A Simple Asymptotically F-Distributed Portmanteau Test for Diagnostic Checking of Time Series Models With Uncorrelated Innovations

We propose a simple asymptotically F-distributed portmanteau test for diagnostically checking whether the innovations in a parametric time series model are uncorrelated while allowing them to exhibit higher-order dependence of unknown forms. A transform of sample residual autocovariances removing th...

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Vydané v:Journal of business & economic statistics Ročník 40; číslo 2; s. 505 - 521
Hlavní autori: Wang, Xuexin, Sun, Yixiao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Alexandria Taylor & Francis 03.04.2022
Taylor & Francis Ltd
Predmet:
F-distribution
Fixed-smoothing asymptotics
Model diagnostics
Orthonormal series variance estimator
Parameter estimation uncertainty
Uncorrelated innovations
ISSN:0735-0015, 1537-2707
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Shrnutí:We propose a simple asymptotically F-distributed portmanteau test for diagnostically checking whether the innovations in a parametric time series model are uncorrelated while allowing them to exhibit higher-order dependence of unknown forms. A transform of sample residual autocovariances removing the influence of parameter estimation uncertainty makes the test simple. Further, by employing the orthonormal series variance estimator, a special sample autocovariances estimator that is asymptotically invariant to parameter estimation uncertainty, we show that the proposed test statistic is asymptotically F-distributed under fixed-smoothing asymptotics. The asymptotic F-theory accounts for the estimation error of the variance estimator that the asymptotic chi-squared theory ignores. Moreover, an extensive Monte Carlo study demonstrates that the F-test has more accurate finite sample size than existing tests with virtually no power loss. An application to S&P 500 returns illustrates the merits of the proposed methodology.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.2020.1832505