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Large-Scale Ideal Point Estimation

Published online by Cambridge University Press:  31 March 2021

Michael Peress Open the ORCID record for Michael Peress [Opens in a new window]
Michael Peress*
Affiliation:
The State University of New York, Stony Brook, NY, USA. E-mail: michael.peress@stonybrook.edu
*
Corresponding author Michael Peress
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Abstract

Recent advances in the study of voting behavior and the study of legislatures have relied on ideal point estimation for measuring the preferences of political actors, and increasingly, these applications have involved very large data matrices. This has proved challenging for the widely available approaches. Limitations of existing methods include excessive computation time and excessive memory requirements on large datasets, the inability to efficiently deal with sparse data matrices, inefficient computation of standard errors, and ineffective methods for generating starting values. I develop an approach for estimating multidimensional ideal points in large-scale applications, which overcomes these limitations. I demonstrate my approach by applying it to a number of challenging problems. The methods I develop are implemented in an r package (ipe).

Keywords

ideal point estimationitem response theory
Type
Article
Information
Political Analysis , Volume 30 , Issue 3 , July 2022 , pp. 346 - 363
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

References

Barbera, P. 2015. “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data.” Political Analysis 23:76–91.CrossRefGoogle Scholar
Bonica, A. 2013. “Ideology and Interests in the Political Marketplace.” American Journal of Political Science 57:294–311.CrossRefGoogle Scholar
Bonica, A. 2014. “Mapping the Ideological Marketplace.” American Journal of Political Science 58:367–387.CrossRefGoogle Scholar
Byrd, R. H., Nocedal, J., and Waltz, R. A.. 2006. “KNITRO: An Integrated Package for Nonlinear Optimization.” In Large-Scale Nonlinear Optimization, edited by Di Pillo, G. and Roma, M., 35–59. New York: Springer-Verlag.CrossRefGoogle Scholar
Dennis, J. E., and Schnabel, R. B.. 1983. Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Gill, P. E., Murray, W., and Wright, M. H.. 1981. Practical Optimization. London: Academic Press.Google Scholar
Gill, P. E., Murray, W., and Wright, M. H.. 2002. “SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization.” SIAM Journal of Optimization 12:979–1006.CrossRefGoogle Scholar
Golub, G. H., and Van Loan, C. F.. 1996. Matrix Computations. 3rd edn. Baltimore, MD: Johns Hopkins University Press.Google Scholar
Griewank, A., and Walther, A.. 2008. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. Philadelphia, PA: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Imai, K., Lo, J., and Olmsted, J.. 2016. “Fast Estimation of Ideal Points with Massive Data.” American Political Science Review 110:631–656.CrossRefGoogle Scholar
Jackman, S. 2001. “Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking.” Political Analysis 9:227–241.CrossRefGoogle Scholar
Lewis, J. B., and Tausanovitch, C.. 2018. “gpuideal: Fast Fully Bayesian Estimation of Ideal Points with Massive Data.” Working Paper.Google Scholar
Lewis, J. B., and Poole, K. T.. 2004. “Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap.” Political Analysis 12:105–127.CrossRefGoogle Scholar
Martin, A. D., and Quinn, K. M.. 2002. “Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999.” Political Analysis 10:134–153.CrossRefGoogle Scholar
Nocedal, J., and Wright, S. J.. 2006. Numerical Optimization. New York: Springer-Verlag.Google Scholar
Peress, M. 2013. “Estimating Proposal and Status Quo Locations Using Voting and Cosponsorship Data.” Journal of Politics 75:613–631.CrossRefGoogle Scholar
Peress, M. 2020. “Replication Data for: Large Scale Ideal Point Estimation.” https://doi.org/10.7910/DVN/P5SIO1, Harvard Dataverse, V1.CrossRefGoogle Scholar
Peress, M., and Spirling, A.. 2010. “Scaling the Critics: Uncovering the Latent Dimensions of Movie Criticism.” Journal of the American Statistical Association 105:71–83.CrossRefGoogle Scholar
Poole, K. T., and Rosenthal, H.. 1991. “Patterns of Congressional Voting.” American Journal of Political Science 35:228–278.CrossRefGoogle Scholar
Poole, K. T., and Rosenthal, H.. 1997. Congress: A Political Economic History of Roll Call Voting. New York: Oxford University Press.Google Scholar
Shor, B., and McCarty, N.. 2011. “The Ideological Mapping of American Legislatures.” American Political Science Review 105:530–551.CrossRefGoogle Scholar
Slapin, J. B., and Proksch, S.. 2010. “A Poisson Scaling Model for Estimating Time-Series Party Position from Texts.” American Journal of Political Science 52:705–722.CrossRefGoogle Scholar
Tausanovitch, C., and Warshaw, C.. 2013. “Measuring Constituent Policy Preferences in Congress, State Legislatures, and Cities.” Journal of Politics 75:330–342.CrossRefGoogle Scholar
Supplementary material: Link

Peress Dataset

Dataset

https://doi.org/10.7910/DVN/P5SIO1
Link
Supplementary material: PDF

Peress supplementary material

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