Methods

Which housing characteristics are important for understanding homeownership rates?

This article provides a selective review on the recent literature on econometric models of network formation.

This paper introduces measures for how each moment contributes to the precision of parameter estimates in GMM settings.

We propose an optimal-transport-based matching method to nonparametrically estimate linear models with independent latent variables.

We study the incidental parameter problem in "three-way" Poisson Pseudo-Maximum Likelihood "PPML" gravity models recently recommended for identifying the effects of trade policies.

Consumption Euler equations are important tools in empirical macroeconomics. When estimated on micro data, they are typically linearized, so standard IV or GMM methods can be employed to deal with the measurement error that is endemic to survey data. However, linearization, in turn, may induce serious approximation bias.

We propose a nonparametric inference method for causal effects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters.

Many economic activities are embedded in networks: sets of agents and the (often) rivalrous relationships connecting them to one another.

We show the extent of errors made in the award of disability insurance using matched survey-administrative data. False rejections (Type I errors) are widespread, and there are large gender differences in these type I error rates.

We investigate the consequences of discreteness in the assignment variable in regression-discontinuity designs for cases where the outcome variable is itself discrete.

We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability.

This study develops a framework for testing hypotheses on structural parameters in in-complete models.

We compare two groups of the non-student Korean population—native-born South Koreans (SK) and North Korean refugees (NK)—with contrasting institutional and cultural backgrounds.

This paper presents a weighted optimization framework that unifies the binary, multi-valued, continuous, as well as mixture of discrete and continuous treatment, under unconfounded treatment assignment.

We propose a solution to the measurement error problem that plagues the estimation of the relation between the expected return of the stock market and its conditional variance due to the latency of these conditional moments.

Recent research underscores the sensitivity of conclusions drawn from the application of econometric methods devised for quantitative outcome variables to data featuring ordinal outcomes. The issue is particularly acute in the analysis of happiness data, for which no natural cardinal scale exists, and which is thus routinely collected by ordinal response. With ordinal responses, comparisons of means across different populations and the signs of OLS regression coefficients have been shown to be sensitive to monotonic transformations of the cardinal scale onto which ordinal responses are mapped.

We present a general framework for studying regularized estimators; such estimators are pervasive in estimation problems wherein “plug-in” type estimators are either ill-defined or ill-behaved.

We analyze the sources of changes in the distribution of hourly wages in the United States using CPS data for the survey years 1976 to 2016.

Collective models have become the go-to framework for intra-household allocations.

This paper investigates how different income shocks shape consumption dynamics over the business cycle. First, we break new ground by creating a unique, panel dataset of transitory and permanent income shocks, using subjective income expectations from the Dutch Household Survey.