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Testing unit root based on partially adaptive estimation

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  • Lima, Zhijie
  • Lima, Luiz Renato Regis de Oliveira

Abstract

This paper proposes unit root tests based on partially adaptive estimation. The proposed tests provide an intermediate class of inference procedures that are more efficient than the traditional OLS-based methods and simpler than unit root tests based on fully adaptive estimation using nonparametric methods. The limiting distribution of the proposed test is a combination of standard normal and the traditional Dickey-Fuller (DF) distribution, including the traditional ADF test as a special case when using Gaussian density. Taking into account the well documented characteristic of heavy-tail behavior in economic and financial data, we consider unit root tests coupled with a class of partially adaptive M-estimators based on the student-t distributions, which includes the normal distribution as a limiting case. Monte Carlo Experiments indicate that, in the presence of heavy tail distributions or innovations that are contaminated by outliers, the proposed test is more powerful than the traditional ADF test.

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Paper provided by FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil) in its series Economics Working Papers (Ensaios Economicos da EPGE) with number 528.

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Date of creation: 01 Mar 2004
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Handle: RePEc:fgv:epgewp:528

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  1. Campbell, John & Mankiw, Gregory, 1987. "Are Output Fluctuations Transitory?," Scholarly Articles 3122545, Harvard University Department of Economics.
  2. Robert J. Gordon, 1986. "The American Business Cycle: Continuity and Change," NBER Books, National Bureau of Economic Research, Inc, number gord86-1, October.
  3. Cheung, Yin-Wong & Lai, Kon S., 1997. "Bandwidth Selection, Prewhitening, and the Power of the Phillips-Perron Test," Econometric Theory, Cambridge University Press, vol. 13(05), pages 679-691, October.
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Cited by:
  1. Amélie Charles & Olivier Darné, 2010. "A note on the uncertain trend in US real GNP: Evidence from robust unit root test," Working Papers hal-00547737, HAL.
  2. Charles, Amélie & Darné, Olivier, 2012. "Trends and random walks in macroeconomic time series: A reappraisal," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 167-180.

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