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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 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.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF BUSINESS AND ECONOMIC STATISTICS; Vol.40 No.2 April 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 505-521

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MODEL DIAGNOSTICS
F-DISTRIBUTION
FIXED-SMOOTHING ASYMPTOTICS
ORTHONORMAL SERIES VARIANCE ESTIMATOR
PARAMETER ESTIMATION UNCERTAINTY
UNCORRELATED INNOVATIONS

Info Detail Spesifik

https://doi.org/10.1080/07350015.2020.1832505

Pernyataan Tanggungjawab

Xuexin Wang, Yixiao Sun

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