Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Moving averages are a common method of forecasting futures basis. We argue that the optimal lengths of moving averages depend on the frequency of structural breaks. A new stochastic time-series process including structural breaks is modelled by discrete probability distributions that capture the frequency and size of structural breaks. A permanent shock (means structural breaks in this article) is captured by a Poisson-jump or a Bernoulli-jump process, and a temporary shock is represented by a white noise process. Futures basis data are used to estimate the frequency of permanent shocks as well as the size of both shocks. Most shocks are permanent shocks. Since most shocks are permanent, the most recent year provides the best forecast and the optimal length of the moving average is one.