scholarly journals Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999

2002 ◽  
Vol 10 (2) ◽  
pp. 134-153 ◽  
Author(s):  
Andrew D. Martin ◽  
Kevin M. Quinn
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Supreme Court ◽  
Ideal Point ◽  
Point Estimation ◽  
Judicial Behavior ◽  
Preference Change ◽  
Ideal Points ◽  
The U.S

At the heart of attitudinal and strategic explanations of judicial behavior is the assumption that justices have policy preferences. In this paper we employ Markov chain Monte Carlo methods to fit a Bayesian measurement model of ideal points for all justices serving on the U.S. Supreme Court from 1953 through 1999. We are particularly interested in determining to what extent ideal points of justices change throughout their tenure on the Court. This is important because judicial politics scholars oftentimes invoke preference measures that are time invariant. To investigate preference change, we posit a dynamic item response model that allows ideal points to change systematically over time. Additionally, we introduce Bayesian methods for fitting multivariate dynamic linear models to political scientists. Our results suggest that many justices do not have temporally constant ideal points. Moreover, our ideal point estimates outperform existing measures and explain judicial behavior quite well across civil rights, civil liberties, economics, and federalism cases.

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.

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.

A Recommendation System for Scientific Papers through Bayesian Nonparametric Hybrid Filtering

2014 ◽  
pp. 20-41
Author(s):  
Abel Rodriguez ◽  
Radhakrishna Vuppala
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Topic Model ◽  
User Preferences ◽  
Point Estimation ◽  
Relational Model ◽  
Bayesian Nonparametric ◽  
Homogeneous Groups ◽  
Scientific Papers

Recommender systems have become an important area of research with numerous applications on e-commerce. This chapter introduces a joint statistical model for user preferences and item features that can serve as the basis for a recommendation about recently published scientific papers. The model is constructed using ideas from the literature on Bayesian nonparametric mixture modeling. More specifically, user preferences are modeled using an Infinite Relational Model (IRM) in which both users and items are independently partitioned into homogeneous groups, while item features are modeled using a topic model, which also partitions items into groups with homogenous features. Information is shared across both components of the model through a common partition of items. Hence, the model is a hybrid system that combines ideas from collaborative and content-based filtering. The chapter discusses three different computational strategies, including a Markov chain Monte Carlo algorithm for full posterior inference, an iterated conditional maximization algorithm, and a mean-field variational algorithm for point estimation and prediction in large datasets where Markov chain Monte Carlo approaches might not be practical. The model is illustrated through simulation studies and by analyzing data from CiteULike.

Ideal Point Estimation with a Small Number of Votes: A Random-Effects Approach

2001 ◽  
Vol 9 (3) ◽  
pp. 192-210 ◽  
Author(s):  
Michael Bailey
Keyword(s):  
Monte Carlo ◽  
Random Effects ◽  
Bayesian Approach ◽  
Fixed Effects ◽  
Ideal Point ◽  
Point Estimation ◽  
Standard Errors ◽  
Fixed Effects Models ◽  
Ideal Points ◽  
Ideal Point Estimation

Many conventional ideal point estimation techniques are inappropriate when only a limited number of votes are available. This paper presents a covariate-based random-effects Bayesian approach that allows scholars to estimate ideal points based on fewer votes than required for fixed-effects models. Using covariates brings more information to bear on the estimation; using a Bayesian random-effects approach avoids incidental parameter problems. Among other things, the method allows us to estimate directly the effect of covariates such as party on preferences and to estimate standard errors for ideal points. Monte Carlo results, an empirical application, and a discussion of further applications demonstrate the usefulness of the method.

On Markov Chain Monte Carlo Acceleration

1994 ◽  
Author(s):  
Alan E. Gelfand ◽  
Sujit K. Sahu
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo
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