Stochastic Simulation for Inference in Nonlinear Errors-in- Variables Models

这个项目将研究同时非线性推理 变量误差模型-识别，估计， 预测和规格测试。之间的联系 测量误差模型的可辨识性和非线性 将进行研究，并适当充分识别 条件分析。测量误差的后果 检验假设、估计和 将研究非线性模型的验证。参数和 建立了半参数估计程序， 非线性变量误差模型蒙特卡罗的改进 和基于残差的随机模拟被开发来处理 测量误差问题，不仅适用于预测， 以及估算和规格测试。大样本 这些程序的渐近理论将得到发展。 特别注意的是给予治疗的测量 错误生成过程。标准高斯白色鼻子模型 测量误差是分析的起点。 然后，分析继续进行，以减少限制， 测量误差模型是非线性和非高斯的。 同时系统与有限的依赖变量使用 为了更具体地审查新的 技术.

This project will study inference in simultaneous nonlinear error-in-variables models - identification, estimation, prediction and specification testing. The linkages between identifiability and nonlinearities in the measurement error model will be studied and appropriate sufficient identifiability conditions analyzed. The consequences of measurement errors on standard procedures for testing hypotheses, estimation and validation in nonlinear models will be studied. Parametric and semiparametric procedures are established for the estimation of nonlinear error-in-variables models. Modifications of Monte Carlo and residual-based stochastic simulations are developed to handle measurement error problems and adapted to not only prediction but also estimation and specification testing. The large-sample asymptotic theory for these procedures will be developed. Particular attention is given to the treatment of the measurement error generating process. The standard Gaussian white nose model for measurement errors is the starting point for the analysis. Analysis then continues to less restrictive and more appropriate measurement error models which are nonlinear and non-Gaussian. Simultaneous systems with limited dependent variables are used for a more specific examination of the applicability of the new techniques.