Skip to main content

A Spatial Prior for Bayesian Vector Autoregressive Models

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

In this paper we develop a Bayesian prior motivated by cross-sectional spatial autoregressive models for use in time-series vector autoregressive forecasting involving spatial variables. We compare forecast accuracy of the proposed spatial prior to that from a vector autoregressive model relying on the Minnesota prior and find a significant improvement. In addition to a spatially motivated prior variance as in LeSage and Pan (1995) we develop a set of prior means based on spatial contiguity. A Theil-Goldberger estimator may be used for the proposed model making it easy to implement.

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/1467-9787.00135

Affiliations: University of Toledo, Toledo, OH

Publication date: May 1, 1999

bpl/jors/1999/00000039/00000002/art00004
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more