Efficient Estimation of Structural Models via Sieves

TL;DR

This paper introduces a sieve-based estimation method for structural models that improves efficiency by avoiding repeated model solutions and simplifies standard error calculations.

Contribution

The paper presents a novel class of sieve-based estimators (SEES) that are consistent, asymptotically normal, and efficient, applicable to a broad range of models.

Findings

Estimators are consistent and asymptotically normal.

Method avoids repeated model solving, reducing computational burden.

Applied successfully to an entry game between Walmart and Kmart.

Abstract

We propose a class of sieve-based efficient estimators for structural models (SEES), which approximate the solution using a linear combination of basis functions and impose equilibrium conditions as a penalty to determine the best-fitting coefficients. Our estimators avoid the need to repeatedly solve the model, apply to a broad class of models, and are consistent, asymptotically normal, and asymptotically efficient. Moreover, they solve unconstrained optimization problems with fewer unknowns and offer convenient standard error calculations. As an illustration, we apply our method to an entry game between Walmart and Kmart.