A0163
Title: Efficient estimation of structural models via sieves
Authors: Yao Luo - University of Toronto (Canada) [presenting]
Peijun Sang - University of Waterloo (Canada)
Abstract: A class of sieve-based efficient estimators is proposed 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. The estimators avoid the need to repeatedly solve the model, apply it 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, the method is applied to an entry game between Walmart and Kmart.
