Inverse problems can be described as functional equations where the value of the function is known or easily estimable but the argument is unknown. Many problems in econometrics can be stated in the form of inverse problems where the argument itself is a function. For example, consider a...

Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; semi-nonparametric models are more flexible and robust, but lead to other complications such as introducing infinite-dimensional parameter spaces that may not be compact and the...

This chapter reviews recent advances in nonparametric and semiparametric estimation, with an emphasis on applicability to empirical research and on resolving issues that arise in implementation. It considers techniques for estimating densities, conditional mean functions, derivatives of...

This chapter discusses structural econometric approaches to auctions. Remarkably, much of what can be learned from auction data can be learned without restrictions beyond those derived from the relevant economic model. This enables us to take a nonparametric perspective in discussing how the...

Surveys that are designed to measure subjective states (e.g., happiness) typically generate ordinal data. A fundamental problem is that methods used to analyse ordinal data (e.g., ordered probit) rely on strong and often unjustified distributional assumptions. In this paper, we propose using...

Surveys are an important tool in economics and in the social sciences more broadly. However, methods used to analyse ordinal survey data (e.g., ordered probit) rely on strong and often unjustified distributional assumptions. In this paper, we propose using survey response times to solve that...

Given the ubiquitous presence of endogenous regressors and the challenges in finding good instruments to overcome the endogeneity problem, a forefront of recent research is the development and application of endogeneity correction methods without requiring instruments. In this article, we...

We provide semiparametric identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: i) known heterogeneity, ii) initially unknown heterogeneity that may be revealed over time, and iii) transitory uncertainty. We consider a...