Research methods

This paper studies inference in randomized controlled trials with covariate-adaptive randomization when there are multiple treatments. More specically, we study in this setting inference about the average effect of one or more treatments relative to other treatments or a control.

We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions.

Slides and software used to introduce the study of inter-temporal life-cycle models of consumption and savings and support the development of code to solve these type of problems numerically.

This paper shows that the IVQR estimation problem can be decomposed into a set of conventional quantile regression sub-problems, which are convex and can be solved efficiently. This allows for reformulating the original estimation problem as the problem of finding the fixed point of a low dimensional map.

Many empirical questions can be cast as inference on a parameter selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data. Such settings give rise to a winner’s curse, where conventional estimates are biased and conventional confidence intervals are unreliable.

We consider nonlinear moment restriction semiparametric models where both the dimension of the parameter vector and the number of restrictions are divergent with sample size and an unknown smooth function is involved.

We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model that accommodates much rich patterns of heterogeneity in the selection process and effect of the covariates. The model applies to continuous, discrete and mixed outcomes. We study the identification of the model, and develop a computationally attractive two-step method to estimate the model parameters, where the first step is a probit regression for the selection equation and the second step consists of multiple distribution regressions with selection corrections for the outcome equation.

e develop a consistent estimator using external information on the true distribution of prices. Examining the demand for gasoline in the U.S., accounting for Berkson errors is found to be quantitatively important for estimating price effects and for welfare calculations. Imposing the Slutsky shape constraint greatly reduces the sensitivity to Berkson errors.

Multidimensional heterogeneity and endogeneity are important features of a wide class of econometric models. We consider heterogenous coefficients models where the outcome is a linear combination of known functions of treatment and heterogenous coefficients.

We propose a new approach to computation of NPMLEs for binary response models that signicantly increase their computational tractability thereby facilitating greater exibility in applications.

Many econometric models used in applied work integrate over unobserved heterogeneity. We show that a class of these models that includes many random coefficients demand systems can be approximated by a "small-sigma" expansion that yields a straightforward 2SLS estimator. We study in detail the models of market shares popular in empirical IO ("macro BLP").

We study social learning in a continuous action space experiment. Subjects, acting in sequence, state their belief about the value of a good, after observing their predecessors' statements and a private signal.

This paper reconciles the asymptotic disagreement between Bayesian and frequentist inference in set-identified models by adopting a multiple-prior (robust) Bayesian approach.

Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables.

We propose a framework for estimation and inference about the parameters of an economic model and predictions based on it, when the model may be misspecified.

It is almost self-evident that social interactions can determine economic behavior and outcomes. Yet, information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents.

Currently there is little practical advice on which treatment effect estimator to use when trying to adjust for observable differences. A recent suggestion is to compare the performance of estimators in simulations that somehow mimic the empirical context. Two ways to run such ‘empirical Monte Carlo studies’ (EMCS) have been proposed.

This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics.

We consider many structural objects of interest, such as average or quantile treatment effects. We illustrate our results with an empirical application to Engel curve estimation.

This paper presents a simple method for carrying out inference in a wide variety of possibly nonlinear IV models under weak assumptions.