Optimal selection of forecasts

✍ Scribed by Lian Chen; G. Anandalingam

• Publisher: John Wiley and Sons
• Year: 1990
• Tongue: English
• Weight: 732 KB
• Volume: 9
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/for.3980090307

✦ Synopsis

Many studies have shown that, in general, a combination of forecasts often outperforms the forecasts of a single model or expert. In this paper we postulate that obtaining forecasts is costly, and provide models for optimally selecting them. Based on normality assumptions, we derive a dynamic programming procedure for maximizing precision net of cost. We examine the solution for cases where the forecasters are independent, correlated and biased. We provide illustrative examples for each case.

KEY WORDS Forecast combination Dynamic programming

Bayesian methods summary of the results). As long as each forecasting model or expert provides new information, more reliable forecasts are obtained by combining them. When forecasters are independent, the weight assigned to each is usually inversely proportional to the variance of the past error of forecast (Winkler and Makridakis, 1983) or to the decision maker's perception of the forecasters error-proneness (Anandalingam and Chen, 1989). The expression for the weight assigned to correlated forecasters is more compIicated.