Weak Dependence: Models and Applications

Patrick Ango Nze () and Paul Doukhan ()
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Patrick Ango Nze: Université Lille 3, UFR AES
Paul Doukhan: Université de Cergy Pontoise Bâtiment Les Chênes, UPRESA 8088 CNRS-Mathématiques

A chapter in Empirical Process Techniques for Dependent Data, 2002, pp 117-136 from Springer

Abstract: Abstract This paper aims at a systematic introduction to a new weak dependence condition. We show that some standard models satisfy this property, including stationary Markov models, bilinear models, and more generally, Bernoulli shifts. In some cases no mixing properties can be expected without additional regularity assumption on the distribution of the innovations distribution for which a weak dependence condition can be easily derived. We apply the theory to derive a weak Donsker invariance principle and the empirical CLT and we present an application to kernel estimates for density and regression functions.

Keywords: Central Limit Theorem; Stationary Sequence; Weak Dependence; Empirical Process; Bernoulli Shift (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-0099-4_2

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DOI: 10.1007/978-1-4612-0099-4_2

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