Using wavelets to decompose time-frequency economic relations

Economic agents simultaneously operate at different horizons. Many economic processes are the result of the actions of several agents with different term objectives. Therefore, economic time-series is a combination of components operating on different frequencies. Several questions about the data are connected to the understanding of the time-series behavior at different frequencies. While Fourier analysis is not appropriate to study the cyclical nature of economic time-series, because these are rarely stationary, wavelet analysis performs the estimation of the spectral characteristics of a time-series as a function of time. In spite of all its advantages, wavelets are hardly ever used in economics. The purpose of this paper is to show that cross wavelet analysis can be used to directly study the interactions different time-series in the time-frequency domain. We use wavelets to analyze the impact of interest rate price changes on some macroeconomic variables: Industrial Production, Inflation and the monetary aggregates M1 and M2. Specifically, three tools are utilized: the wavelet power spectrum, wavelet coherency and wavelet phase-difference. These instruments illustrate how the use of wavelets may help to unravel economic time-frequency relations that would otherwise remain hidden