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A Monte Carlo comparison of estimating the number of dynamic factors

Author

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  • Zhao Zhao

    (Huazhong University of Science and Technology)

  • Guowei Cui

    (Huazhong University of Science and Technology)

  • Shaoping Wang

    (Huazhong University of Science and Technology)

Abstract

Based on a Monte Carlo simulation, this study compares the finite sample performance of five of the most widely used methods for estimating the number of dynamic factors. The simulation results show that although the performance is affected by the data generating process, the methods proposed by Hallin and Liška (J Am Stat Assoc 102(478):603–617, 2007) and Bai and Ng (Bus Econ Stat 25(1):52–60, 2007) generally outperform the others. Specifically, Amengual and Watson’s (J Bus Econ Stat 25(1):91–96, 2007) method is sensitive to cross-sectional correlation, and Breitung and Pigorsch’s (Oxf Bull Econ Stat 75(1):23–36, 2013) estimator is sensitive to the overestimation of the number of static factors. The results of this study are further supported by an empirical application to a Chinese macroeconomic dataset.

Suggested Citation

  • Zhao Zhao & Guowei Cui & Shaoping Wang, 2017. "A Monte Carlo comparison of estimating the number of dynamic factors," Empirical Economics, Springer, vol. 53(3), pages 1217-1241, November.
    • Handle: RePEc:spr:empeco:v:53:y:2017:i:3:d:10.1007_s00181-016-1167-4
    DOI: 10.1007/s00181-016-1167-4
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    References listed on IDEAS

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    3. Xin Tian & Jan Jacobs & Jakob de Haan, 2022. "Alternative Measures for the Global Financial Cycle: Do They Make a Difference?," CESifo Working Paper Series 9730, CESifo.

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    More about this item

    Keywords

    Finite sample performance; Number of dynamic factors; Dynamic factor loading; Cross-sectional correlation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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