Testing for Panel Unit Roots in the Presence of an Unknown Structural Break and Cross-Sectional Dependency
AbstractThis paper introduces two different non-parametric tests for panel unit root based on the wavelet decomposition of time series which may be used in the presence of cross-sectional dependency and an unknown structural break in the data. These tests are compared with the parametric IPS test proposed by Im, Pesaran and Shin (1997) and the Wald test suggested by Taylor and Sarno (1998). By means of Monte Carlo simulations, the results shown that the size and power properties of the new non-parametric tests are robust to cross sectional dependency of the error terms. Furthermore, it is shown that the tests may be used when the time series has an unknown structural break. These tests have also shown to have high power against the alternative hypothesis under the above mentioned conditioned, whiles the IPS and the Wald did not have any power to reject the alternative hypothesis in the presence of structural break in the data.
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Bibliographic InfoPaper provided by HUI Research in its series HUI Working Papers with number 63.
Length: 17 pages
Date of creation: 18 Apr 2012
Date of revision:
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Panel unit root test; cross-sectional dependency; structural break; wavelet;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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- M. Hashem Pesaran, 2007.
"A simple panel unit root test in the presence of cross-section dependence,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
- Pesaran, M.H., 2003. "A Simple Panel Unit Root Test in the Presence of Cross Section Dependence," Cambridge Working Papers in Economics 0346, Faculty of Economics, University of Cambridge.
- Gencay, Ramazan & Fan, Yanqin, 2007.
"Unit Root Tests with Wavelets,"
9832, University Library of Munich, Germany.
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- Ghazi Shukur & Panagiotis Mantalos, 2000. "A simple investigation of the Granger-causality test in integrated-cointegrated VAR systems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(8), pages 1021-1031.
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