Robust inference in deconvolution

Abstract

Kotlarski's identity has been widely used in applied economic research based on repeated‐measurement or panel models with latent variables. However, how to conduct inference for these models has been an open question for two decades. This paper addresses this open problem by constructing a novel confidence band for the density function of a latent variable in repeated measurement error model. The confidence band builds on our finding that we can rewrite Kotlarski's identity as a system of linear moment restrictions. Our approach is robust in that we do not require the completeness. The confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives. Simulation studies support our theoretical results.

Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs

“…This assumptions plays the role of restricting the function class where g 0 resides, similarly to Kato et al (2021) in the spirit of the honest inference approach (Armstrong andKolesár, 2018, 2020) and the bias bound approach (Schennach, 2020). 4…”

“…In the final step, we inflate the bounds by a sieve approximation error bound similarly to Armstrong andKolesár (2018, 2020), Noack and Rothe (2019), Schennach (2020), andKato, Sasaki, andUra (2021).…”