It's Not All about the Benjamins: Political Economy and Social-Psychology Theories of Welfare State Preferences (abstract)
Uses two new estimators (see below) that are implemented in my R package, FAiR, to test traditional political economy theories of preferences for redistribution and other welfare-state programs against alternative theories that stem from sociology, psychology, and lab experiments. In short, the alternative theories perform better, and the new methods produce stronger evidence for this conclusion than do standard methodological techniques.

Published Papers

A Comment on "Rewarding Impatience" (pdf) (data) (Stata code) (BibTeX) Published in International Organization 60(2):499-513.
This paper reanalyzes the empirical evidence presented in Lisa Blaydes, 2004, ???Rewarding Impatience: A Bargaining and Enforcement Model of OPEC,??? International Organization 58(2):213???237. The IO published Lisa Blaydes??? response to this critique in the same issue, which is also available on her website.

Public Software

The "stable" version (0.4-x) of my R package to estimate structural equation models with latent variables is available here. The "development" version (0.6-0) is available here. It is mostly functional but some things may not work (correctly) at any particular point in time, and they may or may not correspond to the (lack of) documentation. Some of the working papers depend on functionality in FA i R 0.6-x.

Papers Under Review

Integrating Risk Context into Risk Assessments: The Risk Context Scale with Daryl G. Kroner and Andrew Gray.
This paper uses FA i R (0.4-x) to evaluate a new Risk Context Scale that evaluates recidivism risk of male parolees.
Visual Representation of Distributions of Covariance Matrices with with Andrew Gelman, Tomoki Tokuda, and Francis Tuerlinckx.
This paper develops graphical tools to help researchers understand the (often poorly understood) properties of a distribution of covariance or correlation matrices. For example, when choosing a prior, researchers often use something from the Wishart family. An alternative is to specify a jointly uniform prior over a correlation matrix and some marginal distribution for the standard deviations to form a prior over a covariance matrix. See also my working paper “Generating Correlation Matrices via Canonical Partial Correlations” below.

Working Papers

Bringing Rank-Minimization Back In: An Estimator of the Number of Inputs to a Data-Generating Process (requires FA i R 0.6-0) (pdf) (BibTeX)
This paper derives an algorithm to indirectly successfully solve an optimization problem proposed by Louis Leon Thurstone in the 1930s and worked on by Louis Guttman in the 1950s and various engineers even today. At the optimum, it is possible to infer the number of inputs to the data-generating process of the observed variables and under certain verifiable conditions, to estimate the proportion of each variable that consists of random noise.
Choosing the Number of Latents with the Indirect Rank-Minimization Algorithm (requires FA i R 0.6-0)
This paper is a companion to the previous one that focuses on how best to make finite-sample inferences about the number of latent variables that generated the observed variables. The previous paper proves that the right answer can be found as the sample size goes to infinity, so this paper compares the finite-sample performance of several new and old ways to make this inference.
Inequality Aversion and Preferences for Redistribution: New Tests of Political Economy Theories (pdf)
This paper applies the methods developed in the previous two (and following) papers to cross-country survey-data in order to test theories of preferences for redistribution. It is an article-length statement of the main conclusion of my dissertation, namely that traditional political economy theories that assume a voter's utility depends only on private variable do a demonstrably poor job of explaining the variation in preferences for redistribution within countries.
Generating Correlation Matrices via Canonical Partial Correlations
This paper builds on recent work by Lewandowski, Kurowicka, and Joe (2009), which expresses a correlation matrix as a function of partial correlations. For the “canonical” parameterization, we provide expressions for the Cholesky decomposition of the correlation matrix as a relatively simple function of the partial correlations, which allows for much faster generation of random correlation matrices. Also, the extension to generating random correlation matrices of a given rank is straightforward.
Semi-Exploratory Factor Analysis and Software to Estimate It (pdf) (BibTeX)
This APSA paper describes a new algorithm for estimating a factor analysis model where a specified number of exclusion restrictions are imposed on the model but not which coefficients are subject to exclusion restrictions. The algorithm, which is implemented in the stable version of FA i R, finds the best-fitting combination of the locations of the null coefficients and the values of the non-zero parameters. A generalization of this idea will be implemented for LISREL models sometime in the reasonably near future.