This paper shows how to decompose weakly stationary time series into the sum, across time scales, of uncorrelated components associated with different degrees of persistence. In particular, we provide an Extended Wold Decomposition based on an isometric scaling operator that makes averages of...

This paper shows how to decompose weakly stationary time series into the sum, across time scales, of uncorrelated components associated with different degrees of persistence. In particular, we provide an Extended Wold Decomposition based on an isometric scaling operator...

We view economic time series as the result of a cascade of shocks occurring at different times and different frequencies (scales). We suggest that economic relations that are found to be elusive when using raw data may hold true for different layers (details) in the cascade of economic shocks....

We propose a decomposition for time series in components classified by levels of persistence. Employing this decomposition, we provide empirical evidence that consumption growth contains predictable components highly correlated with well-known proxies of consumption variability. These components...

This paper shows how to decompose weakly stationary time series into the sum, across time scales, of uncorrelated components associated with different degrees of persistence. In particular, we provide an Extended Wold Decomposition based on an isometric scaling operator that makes averages of...

The long-run risk model introduced by R.Bansal and A.Yaron (2004) assumes the existence of a small predictable component in consumption growth and an elasticity of intertemporal substitution of the representative agent larger than one for the substitution effect to dominate the income one....

We propose a methodology based on multiresolution analysis to decompose a time series in components classifi ed by their level of persistence. Using this decomposition to detect the layers with diff erent degrees of persistence in consumption growth, we provide empirical evidence that some of...