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Mototsugu Shintani and Zi-yi Guo
''Improving the Finite Sample Performance of Autoregression Estimators in Dynamic Factor Models: A Bootstrap Approach''
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 e¤ective in reducing the bias, and bootstrap confidence intervals outperform naive asymptotic confidence intervals in terms of the coverage probability.
Keywords: Bias Correction; Bootstrap; Dynamic Factor Model; Principal Components
JEL: C1 - Econometric and Statistical Methods: General
C5 - Econometric Modeling: General
Manuscript Received : Nov 25 2015 Manuscript Accepted : Dec 02 2015

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