Research methods

Rebayes: an R package for empirical bayes mixture methods

Testing for homogeneity in mixture models

This paper studies inference in randomized controlled trials with covariate-adaptive randomization when there are multiple treatments. More specifically, we study in this setting inference about the average effect of one or more treatments relative to other treatments or a control. As in Bugni et al. (2017), covariate-adaptive randomization refers to randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve "balance" within each stratum. In contrast to Bugni et al. (2017), however, we allow for the proportion of units being assigned to each of the treatments to vary across strata.

The exponential GARCH (EGARCH) model introduced by Nelson (1991) is a popular model for discrete time volatility since it allows for asymmetric effects and naturally ensures positivity even when including exogenous variables.

Following the work of Gini, Dagum and Tukey, this paper extends Gini’s Transvariation measure for comparing two distributions to the simultaneous comparison of many distributions.

Oracle Estimation of a Change Point in High Dimensional Quantile Regression

Consideration set models relax the assumption that consumers are aware of all available options.

Increased competition for viewers’ time is threatening the viability of public-service broadcasters (PSBs) around the world.

This article studies the non-parametric identification and estimation of voters’ preferences when voters are ideological. We establish that voter preference distributions and other parameters of interest can be identified from aggregate electoral data.

Multinomial choice models are fundamental for empirical modeling of economic choices among discrete alternatives. We analyze identifcation of binary and multinomial choice models when the choice utilities are nonseparable in observed attributes and multidimen- sional unobserved heterogeneity with cross-section and panel data.

I study a game theoretic spatial model of elections with many heterogeneous constituencies in which both party and candidate behavior are modeled. Parties choose a platform and a ‘whip rate,’ representing the proportion of final policy that will be made by the party, as opposed to by the successful candidates. Candidates are office-motivated and can choose both a platform and a level of advertising in order to defeat their opponent. It is shown that the introduction of whipping as a choice variable can cause party platforms to diverge and that parties will whip on some but not all issues, reflecting the empirical reality of parties influencing rather than determining policy outcomes exclusively.

We study social learning in a continuous action space experiment.

In nonlinear panel models with fixed effects and fixed-T, the incidental parameter problem poses identification difficulties for structural parameters and partial effects. Existing solutions are model-specific, likelihood-based, impose time homogeneity, or restrict the distribution of unobserved heterogeneity. We provide new identification results for the large class of Fixed Effects Linear Transformation (FELT) models with unknown, time-varying, weakly monotone transformation functions.

Robins et al. (2008, 2016b) applied the theory of higher order infuence functions (HOIFs) to derive an estimator of the mean of an outcome Y in a missing data model with Y missing at random conditional on a vector X of continuous covariates; their estimator, in contrast to previous estimators, is semiparametric efficient under minimal conditions. However the Robins et al. (2008, 2016b) estimator depends on a non-parametric estimate of the density of X. In this paper, we introduce a new HOIF estimator that has the same asymptotic properties as their estimator but does not require non-parametric estimation of a multivariate density, which is important because accurate estimation of a high dimensional density is not feasible at the moderate sample sizes often encountered in applications. We also show that our estimator can be generalized to the entire class of functionals considered by Robins et al. (2008) which include the average effect of a treatment on a response Y when a vector X suffices to control confounding and the expected conditional variance of a response Y given a vector X.

If the productive characteristics of workers and firms are truly multi-dimensional, what features of the data do we miss by modeling them as one-dimensional scalars? This is the question we ask in this paper.

The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.

We revisit the classic semiparametric problem of inference on a low dimensional parameter θ0 in the presence of high-dimensional nuisance parameters η0.

This paper studies identification and estimation of the distribution of bidder valuations in an incomplete model of English auctions. As in Haile and Tamer (2003) bidders are assumed to (i) bid no more than their valuations and (ii) never let an opponent win at a price they are willing to beat. Unlike the model studied by Haile and Tamer (2003), the requirement of independent private values is dropped, enabling the use of these restrictions on bidder behavior with affiliated private values, for example through the presence of auction specifi…c unobservable heterogeneity. In addition, a semiparametric index restriction on the effect of auction-specifi…c observable heterogeneity is incorporated, which, relative to nonparametric methods, can be help- ful in alleviating the curse of dimensionality with a moderate or large number of covariates. The identification analysis employs results from Chesher and Rosen (2017) to characterize identified sets for bidder valuation distributions and functionals thereof.

This paper studies inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two-way regression model, which is a workhorse method in the analysis of matched data sets. Networks are typically quite sparse and it is difficult to see how the data carry information about certain parameters. We derive bounds on the variance of the fixed-effect estimator that uncover the importance of the structure of the network. These bounds depend on the smallest non-zero eigenvalue of the (normalized) Laplacian of the network and on the degree structure of the network. The Laplacian is a matrix that describes the network and its smallest non-zero eigenvalue is a measure of connectivity, with smaller values indicating less-connected networks. These bounds yield conditions for consistent estimation and convergence rates, and allow to evaluate the accuracy of first-order approximations to the variance of the fixed-effect estimator. The bounds are also used to assess the bias and variance of estimators of moments of the fixed effects.