Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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 generate a term-structure of sizable risk premia. At low frequencies we identify a component correlated with long-run productivity growth and commanding a yearly premium of approximately 2%. At high frequencies we identify a component with yearly half-life, which contributes to the equity premium for another 2%. Accounting for persistence heterogeneity, we obtain an estimate of the IES strictly above one and robust across subsamples. The Author 2013. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.