A simple estimator for nonlinear error in variable models
A simple estimator for nonlinear error in variable models
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DOI：
10.1016/s0304-4076(03)00116-7
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发表时间：
2003-11-01
影响因子：
6.3
通讯作者：
Tamer, E
中科院分区：
文献类型：
--
作者：
Hong, H;Tamer, E
当没有额外的数据，如验证数据或双重测量时，我们提出了一个简单的估计量的非线性矩模型的方法与经典类型的测量误差。我们假设测量误差的边际分布是均值为零且方差未知的拉普拉斯（双指数）分布，并且测量误差与潜变量无关，且相互独立。在这些假设下，我们得到简单的修正矩条件的观测变量。它们用于推断模型参数和测量误差的方差。本文的结果表明，测量误差的分布假设可以用来点识别感兴趣的参数。我们的估计量是一个参数化的矩估计方法，它使用了修正的矩条件，因此计算简单。我们的估计方法在没有额外数据的情况下特别有用，这是许多经济数据集的情况。仿真研究表明，我们提出的估计良好的有限样本性质。我们还研究了在错误分布的情况下估计的性能。(C)2003 Elsevier B.V.保留所有权利。
We propose a simple estimator for nonlinear method of moment models with measurement error of the classical type when no additional data, such as validation data or double measurements, are available. We assume that the marginal distributions of the measurement errors are Laplace (double exponential) with zero means and unknown variances and the measurement errors are independent of the latent variables and are independent of each other. Under these assumptions, we derive simple revised moment conditions in terms of the observed variables. They are used to make inference about the model parameters and the variance of the measurement error. The results of this paper show that the distributional assumption on the measurement errors can be used to point identify the parameters of interest. Our estimator is a parametric method of moments estimator that uses the revised moment conditions and hence is simple to compute. Our estimation method is particularly useful in situations where no additional data are available, which is the case in many economic data sets. Simulation study demonstrates good finite sample properties of our proposed estimator. We also examine the performance of the estimator in the case where the error distribution is misspecified. (C) 2003 Elsevier B.V. All rights reserved.