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.
