Estimation with Simulation: Theory and Application

该项目开发了使用模拟的估计方法， 克服数值复杂的计算负担， 经济计量模型 这是一个非常重要的研究方向 因为许多经济问题即使使用 超级计算机 许多应用经济学家必须有意识地设计 模型，以避免计算棘手的方面，即使 这一问题的经济学原理自然会导致这一方向。 本课题对拟矩法进行了推广， 解决复杂的经验经济问题，并扩展了 线性指数模型的一类新的半参数估计 由研究人员在他以前的NSF资助下开发。 将模拟矩法推广到有限矩法， 非条件多项式因变量模型 Probit模型及其相关的推理问题 近似的矩函数就像对数的分数， 可能性 计算的研究解决了 和模拟矩估计的不可微性。 的 第二部分是实证研究， 模拟矩估计方法对劳动力的分析 提供非线性税收。 研究的第三部分是 指数模型的半参数估计 渐近 一类线性指标估计的分布理论 模型将被推导出来，这个家庭之间的关系 的估计和其他人进行了探讨。

This project develops estimation methods that use simulations to overcome the computational burdens of numerically complex econometric models. This is a very important line of research because many economic problems are intractable even using supercomputers. Many applied economists must consciously design models to avoid computationally intractable aspects, even though the economics of the issue naturally lead in that direction. This project generalizes the simulated moments methods, uses it to solve complicated empirical economic problems and extends a new class of semi-parametric estimators for linear index models developed by the investigator under his previous NSF grant. The method of simulated moments is generalized to limited dependent variable models besides the conditional multinomial Probit model and to inference problems associated with approximating moment functions like the score of the log- likelihood. The research in computation addresses the existence and nondifferentiability of simulated moments estimators. The second part of the research is an empirical application of simulated moments estimation methods to the analysis of labor supply with nonlinear taxes. The third part of the research is about semi-parametric estimation of index models. Asymptotic distribution theory for a class of estimators for linear index models will be derived and the relationship between this family of estimators and others is explored.