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Factor-augmented regression models with structural change

机译:具有结构变化的因子增强回归模型

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摘要

This paper considers a factor-augmented regression model in the presence of structural change. We propose a two-step procedure to estimate the coefficients of explanatory variables. We show that when the number of units (N) and the number of periods (T) are large and comparable, the proposed two-step estimator is root T-consistent and has the same limiting distribution as if the unobservable factors were observed. Monte Carlo simulations confirm our theoretical results and show good finite sample performance of the two-step estimator. (C) 2015 Elsevier B.V. All rights reserved.
机译:本文考虑存在结构变化的因素增强回归模型。我们提出了一个两步过程来估计解释变量的系数。我们表明,当单位数(N)和周期数(T)大且可比较时,拟议的两步估计量与根T一致,并且具有相同的极限分布,就像观察到了不可观察的因素一样。蒙特卡洛模拟证实了我们的理论结果,并显示了两步估计器的良好有限样本性能。 (C)2015 Elsevier B.V.保留所有权利。

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