Minimum Sliced Distance Estimation in a Class of Nonregular Econometric Models

TL;DR

This paper introduces a minimum sliced distance estimator for structural econometric models with potentially parameter-dependent supports, offering a robust alternative to likelihood-based methods with straightforward inference.

Contribution

It develops a novel estimation method that remains effective under mild regularity conditions and supports parameter-dependent supports, demonstrated through an auction model.

Findings

Estimator is asymptotically normally distributed

Performs well in models with parameter-dependent supports

Enables simple inference regardless of support regularity

Abstract

This paper proposes minimum sliced distance estimation in structural econometric models with possibly parameter-dependent supports. In contrast to likelihood-based estimation, we show that under mild regularity conditions, the minimum sliced distance estimator is asymptotically normally distributed leading to simple inference regardless of the presence/absence of parameter dependent supports. We illustrate the performance of our estimator on an auction model.