Simulation and Semiparametric Estimation of MicroeconometricModels

本项目包括三个独立但相关的研究项目：最大模拟似然估计、半参数最小距离估计和非线性变量误差计量模型的估计与验证数据信息。这些项目研究了许多重要的微观计量模型的模拟和半参数估计方法和统计检验。这些方法将有助于估计和测试离散面板数据模型、有限因变量的联立方程模型、多市场不平衡模型、非负性和/或数量限制的消费者需求和生产系统。以及带有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模拟似然估计方法的偏差校正程序的泛化，该方法是在以前的美国国家科学基金会资助下为离散选择模型开发的。这些估计和推断程序应用于根据印度尼西亚数据对消费者需求和/或生产系统进行经验估计。第二部分介绍了具有定性和有限因变量的联立方程模型、多市场非均衡模型和具有条件期望规范的样本选择模型的经典最小距离估计方法的半参数估计过程。这些模型的半参数估计方法在目前的计量经济学文献中是有限的。第三部分提出了考虑验证数据和调查面板数据信息以及各变量测量误差的估计方法。所提出的方法将应用于使用收入动态面板研究数据的劳动力供给模型的研究。

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.