In this paper discrete wavelet filtering techniques are applied to decompose macroeconomic data so that they can be simultaneously analyzed in both the time and frequency domains. The MODWT (Maximal Overlap Discrete Wavelet Transform) is applied to U.S. quarterly GDP data from 1947–2012 to...

This paper uses wavelet-based optimal control to simulate fiscal and monetary strategies under different levels of policy restrictions. The model applies the Maximal Overlap Discrete Wavelet Transform (MODWT) to United States quarterly GDP data, and then uses the decomposed variables to build a...

We present a multiscale analysis of the price dynamics of U.S. sector exchange-traded funds (ETFs). Our methodology features a multiscale noise-assisted approach, called the complementary ensemble empirical mode decomposition (CEEMD), that decomposes any financial time series into a number of...

This paper uses a frequency domain approach to gain insight into the correlation between survey indicators and year-on-year GDP growth. Using the Baxter-King filter, we split up each series into three components: a short-term, a business cycle (oscillations between 18 and 96 months) and a...