Penalized Sieve Estimation of Structural Models

28 Apr 2022  ·  Yao Luo, Peijun Sang ·

Estimating structural models is an essential tool for economists. However, existing methods are often inefficient either computationally or statistically, depending on how equilibrium conditions are imposed. We propose a class of penalized sieve estimators that are consistent, asymptotic normal, and asymptotically efficient. Instead of solving the model repeatedly, we approximate the solution with a linear combination of basis functions and impose equilibrium conditions as a penalty in searching for the best fitting coefficients. We apply our method to an entry game between Walmart and Kmart.

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