Bayesian analysis of concurrent time series with application to regional IBM revenue data


Journal Article

Business data frequently arise in the form of concurrent time series. We present a general framework for simultaneous modeling and fitting of such series using the class of Box—Jenkins models. This framework is an exchangeable hierarchical Bayesian model incorporating dependence among the series. Our motivating data set consists of regional IBM revenue available monthly for several geographic regions. Stationary seasonal autoregressive models are simultaneously fit to the regional data series using various error covariance specifications for the strong interregional dependence. A modified Gibbs sampling algorithm is used to carry out the fitting and to enable all subsequent inference. Graphical techniques using predictive distributions are employed to assess model adequacy and to select among models. Outlier estimation and prediction under the chosen model are used for planning and to measure the effect of special promotional events. Copyright © 1994 John Wiley & Sons, Ltd.

Full Text

Duke Authors

Cited Authors

  • Pai, J; Ravishanker, N; Gelfand, AE

Published Date

  • January 1, 1994

Published In

Volume / Issue

  • 13 / 5

Start / End Page

  • 463 - 479

Electronic International Standard Serial Number (EISSN)

  • 1099-131X

International Standard Serial Number (ISSN)

  • 0277-6693

Digital Object Identifier (DOI)

  • 10.1002/for.3980130505

Citation Source

  • Scopus