Nonparametric Ideal-Point Estimation and Inference

2018 ◽  
Vol 26 (2) ◽  
pp. 131-146 ◽  
Author(s):  
Alexander Tahk
Keyword(s):  
Supreme Court ◽  
Voting Behavior ◽  
Ideal Point ◽  
Point Estimation ◽  
The Other ◽  
Hypothesis Tests ◽  
Consistent Estimation ◽  
Nonparametric Approach ◽  
Ideal Points ◽  
Ideal Point Estimation

Existing approaches to estimating ideal points offer no method for consistent estimation or inference without relying on strong parametric assumptions. In this paper, I introduce a nonparametric approach to ideal-point estimation and inference that goes beyond these limitations. I show that some inferences about the relative positions of two pairs of legislators can be made with minimal assumptions. This information can be combined across different possible choices of the pairs to provide estimates and perform hypothesis tests for all legislators without additional assumptions. I demonstrate the usefulness of these methods in two applications to Supreme Court data, one testing for ideological movement by a single justice and the other testing for multidimensional voting behavior in different decades.

Monetary Policy Committees and Voting Behavior

2019 ◽  
pp. 38-58
Author(s):  
Sylvester Eijffinger ◽  
Ronald Mahieu ◽  
Louis Raes
Keyword(s):  
Monetary Policy ◽  
Voting Behavior ◽  
Ideal Point ◽  
Point Estimation ◽  
Point Estimates ◽  
Monetary Policy Committees ◽  
Using Data ◽  
Traditional Approaches ◽  
Ideal Point Estimation

In this chapter we suggest to use Bayesian ideal point estimation to analyze voting in monetary policy committees. Using data from the Riksbank we demonstrate what this entails and we compare ideal point estimates with the results from traditional approaches. We end by suggesting possible extensions.

A Multinomial Framework for Ideal Point Estimation

2018 ◽  
Vol 27 (1) ◽  
pp. 69-89 ◽  
Author(s):  
Max Goplerud
Keyword(s):  
Em Algorithm ◽  
Ideal Point ◽  
Network Models ◽  
Point Estimation ◽  
National Election Study ◽  
Special Cases ◽  
Recent Developments ◽  
Conservative Beliefs ◽  
Survey Responses ◽  
Ideal Point Estimation

This paper creates a multinomial framework for ideal point estimation (mIRT) using recent developments in Bayesian statistics. The core model relies on a flexible multinomial specification that includes most common models in political science as “special cases.” I show that popular extensions (e.g., dynamic smoothing, inclusion of covariates, and network models) can be easily incorporated whilst maintaining the ability to estimate a model using a Gibbs Sampler or exact EM algorithm. By showing that these models can be written and estimated using a shared framework, the paper aims to reduce the proliferation of bespoke ideal point models as well as extend the ability of applied researchers to estimate models quickly using the EM algorithm. I apply this framework to a thorny question in scaling survey responses—the treatment of nonresponse. Focusing on the American National Election Study (ANES), I suggest that a simple but principled solution is to treat questions as multinomial where nonresponse is a distinct (modeled) category. The exploratory results suggest that certain questions tend to attract many more invalid answers and that many of these questions (particularly when signaling out particular social groups for evaluation) are masking noncentrist (typically conservative) beliefs.

Small Chamber Ideal Point Estimation

2009 ◽  
Vol 17 (3) ◽  
pp. 276-290 ◽  
Author(s):  
Michael Peress
Keyword(s):  
Supreme Court ◽  
Latin American ◽  
Ideal Point ◽  
Point Estimation ◽  
Multidimensional Space ◽  
Incidental Parameters ◽  
Ideal Points ◽  
Point Estimators ◽  
The Ideal ◽  
Ideal Point Estimation

Ideal point estimation is a topic of central importance in political science. Published work relying on the ideal point estimates of Poole and Rosenthal for the U.S. Congress is too numerous to list. Recent work has applied ideal point estimation to the state legislatures, Latin American chambers, the Supreme Court, and many other chambers. Although most existing ideal point estimators perform well when the number of voters and the number of bills is large, some important applications involve small chambers. We develop an estimator that does not suffer from the incidental parameters problem and, hence, can be used to estimate ideal points in small chambers. Our Monte Carlo experiments show that our estimator offers an improvement over conventional estimators for small chambers. We apply our estimator to estimate the ideal points of Supreme Court justices in a multidimensional space.

Large-Scale Ideal Point Estimation

2021 ◽  
pp. 1-18
Author(s):  
Michael Peress
Keyword(s):  
Voting Behavior ◽  
Large Scale ◽  
Ideal Point ◽  
Computation Time ◽  
Large Data ◽  
R Package ◽  
Point Estimation ◽  
Political Actors ◽  
Ideal Point Estimation ◽  
Data Matrices

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).

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