An automatic Portmanteau test for serial correlation

Escanciano, Juan Carlos; Lobato, Ignacio N.

doi:10.1016/j.jeconom.2009.03.001

Cited by 227 publications

(205 citation statements)

References 63 publications

(87 reference statements)

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“…The monthly data is obtained from the Federal Reserve Bank of St. Louis FRED economic data 1 . We use AVR (Choi, 1999;Kim, 2009) and AQ (Escanciano and Lobato, 2009). The holding period value of AVR is chosen by a data driven dependent choice procedure.…”

Section: Methodsmentioning

confidence: 99%

Adaptive Market Hypothesis: Evidence from three centuries of UK data

Almail¹,

Almudhaf

2017

Econ Bus Lett

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Section: Methodsmentioning

confidence: 99%

Adaptive Market Hypothesis: Evidence from three centuries of UK data

Almail¹,

Almudhaf

2017

Econ Bus Lett

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“…One of the most popular approaches is to establish the asymptotic normality of a normalized portmanteau test statistic. An incomplete list in this endeavour includes Durlauf (1991), Romano & Thombs (1996), Deo (2000), Lobato (2001), Francq et al (2005), Escanciano & Lobato (2009) and Shao (2011). However, the convergence is typically slow.…”

Section: Introductionmentioning

confidence: 99%

Testing for high-dimensional white noise using maximum cross-correlations

Chang¹,

Yao²,

Zhou³

2017

Biometrika

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SUMMARYWe propose a new omnibus test for vector white noise using the maximum absolute autocorrelations and cross-correlations of the component series. Based on an approximation by the L ∞ -norm of a normal random vector, the critical value of the test can be evaluated by bootstrapping from a multivariate normal distribution. In contrast to the conventional white noise test, the new method is proved to be valid for testing the departure from white noise that is not independent and identically distributed. We illustrate the accuracy and the power of the proposed test by simulation, which also shows that the new test outperforms several commonly used methods including, for example, the Lagrange multiplier test and the multivariate Box-Pierce portmanteau tests, especially when the dimension of time series is high in relation to the sample size. The numerical results also indicate that the performance of the new test can be further enhanced when it is applied to pre-transformed data obtained via the time series principal component analysis proposed by Chang, Yao (arXiv:1410.2323). The proposed procedures have been implemented in an R package.

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“…Consider the work of Escanciano and Lobato (2009) for univariate time series and the extension to multivariate time series given by Escanciano et al (2013). They propose a method that automatically selects the max-imum lag for Q m based on a penalty term that relates to the well-known AIC and BIC criteria.…”

Section: Other Methodsmentioning

confidence: 99%