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Accurate Confidence Regions for Principal Components Factors*
Oxford Bulletin of Economics and Statistics ( IF 2.5 ) Pub Date : 2021-04-23 , DOI: 10.1111/obes.12436
Javier Maldonado 1 , Esther Ruiz 1
Affiliation  

In dynamic factor models, factors are often extracted using principal components with their asymptotic confidence regions having empirical coverages below the nominal ones when the temporal dimension is small. We propose a subsampling procedure to compute the factor loadings uncertainty and correct the asymptotic covariance matrix of the extracted factors. We show that the empirical coverages of the modified confidence regions are closer to the nominal ones than those of asymptotic regions and asymptotically valid bootstrap regions. The results are empirically illustrated obtaining confidence intervals of the underlying factor in a system of Spanish macroeconomic variables.

中文翻译:

主成分因子的准确置信区域*

在动态因子模型中,当时间维度较小时,因子通常使用主成分提取,其渐近置信区域的经验覆盖率低于名义值。我们提出了一个子抽样程序来计算因子载荷的不确定性并校正提取因子的渐近协方差矩阵。我们表明,与渐近区域和渐近有效引导区域的经验覆盖相比,修改后的置信区域的经验覆盖更接近名义上的覆盖。结果是凭经验说明获得西班牙宏观经济变量系统中潜在因素的置信区间。
更新日期:2021-04-23
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