Chapter 1 Bayesian Forecasting

John Geweke, Charles Whiteman

Research output: Chapter in Book/Report/Conference proceedingChapter

63 Scopus citations


Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of the unknown quantities to be future values of some variables of interest. This chapter presents the principles of Bayesian forecasting, and describes recent advances in computational capabilities for applying them that have dramatically expanded the scope of applicability of the Bayesian approach. It describes historical developments and the analytic compromises that were necessary prior to recent developments, the application of the new procedures in a variety of examples, and reports on two long-term Bayesian forecasting exercises.

Original languageEnglish (US)
Title of host publicationHandbook of Economic Forecasting
EditorsG. Elliott, C.W.J. Granger, Granger Timmermann
Number of pages78
StatePublished - 2006

Publication series

NameHandbook of Economic Forecasting
ISSN (Print)1574-0706

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


Dive into the research topics of 'Chapter 1 Bayesian Forecasting'. Together they form a unique fingerprint.

Cite this