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Estimation of conditional moment restrictions without assuming parameter identifiability in the implied unconditional moments

机译:在不假定隐含无条件矩的参数可识别性的情况下,对有条件矩限制的估计

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摘要

A well-known difficulty in estimating conditional moment restrictions is that the parameters of interest need not be globally identified by the implied unconditional moments. In this paper, we propose an approach to constructing a continuum of unconditional moments that can ensure parameter identifiability. These unconditional moments depend on the "instruments" generated from a "generically comprehensively revealing" function, and they are further projected along the exponential Fourier series. Theobjective function is based on the resulting Fourier coefficients, from which an estimator can be easily computed. A novel feature of our method is that the full continuum of unconditional moments is incorporated into each Fourier coefficient. We show that, when the number of Fourier coefficients in the objective function grows at a proper rate, the proposed estimator is consistent and asymptotically normally distributed. An efficient estimator is also readily obtained via the conventional two-step GMMmethod. Our simulations confirm that the proposed estimator compares favorably with that of Dominguez and Lobato (2004, Econometrica) in terms of bias, standard error, and mean squared error.
机译:估计条件矩约束的一个众所周知的困难是,所需参数不需要通过隐含的无条件矩来全局标识。在本文中,我们提出了一种构造无条件矩连续体的方法,该方法可以确保参数的可识别性。这些无条件矩取决于从“一般全面显示”功能生成的“仪器”,并且它们会沿着指数傅立叶级数进一步投影。目标函数基于所得的傅立叶系数,可以轻松地从中计算出估计量。我们方法的一个新颖特征是将无条件矩的完整连续体合并到每个傅立叶系数中。我们表明,当目标函数中的傅立叶系数数目以适当的速率增长时,所提出的估计量是一致的,并且渐近正态分布。通过常规的两步GMM方法也可以轻松获得有效的估计量。我们的仿真证实,在偏倚,标准误差和均方误差方面,拟议的估计量与Dominguez和Lobato(2004,Econometrica)相比具有优势。

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