ARMA Sieve bootstrap unit root tests

Patrick Richard

Cahiers de recherche from Departement d'économique de l'École de gestion à l'Université de Sherbrooke

Abstract: Augmented Dickey-Fuller unit root tests may severely overreject when the DGP is a general linear process. The use of the AR sieve bootstrap, proposed by Park (2002) and Chang and Park (2003), may alleviate this problem. We propose sieve bootstraps based on MA and ARMA approximations. Invariance principles for the partial sum processes built from these sieve bootstrap DGPs are established and a proof of the asymptotic validity of the resulting ADF bootstrap tests is provided. Through Monte Carlo experiments, we find that the rejection probabilities of the MA and ARMA sieve bootstraps are often lower and more robust to the underlying DGP than that of the AR sieve bootstrap. In particular, the new sieve bootstraps perform much better than the AR sieve when a large MA root is present. We also find that the ARMA sieve bootstrap requires only a very parsimonious specification to achieve excellent results.

Keywords: Sieve bootstrap; Unit root; ADF tests; ARMA approximations; Invariance Principle (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2007, Revised 2009-07
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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http://gredi.recherche.usherbrooke.ca/wpapers/GREDI-0705.pdf First version, 2007 (application/pdf)
http://gredi.recherche.usherbrooke.ca/wpapers/GREDI-0705R.pdf Revised version, July 2009 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:shr:wpaper:07-05

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