Improving the Finite Sample Performance of Autoregression Estimators in Dynamic Factor Models: A Bootstrap Approach

Mototsugu Shintani () and Zi-yi Guo ()
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Mototsugu Shintani: University of Tokyo and Vanderbilt University
Zi-yi Guo: Vanderbilt University

No 15-00013, Vanderbilt University Department of Economics Working Papers from Vanderbilt University Department of Economics

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C1 C5 (search for similar items in EconPapers)
Date: 2015-12-02
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Improving the finite sample performance of autoregression estimators in dynamic factor models: A bootstrap approach (2018)
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