Asymptotics of quantiles and rank scores in nonlinear time series

Mukherjee, Kanchan (1999) Asymptotics of quantiles and rank scores in nonlinear time series. Journal of Time Series Analysis, 20 (2). pp. 173-192. ISSN 0143-9782

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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®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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Time Series Analysis
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
65672
Deposited By:
Deposited On:
15 Jul 2013 09:11
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jan 2020 08:34