Use and Value of Multiple-Period Forecasts in a Dynamic Model of the Cost-Loss Ratio Situation

Edward S. Epstein Climate Analysis Center, National Weather Service, NOAA, Washington, DC

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Allan H. Murphy Department of Atmospheric Sciences, Oregon State University, Corvallis, 0regon

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Abstract

On most forecasting occasions forecasts are made for several successive periods, but decision-making models have traditionally neglected the impact of the potentially useful information contained in forecasts for periods beyond the initial period. The use and value of multiple-period forecasts are investigated here in the context of a recently developed dynamic model of the basic cost-loss ratio situation. We also extend previous studies of this model by examining the impacts—on forecast use and value—of assuming (i) that weather events in successive periods are dependent and (ii) that the forecasts of interest are expressed in probabilistic terms. In this regard, expressions are derived for the expected expenses associated with the use of climatological, imperfect (categorical or probabilistic) and perfect multiple-period forecasts under conditions of dependence and independence between events.

Numerical results are presented concerning expected expense and economic value, based on artificially generated forecasts that incorporate the effects of the decrease in forecast quality as lead time increases. Comparisons are made between multiple-period and single-period forecasts, between dependent and independent events, and between probabilistic and categorical forecasts. For some values of the relevant parameters (e.g., cost-loss ratio, climatological probability), the availability of information for longer lead times can substantially increase economic value. It appears, however, that (i) current imperfect forecasts achieve relatively little of this potential value and (ii) improvements in forecasts at longer lead times must be accompanied by improvements at the shortest lead times for these benefits to be realized. Dependence (i.e., persistence) between events generally reduces weather-related expected expenses, sometimes quite substantially, and consequently reduces forecast value. The results also demonstrate once again the potential economic benefits of expressing forecasts in a probabilistic rather than a categorical formal.

Abstract

On most forecasting occasions forecasts are made for several successive periods, but decision-making models have traditionally neglected the impact of the potentially useful information contained in forecasts for periods beyond the initial period. The use and value of multiple-period forecasts are investigated here in the context of a recently developed dynamic model of the basic cost-loss ratio situation. We also extend previous studies of this model by examining the impacts—on forecast use and value—of assuming (i) that weather events in successive periods are dependent and (ii) that the forecasts of interest are expressed in probabilistic terms. In this regard, expressions are derived for the expected expenses associated with the use of climatological, imperfect (categorical or probabilistic) and perfect multiple-period forecasts under conditions of dependence and independence between events.

Numerical results are presented concerning expected expense and economic value, based on artificially generated forecasts that incorporate the effects of the decrease in forecast quality as lead time increases. Comparisons are made between multiple-period and single-period forecasts, between dependent and independent events, and between probabilistic and categorical forecasts. For some values of the relevant parameters (e.g., cost-loss ratio, climatological probability), the availability of information for longer lead times can substantially increase economic value. It appears, however, that (i) current imperfect forecasts achieve relatively little of this potential value and (ii) improvements in forecasts at longer lead times must be accompanied by improvements at the shortest lead times for these benefits to be realized. Dependence (i.e., persistence) between events generally reduces weather-related expected expenses, sometimes quite substantially, and consequently reduces forecast value. The results also demonstrate once again the potential economic benefits of expressing forecasts in a probabilistic rather than a categorical formal.

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