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Asymptotics of quantiles and rank scores in nonlinear time series

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Asymptotics of quantiles and rank scores in nonlinear time series. / Mukherjee, Kanchan.
In: Journal of Time Series Analysis, Vol. 20, No. 2, 03.1999, p. 173-192.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Mukherjee, K 1999, 'Asymptotics of quantiles and rank scores in nonlinear time series', Journal of Time Series Analysis, vol. 20, no. 2, pp. 173-192. https://doi.org/10.1111/1467-9892.00132

APA

Mukherjee, K. (1999). Asymptotics of quantiles and rank scores in nonlinear time series. Journal of Time Series Analysis, 20(2), 173-192. https://doi.org/10.1111/1467-9892.00132

Vancouver

Mukherjee K. Asymptotics of quantiles and rank scores in nonlinear time series. Journal of Time Series Analysis. 1999 Mar;20(2):173-192. doi: 10.1111/1467-9892.00132

Author

Mukherjee, Kanchan. / Asymptotics of quantiles and rank scores in nonlinear time series. In: Journal of Time Series Analysis. 1999 ; Vol. 20, No. 2. pp. 173-192.

Bibtex

@article{29d399d8e4d442d78a9b4fed7b5f66fa,
title = "Asymptotics of quantiles and rank scores in nonlinear time series",
abstract = "This paper extends the concept of regression and autoregression quantiles and rank scores to a very general nonlinear time series model. The asymptotic linearizations of these nonlinear quantiles are then used to obtain the limiting distributions of a class of L-estimators of the parameters. In particular, the limiting distributions of the least absolute deviation estimator and trimmed estimators are obtained. These estimators turn out to be asymptotically more ef{\textregistered}cient than the widely used conditional least squares estimator for heavy-tailed error distributions. The results are applicable to linear and nonlinear regression and autoregressive models including self-exciting threshold autoregressive models with known threshold.",
keywords = "Nonlinear time series models , SETAR models , regression and autoregression",
author = "Kanchan Mukherjee",
year = "1999",
month = mar,
doi = "10.1111/1467-9892.00132",
language = "English",
volume = "20",
pages = "173--192",
journal = "Journal of Time Series Analysis",
issn = "0143-9782",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Asymptotics of quantiles and rank scores in nonlinear time series

AU - Mukherjee, Kanchan

PY - 1999/3

Y1 - 1999/3

N2 - This paper extends the concept of regression and autoregression quantiles and rank scores to a very general nonlinear time series model. The asymptotic linearizations of these nonlinear quantiles are then used to obtain the limiting distributions of a class of L-estimators of the parameters. In particular, the limiting distributions of the least absolute deviation estimator and trimmed estimators are obtained. These estimators turn out to be asymptotically more ef®cient than the widely used conditional least squares estimator for heavy-tailed error distributions. The results are applicable to linear and nonlinear regression and autoregressive models including self-exciting threshold autoregressive models with known threshold.

AB - This paper extends the concept of regression and autoregression quantiles and rank scores to a very general nonlinear time series model. The asymptotic linearizations of these nonlinear quantiles are then used to obtain the limiting distributions of a class of L-estimators of the parameters. In particular, the limiting distributions of the least absolute deviation estimator and trimmed estimators are obtained. These estimators turn out to be asymptotically more ef®cient than the widely used conditional least squares estimator for heavy-tailed error distributions. The results are applicable to linear and nonlinear regression and autoregressive models including self-exciting threshold autoregressive models with known threshold.

KW - Nonlinear time series models

KW - SETAR models

KW - regression and autoregression

U2 - 10.1111/1467-9892.00132

DO - 10.1111/1467-9892.00132

M3 - Journal article

VL - 20

SP - 173

EP - 192

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

IS - 2

ER -