Simulation and Semiparametric Estimation of MicroeconometricModels

该项目由三个独立但相关的研究组成 最大模拟似然估计中的项目， 半参数最小距离估计 带验证的非线性计量误差模型 数据信息。 这些项目研究模拟和 半参数估计和统计检验方法 许多重要的微观经济计量模型。 这些方法将 对离散面板数据的估计和检验是有用的 模型，有限相依的联立方程模型 变量，多市场非均衡模型，消费者需求， 非负和/或数量的产生式系统 约束和变量误差的非线性模型。 更具体地说，该项目的第一部分涉及 最大值偏差校正方法的推广 模拟似然估计方法下开发的 先前NSF授予离散选择模型。 这些估计 和推理程序应用于经验估计 消费者需求和/或生产系统， 印尼 第二部分介绍半参数估计 经典的最小距离估计法程序 对于具有定性和限制性的联立方程模型， 因变量，多市场非均衡模型， 条件期望样本选择模型 规范. 这类模型的半参数估计方法 在当前的计量经济学文献中， 第三 部分开发的估计方法，考虑到 验证数据和调查面板数据的信息，以及 所有变量的测量误差。 拟议 方法论将应用于劳动力供给的研究 模型使用的数据来自收入动态的面板研究。

This project consists of three separate but related research projects in maximum simulated likelihood estimation, semiparametric minimum distance estimation and estimation of nonlinear error-in-variables econometric models with validation data information. These projects investigate simulation and semiparametric methods of estimation and statistical tests for many important microeconometric models. The methodologies will be useful for the estimation and testing of discrete panel data models, simultaneous equation models with limited dependent variables, multimarket disequilibrium models, consumer demand and production systems with nonnegativity and/or quantity constraints, and nonlinear models with error-in-variables. More specifically, the first part of the project involves the generalization of bias correction procedures for maximum simulated likelihood estimation methods developed under the previous NSF grant for discrete choice models. These estimation and inference procedures are applied to empirical estimation of consumer demand and/or production systems with data from Indonesia. The second part introduces semiparametric estimation procedures for the classical minimum distance estimation method for simultaneous equation models with qualitative and limited dependent variables, multimarket disequilibrium models, and sample selection models with conditional expectation specifications. Semiparametric estimation methods of such models are limited in the current econometric literature. The third part develops estimation methods which take into account the information of validation data and survey panel data and the measurement errors in all of the variables. The proposed methodologies will be applied to the study of a labor supply model using data from the Panel Study of Income Dynamics.