Bootstrap Model Averaging Unit Root Inference

Bruce Hansen () and Jeffrey Racine

Department of Economics Working Papers from McMaster University

Abstract: Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this paper, we adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, we leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

Keywords: inference; model selection; size distortion; time series. (search for similar items in EconPapers)
Pages: 20 pages
Date: 2018-04
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 (4)

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Related works:
Chapter: Bootstrap Model Averaging Unit Root Inference (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:mcm:deptwp:2018-09

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