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This paper evaluated the performance of OECD's Composite Leading Indicator using the Continuous Wavelet Transform. We used various wavelet tools ‐ wavelet coherency, wavelet phase and wavelet phase difference ‐ to assess the co‐movement between the Composite Leading Indicator and three macroeconomic variables ‐ Industrial Production Index, unemployment rate, and real GDP Growth ‐ at each frequency and moment. We also explored the lead–lag relationship between each pair of variables across time and frequency. We concluded that OECD's Composite Leading Indicator is a useful leading indicator of the Industrial Production Index and can be suited for forecasting the unemployment rate. However, it exhibited poor performance regarding GDP growth. [ABSTRACT FROM AUTHOR]

While we can say that there is a global market for crude oil, we cannot say the same for natural gas. There is a strand of literature that argues that, in the last decades, gas markets have become less regional and more global. We use wavelets to test this hypothesis and conclude otherwise: although the European and Japanese gas markets are significantly synchronized, they are much less than the oil markets, which we take as the benchmark. We also show that the North American gas market fluctuations are independent of the other gas markets. Finally, we show that the existing synchronization between gas markets almost vanishes once one filters out the effect of oil price variations, suggesting that it is the global oil market that connects the regional gas markets. [ABSTRACT FROM AUTHOR]

A body of work using the continuous wavelet transform has been growing. We provide a self-contained summary on its most relevant theoretical results, describe how such transforms can be implemented in practice, and generalize the concept of simple coherency to partial wavelet coherency and multiple wavelet coherency, moving beyond bivariate analysis. We also describe a family of wavelets, which emerges as an alternative to the popular Morlet wavelet, the generalized Morse wavelets. A user-friendly toolbox, with examples, is attached to this paper. [ABSTRACT FROM AUTHOR]

Abstract: Central banks have different objectives in the short and long run. Governments operate simultaneously at different timescales. Many economic processes are the result of the actions of several agents, who have different term objectives. Therefore, a macroeconomic time series is a combination of components operating on different frequencies. Several questions about economic time series are connected to the understanding of the behavior of key variables at different frequencies over time, but this type of information is difficult to uncover using pure time-domain or pure frequency-domain methods. To our knowledge, for the first time in an economic setup, we use cross-wavelet tools to show that the relation between monetary policy variables and macroeconomic variables has changed and evolved with time. These changes are not homogeneous across the different frequencies. [Copyright &y& Elsevier]