Research methods

This chapter reviews the microeconometrics literature on partial identification, focusing on the developments of the last thirty years.

Across many fields in economics, a common approach to estimation of economic models is to calibrate a sub-set of model parameters and keep them fixed when estimating the remaining parameters.

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

We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the parameters of interest are set-identified using external instruments, or ‘proxy SVARs’.

We examine changes in inequality in socio-emotional skills very early in life in two British cohorts born 30 years apart.

It is often desired to rank different populations according to the value of some feature of each population.

Models of simultaneous discrete choice may be incomplete, delivering multiple values of outcomes at certain values of the latent variables and co-variates, and incoherent, delivering no values.

This paper studies identification and inference in transformation models with endogenous censoring.

We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures.

This paper examines the case for randomized controlled trials in economics. I revisit my previous paper “Randomization and Social Policy Evaluation” and update its message.

Which housing characteristics are important for understanding homeownership rates?

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. In our experiment, the subjects play dictator games under three different treatments in which the income source varies: first, the income is randomly given to the subject; second, it is earned by the subject; third, it is individually earned by the subject and an anonymous partner and then pooled together.

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

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

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.