Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-12T13:25:19.316Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimating Legislators' Preferred Points

Published online by Cambridge University Press:  04 January 2017

John Londregan
John Londregan*
Affiliation:
University of California, Los Angeles
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.

This paper shows that agnostic spatial models that simultaneously attempt to estimate legislators' preferred points and ideological locations for the proposals on which they vote, such as the well-known NOMINATE model of Poole and Rosenthal, are not identified. The problem arises because the agnostic estimators inherit the granularity of the voting data and, so, cannot recapture the underlying continuous parameter space. I propose an alternative estimator that achieves identification by modeling the agenda.

Information

Type
Research Article
Information
Political Analysis , Volume 8 , Issue 1 , 1999, pp. 35 - 56
Copyright
Copyright © 1999 by the Society for Political Methodology 

References

Birnbaum, A. 1968. “Some Latent Trait Models and Their Use in Inferring an Examinee's Ability.” In Statistical Theories of Mental Test Scores, eds. Lord, F. M. and Novick, M. R. Reading, MA: Addison Wesley.Google Scholar
Brady, Henry. 1991. “Traits Versus Issues: Factor Versus Ideal-Point Analysis of Candidate Thermometer Ratings.” Political Analysis 2: 97–129.CrossRefGoogle Scholar
Cox, D. R., and Hinkely, D. V. 1974. Theoretical Statistics. New York: Chapman and Hall.CrossRefGoogle Scholar
Fischer, Gerhard H. 1995. “Derivations of the Rasch Model.” In Rasch Models: Foundations, Recent Developments and Applications, ed. Fischer, Gerhard H. New York: Springer Verlag.Google Scholar
Haberman, S. J. 1977. “Maximum Likelihood Estimates in Exponential Response Models.” Annals of Statistics 5: 815–841.Google Scholar
Heckman, James J., and Snyder, James. 1996. “Linear Factor Models of the Demand for Attributes with an Application to Estimating the Preferences of Legislators,” Department of Economics, University of Chicago. Unpublished paper.Google Scholar
Hinich, Melvin J., Cahoon, Lawerence, and Ordeshook, Peter C. 1978. “A Statistical Multidimensional Scaling Method Based on the Spatial Theory of Voting.” In Graphical Representation of Multivariate Data, ed. Wang, P C. New York: Harwood, pp. 243–279.Google Scholar
Ladha, Krishna. 1991. “A Spatial Model of Legislative Voting with Perceptual Error.” Public Choice 68: 151–174.Google Scholar
Londregan, John B. 2000. Legislative Institutions and Ideology in Chile's Democratic Transition. New York: Cambridge University Press.Google Scholar
Lord, Frederic M. 1983. “Unbiased Estimates of Ability Parameters, of Their Variance, and of Their Parallel Forms Reliability.” Psychometrika 48: 477–482.CrossRefGoogle Scholar
Maddala, G. S. 1986. Limited Dependent and Qualitative Variables in Econometrics. New York: Cambridge.Google Scholar
Marriott, F. H. C., and Pope, J. A. 1954. “Bias in the Estimation of Autocorrelations.” Biometrika 41: 390–402.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1991. “Patterns of Congressional Voting.” American Journal of Political Science 35: 228–278.CrossRefGoogle Scholar
Poole, Keith T., and Rosenthal, Howard. 1996. Congress: A Political-Economic History of Roll Call Voting. New York: Oxford University Press.Google Scholar
Rasch, Georg. 1961. “On General Laws and the Meaning of Measurement in Psychology.” Proceedings of the IV Berkeley Symposium on Mathematical Statistics and Probability 4: 321–333.Google Scholar
Stokes, Donald. 1963. “Spatial Models of Party Competition.” American Political Science Review 57: 368–377.CrossRefGoogle Scholar
Thurstone, L. L. 1925. “A Method of Scaling Psychological and Educational Tests.” Journal of Educational Psychology 16: 433–451.Google Scholar