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Asymptotics For Estimation Of Truncated Infinite-Dimensional Quantile Regressions

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Author Info
Serguei Zernov ()
Victoria Zindle-Walsh ()
John Galbraith ()

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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.

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Paper provided by McGill University, Department of Economics in its series Departmental Working Papers with number 2006-16.

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Length: 13 pages
Date of creation: Aug 2006
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Handle: RePEc:mcl:mclwop:2006-16

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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    Other versions:
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