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Simultaneous statistical inference in dynamic factor models: Chi-square approximation and model-based bootstrap

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  • Dickhaus, Thorsten
  • Sirotko-Sibirskaya, Natalia

Abstract

Statistical inference methodology in dynamic factor models (DFMs) is extended to the multiple testing context based on a central limit theorem for empirical Fourier transforms of multivariate time series. This theoretical result allows for employing a vector of Wald-type test statistics which asymptotically follows a multivariate chi-square distribution under the global null hypothesis when the observation horizon tends to infinity. Multiplicity-adjusted asymptotic multiple test procedures based on Wald statistics are compared with a model-based bootstrap procedure proposed in recent previous work. Monte Carlo simulations demonstrate that both the asymptotic multiple chi-square test with an appropriate multiplicity adjustment and the bootstrap-based multiple test procedure keep the family-wise error rate approximately at the predefined significance level. The estimation algorithm as well as the implementation of the testing procedures are described in detail and a real-life application is performed on European commodity data.

Suggested Citation

  • Dickhaus, Thorsten & Sirotko-Sibirskaya, Natalia, 2019. "Simultaneous statistical inference in dynamic factor models: Chi-square approximation and model-based bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 30-46.
    • Handle: RePEc:eee:csdana:v:129:y:2019:i:c:p:30-46
    DOI: 10.1016/j.csda.2018.08.012
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    References listed on IDEAS

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