Hostname: page-component-76d6cb85b7-xh428 Total loading time: 0 Render date: 2026-07-20T00:40:35.212Z Has data issue: false hasContentIssue false
  • English
  • Français

Postestimation Uncertainty in Limited Dependent Variable Models

Published online by Cambridge University Press:  04 January 2017

Michael C. Herron
Michael C. Herron*
Affiliation:
Northwestern University
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Many political science research articles that use limited dependent variable models report estimated quantities, in particular, fitted probabilities, predicted probabilities, and functions of such probabilities, without indicating that such estimates are subject to uncertainty. This practice, along with the reporting of “percentage correctly predicted,” can overstate the precision of reported results. In light of this, the present article describes a variety of measures of uncertainty that authors can include alongside estimates generated by limited dependent variable models. It also proposes an alternative to “percentage correctly predicted” and illustrates its calculations with congressional cosponsorship data from Krehbiel (1995).

Information

Type
Research Article
Information
Political Analysis , Volume 8 , Issue 1 , 1999 , pp. 83 - 98
Copyright
Copyright © 1999 by the Society for Political Methodology 

References

Agresti, Alan. 1996. An Introduction to Categorical Data Analysis. New York: John Wiley & Sons.Google Scholar
Alvarez, R. Michael, and Brehm, John. 1995. “American Ambivalence Toward Abortion Policy: A Heteroskedastic Probit Method for Assessing Conflicting Values.” American Journal of Political Science 39: 1055–1082.Google Scholar
Alvarez, R. Michael, and Brehm, John. 1998. “Speaking in Two Voices: American Equivocation about the Internal Revenue Service.” American Journal of Political Science 42: 418–452.Google Scholar
Alvarez, R. Michael, and Nagler, Jonathan. 1995. “Economics, Issues, and the Perot Candidacy: Voter Choice in the 1992 Presidential Election.” American Journal of Political Science 39: 714–744.CrossRefGoogle Scholar
Alvarez, R. Michael, and Nagler, Jonathan. 1997. “When Politics and Models Collide: Estimating Models of Multiparty Elections.” American Journal of Political Science 42: 55–96.Google Scholar
Bailey, Michael, and Brady, David W. 1998. “Heterogeneity and Representation: The Senate and Free Trade.” American Journal of Political Science 42: 524–544.Google Scholar
Binder, Sarah A., Lawrence, Eric D., and Maltzman, Forrest. 1999. “Uncovering the Hidden Effect of Party.” Journal of Politics 61: 815–831.CrossRefGoogle Scholar
Greene, William H. 1997. Econometric Analysis, 3rd ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Hagle, Timothy M., and Mitchell, Glen E. II. 1992. “Goodness-of-Fit Measures for Probit and Logit.” American Journal of Political Science 36: 762–784.Google Scholar
Katz, Jonathan N., and King, Gary. 1997. “A Statistical Model for Multiparty Electoral Data.” California Institute of Technology, Social Science Working Paper 1005.Google Scholar
King, Gary, Tomz, Michael, and Wittenberg, Jason. 1998. “Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” Paper presented at the annual meeting of the American Political Science Association, Boston.Google Scholar
Krehbiel, Keith. 1995. “Cosponsors and Wafflers from Ato Z.” American Journal of Political Science 39: 906–923.Google Scholar
Krehbiel, Keith. 1999. “The Party Effect from A-to-Z and Beyond.” Journal of Politics 61: 832–840.Google Scholar
Lacy, Dean, and Burden, Barry C. 1999. “The Vote-Stealing and Turnout Effects of Ross Perot in the 1992 U.S. Presidential Election.” American Journal of Political Science 43: 233–255.Google Scholar
Maddala, G. S. 1983. Limited-Dependent and Qualitative Variables in Econometrics. New York: Cambridge University Press.Google Scholar
McCullagh, P., and Nelder, J. A. 1989. Generalized Linear Models, 2nd ed. Cambridge: Chapman and Hall.Google Scholar
McFadden, Daniel. 1974. “The Measurement of Urban Travel Demand.” Journal of Public Economics 3: 303–328.Google Scholar
Menard, Scott. 1995. Applied Logistic Regression Analysis, Quantitative Applications in the Social Sciences, No. 106. Thousand Oaks, CA: Sage.Google Scholar
Rice, John A. 1988. Mathematical Statistics and Data Analysis. Belmont, CA: Wadsworth & Brooks.Google Scholar
Tanner, Martin A. 1993. Tools for Statistical Inference, 2nd ed. New York: Springer-Verlag.Google Scholar
Train, Kenneth E. 1986. Qualitative Choice Analysis: Theory, Econometrics, and an Application to Automobile Demand. Boston: MIT Press.Google Scholar