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Estimating long memory in panel random-coefficient AR(1) data

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Estimating long memory in panel random-coefficient AR(1) data. / Leipus, Remigijus; Philippe, Anne; Pilipauskaite, Vytaute; Surgailis, Donatas.

In: Journal of Time Series Analysis, Vol. 41, No. 4, 07.2020, p. 520-535.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Leipus, R, Philippe, A, Pilipauskaite, V & Surgailis, D 2020, 'Estimating long memory in panel random-coefficient AR(1) data', Journal of Time Series Analysis, vol. 41, no. 4, pp. 520-535. https://doi.org/10.1111/jtsa.12519

APA

Leipus, R., Philippe, A., Pilipauskaite, V., & Surgailis, D. (2020). Estimating long memory in panel random-coefficient AR(1) data. Journal of Time Series Analysis, 41(4), 520-535. https://doi.org/10.1111/jtsa.12519

CBE

Leipus R, Philippe A, Pilipauskaite V, Surgailis D. 2020. Estimating long memory in panel random-coefficient AR(1) data. Journal of Time Series Analysis. 41(4):520-535. https://doi.org/10.1111/jtsa.12519

MLA

Leipus, Remigijus et al. "Estimating long memory in panel random-coefficient AR(1) data". Journal of Time Series Analysis. 2020, 41(4). 520-535. https://doi.org/10.1111/jtsa.12519

Vancouver

Leipus R, Philippe A, Pilipauskaite V, Surgailis D. Estimating long memory in panel random-coefficient AR(1) data. Journal of Time Series Analysis. 2020 Jul;41(4):520-535. https://doi.org/10.1111/jtsa.12519

Author

Leipus, Remigijus ; Philippe, Anne ; Pilipauskaite, Vytaute ; Surgailis, Donatas. / Estimating long memory in panel random-coefficient AR(1) data. In: Journal of Time Series Analysis. 2020 ; Vol. 41, No. 4. pp. 520-535.

Bibtex

@article{9c040848ef7c475d9a7f35a6a2a169a1,
title = "Estimating long memory in panel random-coefficient AR(1) data",
abstract = "We construct an asymptotically normal estimator (Formula presented.) for the tail index β of a distribution on (0,1) regularly varying at x=1, when its N independent realizations are not directly observable. The estimator (Formula presented.) is a version of the tail index estimator of Goldie and Smith (1987) [Goldie CM, Smith RL. 1987. The Quarterly Journal of Mathematics 38: 45–71] based on suitably truncated observations contaminated with arbitrarily dependent {\textquoteleft}noise{\textquoteright} which vanishes as N increases. We apply (Formula presented.) to panel data comprising N random-coefficient AR(1) series, each of length T, for estimation of the tail index of the random coefficient at the unit root, in which case the unobservable random coefficients are replaced by sample lag 1 autocorrelations of individual time series. Using asymptotic normality of (Formula presented.), we construct a statistical procedure to test if the panel random-coefficient AR(1) data exhibit long memory. A simulation study illustrates finite-sample performance of the introduced inference procedures.",
keywords = "Random-coefficient autoregression, long memory process, measurement error, panel data, tail index estimator",
author = "Remigijus Leipus and Anne Philippe and Vytaute Pilipauskaite and Donatas Surgailis",
year = "2020",
month = jul,
doi = "10.1111/jtsa.12519",
language = "English",
volume = "41",
pages = "520--535",
journal = "Journal of Time Series Analysis",
issn = "0143-9782",
publisher = "Wiley-Blackwell Publishing Ltd.",
number = "4",

}

RIS

TY - JOUR

T1 - Estimating long memory in panel random-coefficient AR(1) data

AU - Leipus, Remigijus

AU - Philippe, Anne

AU - Pilipauskaite, Vytaute

AU - Surgailis, Donatas

PY - 2020/7

Y1 - 2020/7

N2 - We construct an asymptotically normal estimator (Formula presented.) for the tail index β of a distribution on (0,1) regularly varying at x=1, when its N independent realizations are not directly observable. The estimator (Formula presented.) is a version of the tail index estimator of Goldie and Smith (1987) [Goldie CM, Smith RL. 1987. The Quarterly Journal of Mathematics 38: 45–71] based on suitably truncated observations contaminated with arbitrarily dependent ‘noise’ which vanishes as N increases. We apply (Formula presented.) to panel data comprising N random-coefficient AR(1) series, each of length T, for estimation of the tail index of the random coefficient at the unit root, in which case the unobservable random coefficients are replaced by sample lag 1 autocorrelations of individual time series. Using asymptotic normality of (Formula presented.), we construct a statistical procedure to test if the panel random-coefficient AR(1) data exhibit long memory. A simulation study illustrates finite-sample performance of the introduced inference procedures.

AB - We construct an asymptotically normal estimator (Formula presented.) for the tail index β of a distribution on (0,1) regularly varying at x=1, when its N independent realizations are not directly observable. The estimator (Formula presented.) is a version of the tail index estimator of Goldie and Smith (1987) [Goldie CM, Smith RL. 1987. The Quarterly Journal of Mathematics 38: 45–71] based on suitably truncated observations contaminated with arbitrarily dependent ‘noise’ which vanishes as N increases. We apply (Formula presented.) to panel data comprising N random-coefficient AR(1) series, each of length T, for estimation of the tail index of the random coefficient at the unit root, in which case the unobservable random coefficients are replaced by sample lag 1 autocorrelations of individual time series. Using asymptotic normality of (Formula presented.), we construct a statistical procedure to test if the panel random-coefficient AR(1) data exhibit long memory. A simulation study illustrates finite-sample performance of the introduced inference procedures.

KW - Random-coefficient autoregression

KW - long memory process

KW - measurement error

KW - panel data

KW - tail index estimator

U2 - 10.1111/jtsa.12519

DO - 10.1111/jtsa.12519

M3 - Journal article

VL - 41

SP - 520

EP - 535

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

IS - 4

ER -