This paper proposes a bootstrap-based procedure to build confidence intervals for single components of a partially identified parameter vector, and for smooth functions of such components, in moment (in)equality models. The extreme points of our confidence interval are obtained by...

We propose a bootstrap-based calibrated projection procedure to build con fidence intervals for single components and for smooth functions of a partially identi fied parameter vector in moment (in)equality models. The method controls asymptotic coverage uniformly over a large class of data...

This paper is concerned with learning decision makers' (DMs) preferences using data on observed choices from a fi nite set of risky alternatives with monetary outcomes. We propose a discrete choice model with unobserved heterogeneity in consideration sets (the collection of alternatives...

This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identifi cation literature by allowing the upper and lower functions de ning the band to carry an index, and to be unknown but parametrically or...

Econometrics has traditionally revolved around point identi cation. Much effort has been devoted to finding the weakest set of assumptions that, together with the available data, deliver point identifi cation of population parameters, finite or infi nite dimensional that these might be. And...

We propose a bootstrap-based calibrated projection procedure to build confidence intervals for single components and for smooth functions of a partially identified parameter vector in moment (in)equality models. The method controls asymptotic coverage uniformly over a large class of data...

We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between agents' choice sets and...

This chapter reviews the microeconometrics literature on partial identification, focusing on the developments of the last thirty years. The topics presented illustrate that the available data combined with credible maintained assumptions may yield much information about a parameter of interest,...

As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of cumulative population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual...

This paper is concerned with learning decision makers' preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain sufficient conditions for the...