Large-Scale Ideal Point Estimation

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).

Monetary Policy Committees and Voting Behavior

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

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.

Nonparametric Ideal-Point Estimation and Inference

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.

Modeling Preferences Using Roll Call Votes in Parliamentary Systems

Models of ideal point estimation usually build on the assumption of spatial preferences. This ignores legislators' non-policy incentives and is thus likely to produce implausible results for many legislatures. We study this problem in parliamentary systems and develop a model of roll call voting that considers both thepolicyand the non-policy,tacticalincentives of legislators. We go on to show how the relative weight of these policy and tactical incentives is influenced by the identity of the mover and characteristics of the motion. Analyses of two data sets of 2174 roll call votes in German state legislatures and 3295 roll call votes in the British House of Commons result in three main findings. First, we show that tactical incentives may be more important than policy incentives, and second, that the importance of tactical incentives varies with the importance of motions. Third, there are interesting twists: backbench private members' bills may reverse tactical incentives whereas proposals from anti-system parties are virtually always rejected by moderate parties, rendering these votes uninformative. Our findings have implications for ideal point estimation in parliamentary systems, as well as for research on separation of power systems.