Estimation methods for the LRD parameter under a change in the mean

Rooch, Aeneas; Zelo, I.; Fried, Roland

doi:10.1007/s00362-016-0839-7

Cited by 8 publications

(4 citation statements)

References 48 publications

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“…In (A1), condition P P = I r ensures identiability in (1), see (Peña and Box, 1987) and (Lam and Yao, 2012) for further details. Assumption (A2) means that Y t is a stationary long-range dependent process see, for example, (Rooch et al, 2019). It follows from ( 1) and (A1) that…”

Section: Modelmentioning

confidence: 99%

A dimension reduction factor approach for multivariate time series with long-memory: a robust alternative method

Reisen,

Lévy-Leduc,

Monte

et al. 2023

Stat Papers

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This paper studies factor modeling for a vector of time series with long-memory properties to investigate how outliers aect the identication of the number of factors and also proposes a robust method to reduce their impact. The number of factors is estimated using an eigenvalue analysis for a non-negative denite matrix introduced by Lam et al (2011). Two estimators are proposed; the rst is based on the classical sample covariance function, and the second uses a robust covariance function estimate. In both cases, it is shown that the eigenvalues estimates have similar convergence rates. Empirical simulations support both estimators for multivariate stationary longmemory time series and show that the robust method is preferable when the data is contaminated with additive outliers. Time series of daily log returns are used as an example of application. In addition to abrupt observations, exchange rates exhibit non-stationarity properties with long memory parameters greater than one. Then we use semi-parametric long memory estimators to estimate the fractional parameters of the series. The number of factors was estimated using the classical and robust approaches. Due to the inuence of the abrupt observations, these tools suggested a dierent number of factors to model the data. The robust method suggested two factors, while the classical approach indicated only one factor.

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Section: Modelmentioning

confidence: 99%

A dimension reduction factor approach for multivariate time series with long-memory: a robust alternative method

Reisen,

Lévy-Leduc,

Monte

et al. 2023

Stat Papers

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show abstract

“…In order to make use of the full data set in spite of a possible level shift, we split the time series into several non-overlapping subsequences and estimate the parameters of interest from each of them separately. This approach has been analysed by [23] for estimation of the Hurst parameter and by [3] for estimation of the variance. Here, we split the time series into five subsequences of length 28 each, which fits well to the total sample size of 140 and also to a possible level shift right at the end of the third subsequence.…”

Section: Scenarios With a Patch Of Additive Outliersmentioning

confidence: 99%

On robust estimation of negative binomial INARCH models

Elsaied

Fried

2021

METRON

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We discuss robust estimation of INARCH models for count time series, where each observation conditionally on its past follows a negative binomial distribution with a constant scale parameter, and the conditional mean depends linearly on previous observations. We develop several robust estimators, some of them being computationally fast modifications of methods of moments, and some rather efficient modifications of conditional maximum likelihood. These estimators are compared to related recent proposals using simulations. The usefulness of the proposed methods is illustrated by a real data example.

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“…To exploit this fact, we propose a block procedure to filter out the unknown trend component. Blocking was also used in Rooch et al (2019) to estimate the fractional integration parameter in a similar situation. We divide the series into T − B overlapping blocks of length B .…”

Section: Introductionmentioning

confidence: 99%

Unit root testing with slowly varying trends

Otto

2020

Journal Time Series Analysis

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A unit root test is proposed for time series with a general nonlinear deterministic trend component. It is shown that asymptotically the pooled OLS estimator of overlapping blocks filters out any trend component that satisfies some Lipschitz condition. Under both fixed-b and small-b block asymptotics, the limiting distribution of the t-statistic for the unit root hypothesis is derived. Nuisance parameter corrections provide heteroskedasticity-robust tests, and serial correlation is accounted for by pre-whitening. A Monte Carlo study that considers slowly varying trends yields both good size and improved power results for the proposed tests when compared to conventional unit root tests.

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Estimation methods for the LRD parameter under a change in the mean

Cited by 8 publications

References 48 publications

A dimension reduction factor approach for multivariate time series with long-memory: a robust alternative method

A dimension reduction factor approach for multivariate time series with long-memory: a robust alternative method

On robust estimation of negative binomial INARCH models

Unit root testing with slowly varying trends

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