 1996. “The Politics of Gay and Lesbian Rights: Expanding the Scope of the Conflict.” Journal of Politics 58(2): 332–349.CrossRefGoogle Scholar
Johnson, Norman L., Kotz, Samuel, and Balakrishnan, N. 1995. Continuous Univariate Distributions, Vol. 2, 2nd ed. New York: John Wiley and Sons.Google Scholar
Keiser, Lael R., and Soss, Joe. 1998. “With Good Cause: Bureaucratic Discretion and the Politics of Child Support Enforcement.” American Journal of Political Science 42(4): 1133–1156.CrossRefGoogle Scholar
King, Gary, Tomz, Michael, and Wittenberg, Jason. 2000. “Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44(2): 347–361.Google Scholar
Lowery, David, and Gray, Virginia. 1998. “The Dominance of Institutions in Interest Representation: A Test of Seven Explanations.” American Journal of Political Science 42(1): 231–255.Google Scholar
Mebane, Walter R. Jr. 2000. “Coordination, Moderation, and Institutional Balancing in American Presidential and House Elections.” American Political Science Review 94(1): 37–58.Google Scholar
Palmquist, Bradley. 1999. “Analysis of Proportions Data.” Paper presented at the 1999 Annual Meeting of the Political Methodology Society, College Station, TX.Google Scholar
Paolino, Philip. 1998. “Voters’ Perceptions of Candidate Viability: Uncertainty and the Prospects for Momentum.” Prepared for presentation at the 1998 Annual Meeting of the Midwest Political Science Association.Google Scholar
Schram, Stanford, Nitz, Lawrence, and Krueger, Gary. 1998. “Without Cause or Effect: Reconsidering Welfare Migration as a Policy Problem.” American Journal of Political Science 42(1): 210–231.Google Scholar
Selden, Sally Coleman, Brudney, Jeffrey L., and Edward Kellough, J. 1998. “Bureaucracy as a Representative Institution: Toward a Reconciliation of Bureaucratic Government and Democratic Theory.” American Journal of Political Science 42(3): 717–744.Google Scholar