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

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.

Multiscale Interdependence Between Consumer and Producer Prices in Emerging Eastern European Countries

“…For our research, we use MODWT, which is a linear fi ltering operation that transforms series into coeffi cients related to variations over a set of scales. A number of researchers have used wavelet methodology for analysis of various economic phenomena (see, e.g., Dajčman, 2013;Živkov et al, 2019;Bhuiyan et al, 2019;Compains et al, 2021).…”