User’s Guide : Basic Data Analysis : Series : Wavelet Analysis
Wavelet Analysis
Many economic time series feature time-varying characteristics such as non-stationarity, volatility, seasonality, and structural discontinuities. Wavelet analysis is a natural framework for analyzing these phenomena without imposing simplifying assumptions such as stationarity. In particular, wavelet filters can decompose and reconstruct a time series, as well as its correlation structure, across time scales.
While wavelets have applications in diverse areas such as such as regression, unit root testing, bootstrapping (wavestrapping), and fractional integration order estimation, EViews focuses on four popular areas of application:
Wavelet transforms
Variance decomposition
Outlier detection
Thresholding (denoising)
Extensive discussion of wavelet analysis is provided in “Wavelet Analysis”.