Asymptotic Inference for Common Factor Models in the Presence of Jumps
No HIAS-E-4, Discussion paper series from Hitotsubashi Institute for Advanced Study, Hitotsubashi University
Financial and macroeconomic time-series data often exhibit infrequent but large jumps. Such jumps may be considered as outliers that are independent of the underlying data-generating processes and contaminate inferences on their model. In this study, we investigate the effects of such jumps on asymptotic inference for large-dimensional common factor models. We first derive the upper bound of jump magnitudes with which the standard asymptotic inference goes through. Second, we propose a jump-correction method based on a series-by-series outlier detection algorithm without accounting for the factor structure. This method gains standard asymptotic normality for the factor model unless outliers occur at common dates. Finally, we propose a test to investigate whether the jumps at a common date are independent outliers or are of factors. A Monte Carlo experiment confirms that the proposed jump-correction method retrieves good finite sample properties. The proposed test shows good size and power. Two small empirical applications illustrate usefulness of the proposed methods.
Keywords: outliers; large-dimensional common factor models; principal components; jumps (search for similar items in EconPapers)
JEL-codes: C12 C38 (search for similar items in EconPapers)
Pages: 50 p.
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Working Paper: Asymptotic Inference for Common Factor Models in the Presence of Jumps (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:hit:hiasdp:hias-e-4
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