EconPapers    
Economics at your fingertips  
 

Efficient Computation of Hierarchical Trends

M K Francke and Aart de Vos

Journal of Business & Economic Statistics, 2000, vol. 18, issue 1, 51-57

Abstract: To model a large database containing selling prices for houses, in which local trends, general trends, and specific characteristics play a role, we derived a new procedure to implement a state-space model for repeated measurements. The original model is decomposed into two parts, which are treated differently. The first part is ordinary least squares on data in deviation from means. This step provides a prior for coefficients to be used in the second step, which is a Kalman filter, providing estimates of the trends and the parameters. The procedure exploits and illustrates the Bayesian interpretation of a Kalman filter.

Date: 2000
References: Add references at CitEc
Citations: View citations in EconPapers (17)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:18:y:2000:i:1:p:51-57

Ordering information: This journal article can be ordered from
http://www.amstat.org/publications/index.html

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano

More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().

 
Page updated 2024-07-01
Handle: RePEc:bes:jnlbes:v:18:y:2000:i:1:p:51-57