Improving the finite sample performance of autoregression estimators in dynamic factor models: A bootstrap approach
研究了用主成分法估计不可观测共同因子时，自回归系数估计量在有限样本中的向下偏误，并证明自助法能有效减小偏误、改进置信区间覆盖概率。
We investigate the finite sample properties of the estimator of a persistence parameter of an unobservable common factor when the factor is estimated by the principal components method. When the number of cross-sectional observations is not sufficiently large, relative to the number of time series observations, the autoregressive coefficient estimator of a positively autocorrelated factor is biased downward, and the bias becomes larger for a more persistent factor. Based on theoretical and simulation analyses, we show that bootstrap procedures are effective in reducing the bias, and bootstrap confidence intervals outperform naive asymptotic confidence intervals in terms of the coverage probability.