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Asymptotics of quantiles and rank scores in nonlinear time series

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>03/1999
<mark>Journal</mark>Journal of Time Series Analysis
Number of pages20
<mark>Original language</mark>English


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.