A test for additive outliers applicable to long-memory time series

Chareka, Patrick; Matarise, Florance; Turner, Rolf

doi:10.1016/j.jedc.2005.01.003

Cited by 16 publications

(24 citation statements)

References 20 publications

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“…The question has been raised as to whether the Nile time series contains outliers; see, for example Beran (1992), Robinson (1995), Chareka et al. (2006) and Fajardo et al.…”

Section: An Applicationmentioning

confidence: 99%

“…(2009). The test procedure developed by Chareka et al. (2006), suggests the presence of outliers at 646 AD ( p ‐value: 0.0308) and at 809 ( p ‐value: 0.0007).…”

Section: An Applicationmentioning

confidence: 99%

“… Left : Classical (plain line) and robust (dotted line) sample autocorrelation functions of the Nile River data. Right : Classical (plain line) and robust (dotted line) sample autocorrelation functions of the Nile River data and classical sample autocorrelation functions of the Nile River data with artificial outliers at the times detected by Chareka et al. (2006; original data plus 5 standard deviations with ‘’ and original data plus 10 standard deviations with ‘’) …”

Section: An Applicationmentioning

confidence: 99%

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Robust estimation of the scale and of the autocovariance function of Gaussian short- and long-range dependent processes

Lévy‐Leduc

Boistard

Moulines

et al. 2010

Journal of Time Series Analysis

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A desirable property of an autocovariance estimator is to be robust to the presence of additive outliers. It is well known that the sample autocovariance, being based on moments, does not have this property. Hence, the use of an autocovariance estimator which is robust to additive outliers can be very useful for time-series modelling. In this article, the asymptotic properties of the robust scale and autocovariance estimators proposed by Rousseeuw and Croux (1993) and Ma and Genton (2000) are established for Gaussian processes, with either short-or long-range dependence. It is shown in the short-range dependence setting that this robust estimator is asymptotically normal at the rate ffiffiffi n p , where n is the number of observations. An explicit expression of the asymptotic variance is also given and compared with the asymptotic variance of the classical autocovariance estimator. In the long-range dependence setting, the limiting distribution displays the same behaviour as that of the classical autocovariance estimator, with a Gaussian limit and rate ffiffiffi n p when the Hurst parameter H is less than 3/4 and with a non-Gaussian limit (belonging to the second Wiener chaos) with rate depending on the Hurst parameter when H 2 (3/4,1). Some Monte Carlo experiments are presented to illustrate our claims and the Nile River data are analysed as an application. The theoretical results and the empirical evidence strongly suggest using the robust estimators as an alternative to estimate the dependence structure of Gaussian processes.

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“…The question has been raised as to whether the Nile time series contains outliers; see, for example Beran (1992), Robinson (1995), Chareka et al. (2006) and Fajardo et al.…”

Section: An Applicationmentioning

confidence: 99%

“…(2009). The test procedure developed by Chareka et al. (2006), suggests the presence of outliers at 646 AD ( p ‐value: 0.0308) and at 809 ( p ‐value: 0.0007).…”

Section: An Applicationmentioning

confidence: 99%

Section: An Applicationmentioning

confidence: 99%

See 1 more Smart Citation

Robust estimation of the scale and of the autocovariance function of Gaussian short- and long-range dependent processes

Lévy‐Leduc

Boistard

Moulines

et al. 2010

Journal of Time Series Analysis

View full text Add to dashboard Cite

show abstract

“…Table 4 Simulation results on the estimation of ; ARFIMA(1,d,0) Various conclusions have been drawn as to whether or not this series contains outliers. For example, Chareka et al (2006) developed a test to identify outliers and ran it on the Nile data. Their test located two outliers at 646 and 809 A.D.…”

Section: Applicationmentioning

confidence: 99%

Robust estimation in long-memory processes under additive outliers

Molinares

Reisen

Cribari‐Neto

2009

Journal of Statistical Planning and Inference

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“…On the other hand, approaches such as Shin et al (1996), Vogelsang (1999) and Perron and Rodríguez (2003) work under the context of unit root series where there is need to difference them to stationarity before outlier detection and parameter estimation. A general approach proposed in Chareka et al (2006) is the pre-filtering or pre-estimation requirement before detecting outliers.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Note about Estimation of the Fractional Parameter under Additive Outliers

Rodríguez

2014

Communications in Statistics - Simulation and Computation

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In recent papers, Fajardo et al. (2009) and Reinsen and Fajardo (2012) propose an alternative semiparametric estimator of the fractional parameter in ARFIMA models which is robust to the presence of additive outliers. The results are very interesting, however, they use samples of 300 or 800 observations which are rarely found in macroeconomics. In order to perform a comparison, I estimate the fractional parameter using the procedure of Geweke and Porter-Hudak (1983) augmented with dummy variables associated with the (previously) detected outliers using the statistic τ d suggested by Perron and Rodríguez (2003). Comparing with Fajardo et al. (2009) and Reinsen and Fajardo (2012), I found better results for the mean and bias of the fractional parameter when T = 100 and the results in terms of the standard deviation and the MSE are very similar. However, for higher sample sizes such as 300 or 800, the robust procedure performs better. Empirical applications for seven monthly Latin-American inflation series with very small sample sizes contaminated by additive outliers are discussed.

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A test for additive outliers applicable to long-memory time series

Cited by 16 publications

References 20 publications

Robust estimation of the scale and of the autocovariance function of Gaussian short- and long-range dependent processes

Robust estimation of the scale and of the autocovariance function of Gaussian short- and long-range dependent processes

Robust estimation in long-memory processes under additive outliers

A Comparative Note about Estimation of the Fractional Parameter under Additive Outliers

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