Using the HEGY Procedure When Not All Roots Are Present
AbstractEmpirical studies have shown little evidence to support the presence of all unit roots present in the filter in quarterly seasonal time series. This paper analyses the performance of the Hylleberg, Engle, Granger and Yoo (1990) (HEGY) procedure when the roots under the null are not all present. We exploit the Vector of Quarters representation and cointegration relationship between the quarters when factors 4 (1 L),(1+ L), (1+ L2 ), (1 L2 ) and (1+ L + L2 + L3 ) are a source of nonstationarity in a process in order to obtain the distribution of tests of the HEGY procedure when the underlying processes have a root at the zero, Nyquist frequency, two complex conjugates of frequency / 2 and two combinations of the previous cases. We show both theoretically and through a Monte-Carlo analysis that the t-ratios and and the F-type tests used in the HEGY procedure have the same distribution as under the null of a seasonal random walk when the root(s) is/are present, although this is not the case for the t-ratio tests associated with unit roots at frequency 1 t 2 t / 2 .
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Bibliographic InfoPaper provided by Universitat de Barcelona. Espai de Recerca en Economia in its series Working Papers in Economics with number 170.
Length: 31 pages
Date of creation: 2007
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Other versions of this item:
- Tomas del Barrio Castro, 2007. "Using the HEGY Procedure When Not All Roots Are Present," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 910-922, November.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-02-10 (All new papers)
- NEP-ECM-2007-02-10 (Econometrics)
- NEP-ETS-2007-02-10 (Econometric Time Series)
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