How Accurate are Real World Forecasts and Estimates?
Abstract
Modern forecasting and estimation techniques provide not only point estimates of unknown variables, but also associated intervals which reflect the expected accuracy of those estimates. Often different real world forecasts, produce conflicting estimates and associated intervals of accuracy. This paper addresses the issue of how to make sure of such estimates. It is argued that to both Classical and Bayesian statisticians the problem is essentially trivial. However, it is demonstrated that the assumptions required for a formal Bayesian approach are so sensitive to small changes, that the Bayesian approach has dubious advantages over simple intuition. With the Classical attitude being unhelpful in practice, it is argued that techniques should be developed which combine formal Bayesian updating procedures with intuition. Two possible techniques are explored. The first uses Bayesian updating with parameterized likelihood functions. With suitable interpretation of the parameters, decision makers can use their intuition to choose appropriate parameters. The second technique allows for a number of alternate likelihood functions, combined probabilistically according to the decision maker's judgment. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 1982
- Accession Number
- ADA119809
Entities
People
- Robert C. Bromage