Modeling expert forecasting knowledge for incorporation into expert systems

Article Abstract:

A study evaluates the use of continuous multivariate models to represent experts' knowledge of relations among a set ofvariables. These models can be incorporated into expert systems and complement the conventional contingent rules, especially when entities in noisy domains are involved. They also differ from conditional rule representations in handling uncertainty and in error checking. However, the success of an implementation partly depends on the availability of experts and the kinds of judgment with which they are qualified. Available computer tools for helping experts are also discussed.

Research, Usage, Multivariate analysis, Expert systems, Linear models (Statistics)

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Why do regime-switching models forecast so badly?

Article Abstract:

Nonlinear methods usually provide good in-sample performance but they are often outperformed by random walks when applied for forecasting. For regime-switching models, it is readily evident why forecasts based on the true model can have higher mean squared error that those of a random walk or random walk with drift. Several analytical findings for the case of a simple switching model, the segmented trend model, are presented. It needs only a small misclassification, when forecasting which regime the world will be in, to lose any benefit from knowing the correct model specification.

Forecasting, Analysis, Business forecasting, Nonlinear theories

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