Recovering cointegration via wavelets in the presence of non-linear patterns
Martínez Compains Jorge (),
Rodríguez Carreño Ignacio (),
Trani Tommaso and
Ramos Vilardell Daniel
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Gençay Ramazan: Department of Economics, Simon Fraser University, Vancouver, Canada
Rodríguez Carreño Ignacio: School of Economics and Business Administration, DATAI, Data Science and Artificial Intelligence Institute, Universidad de Navarra, Pamplona, Spain
Ramos Vilardell Daniel: School of Economics and Business Administration, Universidad de Navarra, Pamplona, Spain
Studies in Nonlinear Dynamics & Econometrics, 2021, vol. 25, issue 5, 255-265
Johansen’s Cointegration Test (JCT) performs remarkably well in finding stable bivariate cointegration relationships. Nonetheless, the JCT is not necessarily designed to detect such relationships in presence of non-linear patterns such as structural breaks or cycles that fall in the low frequency portion of the spectrum. Seasonal adjustment procedures might not detect such non-linear patterns, and thus, we expose the difficulty in identifying cointegrating relations under the traditional use of JCT. Within several Monte Carlo experiments, we show that wavelets can empower more the JCT framework than the traditional seasonal adjustment methodologies, allowing for identification of hidden cointegrating relationships. Moreover, we confirm these results using seasonally adjusted time series as US consumption and income, gross national product (GNP) and money supply M1 and GNP and M2.
Keywords: cointegration; near cointegration; seasonal adjustment; wavelet decomposition (search for similar items in EconPapers)
JEL-codes: C01 C12 C22 (search for similar items in EconPapers)
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