Exponent of Cross-sectional Dependence for Residuals
Exponent of Cross-sectional Dependence for Residuals
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DOI：
10.1007/s13571-019-00196-9
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发表时间：
2019-09-01
影响因子：
0.8
通讯作者：
Pesaran, M. Hashem
中科院分区：
文献类型：
--
作者：
Bailey, Natalia;Kapetanios, George;Pesaran, M. Hashem
在本文中，我们着重于估计一个经典面板数据回归模型的误差项的横截面依赖程度。为此，我们提出了一个横截面相关性指数的估计量，用a表示，它是基于这些误差的非零成对交叉相关的数量。我们证明了我们的估计量a是一致的，并推导出它接近其真值的速率。我们还提出了一种重采样方法来构造a的估计量周围的置信界。我们利用蒙特卡罗模拟研究评估了所提出的估计量的有限样本性质。数值结果对理论结果具有鼓舞和支持作用。最后，我们使用1989年9月至2018年5月期间标准普尔500指数证券的10年期滚动样本，对CAPM模型及其Fama-French扩展的误差进行了实证调查。
In this paper, we focus on estimating the degree of cross-sectional dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of cross-sectional dependence denoted by a, which is based on the number of non-zero pair-wise cross correlations of these errors. We prove that our estimator, a, is consistent and derive the rate at which it approaches its true value. We also propose a resampling procedure for the construction of confidence bounds around the estimator of a. We evaluate the finite sample properties of the proposed estimator by use of a Monte Carlo simulation study. The numerical results are encouraging and supportive of the theoretical findings. Finally, we undertake an empirical investigation of a for the errors of the CAPM model and its Fama-French extensions using 10-year rolling samples from S&P 500 securities over the period Sept 1989 - May 2018.