Efficient Estimation of Structural Models via Sieves

Yao Luo and Peijun Sang

Papers from arXiv.org

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.

Date: 2022-04, Revised 2025-02
New Economics Papers: this item is included in nep-ecm
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