Simultaneous statistical inference in dynamic factor models: Chi-square approximation and model-based bootstrap
Thorsten Dickhaus and
Computational Statistics & Data Analysis, 2019, vol. 129, issue C, 30-46
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.
Keywords: Bootstrap; Empirical Fourier transform; Family-wise error rate; Multiple hypothesis testing; Multivariate chi-square distribution; Wald-type statistic (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:129:y:2019:i:c:p:30-46
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