Asymptotics for estimation of quantile regressions with truncated infinite-dimensional processes

Serguei Zernov, Victoria Zinde-Walsh and John Galbraith

Journal of Multivariate Analysis, 2009, vol. 100, issue 3, 497-508

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, results are not available at a level of generality that accommodates time series models used as finite approximations to processes of potentially unbounded order. 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. We focus on estimation of the model at a given quantile. 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. The results are illustrated with both analytical and simulation examples.

Keywords: 62G20; 62G05; 62J05; Generalized; function; L1-norm; LAD; Quantile; regression (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (3)

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