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Recovering cointegration via wavelets in the presence of non-linear patterns

Author

Listed:
  • Martínez Compains Jorge
  • 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)

  • Trani Tommaso
  • Ramos Vilardell Daniel

    (School of Economics and Business Administration, Universidad de Navarra, Pamplona, Spain)

Abstract

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.

Suggested Citation

  • Martínez Compains Jorge & Gençay Ramazan & Rodríguez Carreño Ignacio & Trani Tommaso & Ramos Vilardell Daniel, 2021. "Recovering cointegration via wavelets in the presence of non-linear patterns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(5), pages 255-265, December.
    • Handle: RePEc:bpj:sndecm:v:25:y:2021:i:5:p:255-265:n:1
    DOI: 10.1515/snde-2018-0120
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    More about this item

    Keywords

    cointegration; near cointegration; seasonal adjustment; wavelet decomposition;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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