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Sieve Bootstrap for Strongly Dependent Stationary Processes

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Author Info
George Kapetanios () (Queen Mary, University of London)
Zacharias Psaradakis () (Birkbeck College, University of London)
Abstract

This paper studies the properties of the sieve bootstrap for a class of linear processes which exhibit strong dependence. The sieve bootstrap scheme is based on residual resampling from autoregressive approximations the order of which increases slowly with the sample size. The first-order asymptotic validity of the sieve bootstrap is established in the case of the sample mean and sample autocovariances. The finite-sample properties of the method are also investigated by means of Monte Carlo experiments.

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Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number 552.

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Date of creation: Jan 2006
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Handle: RePEc:qmw:qmwecw:wp552

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Related research
Keywords: Autoregressive approximation Linear process Strong dependence Sieve bootstrap Stationary process

Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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This page was last updated on 2008-10-30.


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