论文标题
通过筛子对结构模型的有效估计
Efficient Estimation of Structural Models via Sieves
论文作者
论文摘要
我们为结构模型（SEE）提出了一类基于筛子的有效估计器，该估计器使用基础函数的线性组合近似溶液，并施加平衡条件，以确定最佳拟合系数。我们的估计器避免需要反复解决该模型，适用于广泛的模型，并且是一致，渐近正常和渐近效率的一致性。此外，他们以更少的未知数解决了不受约束的优化问题，并提供了方便的标准错误计算。作为插图，我们将方法应用于沃尔玛和凯马特之间的参赛游戏。
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.
