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On the Choice of Test for a Unit Root when the Errors are Conditionally Heteroskedastic

In English

Författare

Summary, in English

It is well known that in the context of the classical regression model with heteroskedastic errors, while ordinary least squares (OLS) is not efficient, the weighted least squares (WLS) and quasi-maximum likelihood (QML) estimators that utilize the information contained in the heteroskedasticity are. In the context of unit root testing with conditional heteroskedasticity, while intuition suggests that a similar result should apply, the relative performance of the tests associated with the OLS, WLS and QML estimators is not well understood. In particular, while QML has been shown to be able to generate more powerful tests than OLS, not much is known regarding the relative performance of the WLS-based test. By providing an in-depth comparison of the tests, the current paper fills this gap in the literature.

Publiceringsår

2014

Språk

Engelska

Sidor

40-53

Publikation/Tidskrift/Serie

Computational Statistics & Data Analysis

Volym

69

Issue

January

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Economics

Nyckelord

  • Unit root test
  • Conditional heteroskedasticity
  • ARCH

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 0167-9473