ASYMPTOTICS FOR ESTIMATION OF TRUNCATED INFINITE-DIMENSIONAL QUANTILE REGRESSIONS

Serguei Zernov (), Victoria Zindle-Walsh () and John Galbraith
Authors registered in the RePEc Author Service: Victoria Zinde-Walsh

Departmental Working Papers from McGill University, Department of Economics

Abstract: Many processes can be represented in a simple form as infinite-order linear series. In such cases, an approximate model is often derived as a truncation of the infinite-order process, for estimation on the finite sample. The literature contains a number of asymptotic distributional results for least squares estimation of such finite truncations, but for quantile estimation, only results for finite-order processes are available at a level of generality that accommodates time series processes. Here we establish consistency and asymptotic normality for conditional quantile estimation of truncations of such infinite-order linear models, with the truncation order increasing in sample size. The proofs use the generalized functions approach and allow for a wide range of time series models as well as other forms of regression model. As an example, many time series processes may be represented as an AR(?) or an MA(?); here we use a simulation to illustrate the degree of conformity of finite sample results with the asymptotics, in case of a truncated AR representation of a moving average.

JEL-codes: C13 C22 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2006-08
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:mcl:mclwop:2006-16

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