Datasets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data.

We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive.

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

We develop a multi-risk SIR model (MR-SIR) where infection, hospitalization and fatality rates vary between groups—in particular between the “young”, “the middle-aged” and the “old”.

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’.

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

We carry out some analysis of the daily data on the number of new cases and number of new deaths by (191) countries as reported to the European CDC.

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

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

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.

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