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Finite Sample Performance of Principal Components Estimators for Dynamic Factor Models: Asymptotic vs. Bootstrap Approximations

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  • Shintani, Mototsugu
  • Guo, Zi-Yi

Abstract

This paper investigates the finite sample properties of the two-step estimators of dynamic factor models when unobservable common factors are estimated by the principal components methods in the first step. Effects of the number of individual series on the estimation of an auto-regressive model of a common factor are investigated both by theoretical analysis and by a Monte Carlo simulation. When the number of the series is not sufficiently large relative to the number of time series observations, the auto-regressive coefficient estimator of positively auto-correlated factor is biased downward and the bias is larger for a more persistent factor. In such a case, bootstrap procedures are effective in reducing the bias and bootstrap confidence intervals outperform naive asymptotic confidence intervals in terms of controlling the coverage probability.

Suggested Citation

  • Shintani, Mototsugu & Guo, Zi-Yi, 2011. "Finite Sample Performance of Principal Components Estimators for Dynamic Factor Models: Asymptotic vs. Bootstrap Approximations," EconStor Preprints 167627, ZBW - German National Library of Economics.
  • Handle: RePEc:zbw:esprep:167627
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    References listed on IDEAS

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    Cited by:

    1. Gonçalves, Sílvia & Perron, Benoit, 2014. "Bootstrapping factor-augmented regression models," Journal of Econometrics, Elsevier, vol. 182(1), pages 156-173.
    2. Jushan Bai & Kunpeng Li & Lina Lu, 2016. "Estimation and Inference of FAVAR Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 620-641, October.

    More about this item

    Keywords

    Bias Correction; Bootstrap; Dynamic Factor Model; Principal Components;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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