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Generalized R-estimators under Conditional heteroscedasticity.

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Generalized R-estimators under Conditional heteroscedasticity. / Mukherjee, Kanchan.
In: Journal of Econometrics, Vol. 141, No. 2, 12.2007, p. 383-415.

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

Harvard

Mukherjee, K 2007, 'Generalized R-estimators under Conditional heteroscedasticity.', Journal of Econometrics, vol. 141, no. 2, pp. 383-415. https://doi.org/10.1016/j.jeconom.2006.10.002

APA

Mukherjee, K. (2007). Generalized R-estimators under Conditional heteroscedasticity. Journal of Econometrics, 141(2), 383-415. https://doi.org/10.1016/j.jeconom.2006.10.002

Vancouver

Mukherjee K. Generalized R-estimators under Conditional heteroscedasticity. Journal of Econometrics. 2007 Dec;141(2):383-415. doi: 10.1016/j.jeconom.2006.10.002

Author

Mukherjee, Kanchan. / Generalized R-estimators under Conditional heteroscedasticity. In: Journal of Econometrics. 2007 ; Vol. 141, No. 2. pp. 383-415.

Bibtex

@article{5d05124d0a4440bfa4014b8d46e1cac3,
title = "Generalized R-estimators under Conditional heteroscedasticity.",
abstract = "In this paper, we extend the classical idea of Rank estimation of parameters from homoscedastic problems to heteroscedastic problems. In particular, we define a class of rank estimators of the parameters associated with the conditional mean function of an autoregressive model through a three-steps procedure and then derive their asymptotic distributions. The class of models considered includes Engel's ARCH model and the threshold heteroscedastic model. The class of estimators includes an extension of Wilcoxon-type rank estimator. The derivation of the asymptotic distributions depends on the uniform approximation of a randomly weighted empirical process by a perturbed empirical process through a very general weight-dependent partitioning argument.",
keywords = "Rank estimation, Heteroscedastic model, Weighted empirical process, Uniform approximation",
author = "Kanchan Mukherjee",
note = "The final, definitive version of this article has been published in the Journal, Journal of Econometrics 141 (2), 2007, {\textcopyright} ELSEVIER.",
year = "2007",
month = dec,
doi = "10.1016/j.jeconom.2006.10.002",
language = "English",
volume = "141",
pages = "383--415",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "2",

}

RIS

TY - JOUR

T1 - Generalized R-estimators under Conditional heteroscedasticity.

AU - Mukherjee, Kanchan

N1 - The final, definitive version of this article has been published in the Journal, Journal of Econometrics 141 (2), 2007, © ELSEVIER.

PY - 2007/12

Y1 - 2007/12

N2 - In this paper, we extend the classical idea of Rank estimation of parameters from homoscedastic problems to heteroscedastic problems. In particular, we define a class of rank estimators of the parameters associated with the conditional mean function of an autoregressive model through a three-steps procedure and then derive their asymptotic distributions. The class of models considered includes Engel's ARCH model and the threshold heteroscedastic model. The class of estimators includes an extension of Wilcoxon-type rank estimator. The derivation of the asymptotic distributions depends on the uniform approximation of a randomly weighted empirical process by a perturbed empirical process through a very general weight-dependent partitioning argument.

AB - In this paper, we extend the classical idea of Rank estimation of parameters from homoscedastic problems to heteroscedastic problems. In particular, we define a class of rank estimators of the parameters associated with the conditional mean function of an autoregressive model through a three-steps procedure and then derive their asymptotic distributions. The class of models considered includes Engel's ARCH model and the threshold heteroscedastic model. The class of estimators includes an extension of Wilcoxon-type rank estimator. The derivation of the asymptotic distributions depends on the uniform approximation of a randomly weighted empirical process by a perturbed empirical process through a very general weight-dependent partitioning argument.

KW - Rank estimation

KW - Heteroscedastic model

KW - Weighted empirical process

KW - Uniform approximation

U2 - 10.1016/j.jeconom.2006.10.002

DO - 10.1016/j.jeconom.2006.10.002

M3 - Journal article

VL - 141

SP - 383

EP - 415

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -