Linear double autoregression
Yao Zheng and
Journal of Econometrics, 2018, vol. 207, issue 1, 162-174
This paper proposes the linear double autoregression, a conditional heteroscedastic model with a conditional mean structure but compatible with the quantile regression. The existence of a strictly stationary solution is discussed, for which a necessary and sufficient condition is established. A doubly weighted quantile regression estimation procedure is introduced, where the first set of weights ensures the asymptotic normality of the estimator and the second set improves its efficiency through balancing individual quantile regression estimators across multiple quantile levels. Bayesian information criteria are proposed for model selection, and two goodness-of-fit tests are constructed to check the adequacy of the fitted conditional mean and conditional scale structures. Simulation studies indicate that the proposed inference tools perform well in finite samples, and an empirical example illustrates the usefulness of the new model.
Keywords: Conditional quantile estimation; Goodness-of-fit test; Heavy tail; Nonlinear time series model; Stationary solution (search for similar items in EconPapers)
JEL-codes: C15 C22 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:207:y:2018:i:1:p:162-174
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