✦ LIBER ✦

Generalized exponential growth models a bayesian approach

✍ Scribed by Helio S. Migon; Dani Gamerman

Publisher: John Wiley and Sons
Year: 1993
Tongue: English
Weight: 607 KB
Volume: 12
Category: Article
ISSN: 0277-6693
DOI: 10.1002/for.3980120704

No coin nor oath required. For personal study only.

✦ Synopsis

A broad class of normal and non-normal models for processes with nonnegative and non-decreasing mean function is presented. This class is called exponential growth models and the inferential procedure is based on dynamic Bayesian forecasting techniques. The aim is to produce the analysis on the original variable avoiding transformation and giving to the practitioner the opportunity to communicate easily with the model. This class of models includes the well-known exponential, logistic and Gompertz models. Models for counting data are compared with the Normal models using the appropriate variance law. In the examples, the novel aspects of this class of models are illustrated showing an improved performance over simple, standard linear models.

KEY WORDS Bayesian dynamic forecasting

Generalized exponential growth models Counting data Variance law Interventions Discounting

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