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

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  • Mototsugu Shintani

    () (University of Tokyo and Vanderbilt University)

  • Zi-yi Guo

    () (Vanderbilt University)

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.

Suggested Citation

  • Mototsugu Shintani & Zi-yi Guo, 2015. "Improving the Finite Sample Performance of Autoregression Estimators in Dynamic Factor Models: A Bootstrap Approach," Vanderbilt University Department of Economics Working Papers 15-00013, Vanderbilt University Department of Economics.
    • Handle: RePEc:van:wpaper:vuecon-sub-15-00015
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    References listed on IDEAS

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    1. Yohei Yamamoto, 2012. "Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions," Global COE Hi-Stat Discussion Paper Series gd12-249, Institute of Economic Research, Hitotsubashi University.
    2. Shintani, Mototsugu, 2008. "A dynamic factor approach to nonlinear stability analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 32(9), pages 2788-2808, September.
    3. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    4. Woodford, Michael, 1999. "Optimal Monetary Policy Inertia," Manchester School, University of Manchester, vol. 67(0), pages 1-35, Supplemen.
    5. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    6. MacKinnon, James G. & Smith Jr., Anthony A., 1998. "Approximate bias correction in econometrics," Journal of Econometrics, Elsevier, vol. 85(2), pages 205-230, August.
    7. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
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    Cited by:

    1. Yohei Yamamoto, 2012. "Bootstrap Inference for Impulse Response Functions in Factor-Augmented Vector Autoregressions," Global COE Hi-Stat Discussion Paper Series gd12-249, Institute of Economic Research, Hitotsubashi University.
    2. Ruiz Ortega, Esther & Vicente Maldonado, Javier de, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.

    More about this item

    Keywords

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

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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