Efficient Estimation of Structural Models via Sieves

Yao Luo and Peijun Sang

Working Papers from University of Toronto, Department of Economics

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 circumvent repeated solution of the structural 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.

Keywords: Efficient Estimation; Sieves; Empirical Games; Joint Algorithm; Nested Algorithm (search for similar items in EconPapers)
JEL-codes: C45 C51 C57 (search for similar items in EconPapers)
Pages: Unknown pages
Date: 2025-06-22
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Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-801

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