Some identification and estimation results for regression models with stochastically varying coefficients
Some identification and estimation results for regression models with stochastically varying coefficients
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系数随机变化的回归模型的一些识别和估计结果
DOI：
10.1016/0304-4076(80)90084-6
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发表时间：
1980
影响因子：
6.3
通讯作者：
A. Pagan
中科院分区：
文献类型：
--
作者：
A. Pagan
虽然近年来出现了各种理论和应用论文与随机变系数回归模型的估计和使用有关，但在文献中很少有关于所提出的估计量的性质或这种模型的参数的可识别性。本文给出了极大似然估计相合且渐近正态的充分条件，并给出了平稳随机变系数回归模型估计的充分条件。在许多情况下，这些要求被发现有简单的，直观的吸引力的解释。一致性和渐近正态性也证明了两步估计和Rosenberg提出的方法产生的初始估计。
Although various theoretical and applied papers have appeared in recent years concerned with the estimation and use of regression models with stochastically varying coefficients, little is available in the literature on the properties of the proposed estimators or the identifiability of the parameters of such models. The present paper derives sufficient conditions under which the maximum likelihood estimator is consistent and asymptotically normal and also provides sufficient conditions for the estimation of regression models withstationarystochastically varying coefficients. In many instances these requirements are found to have simple, intuitively appealing interpretations. Consistency and asymptotic normality is also proven for a two-step estimator and a method suggested by Rosenberg for generating initial estimates.