Ex post and ex ante prediction of unobserved multivariate time series: a structural-model based approach

Fabio H. Nieto
Additional contact information
Fabio H. Nieto: Department of Statistics, National University of Colombia, Bogotá, COLOMBIA, Postal: Department of Statistics, National University of Colombia, Bogotá, COLOMBIA

Journal of Forecasting, 2007, vol. 26, issue 1, pages 53-76

Abstract: A methodology for estimating high-frequency values of an unobserved multivariate time series from low-frequency values of and related information to it is presented in this paper. This is an optimal solution, in the multivariate setting, to the problem of ex post prediction, disaggregation, benchmarking or signal extraction of an unobservable stochastic process. Also, the problem of extrapolation or ex ante prediction is optimally solved and, in this context, statistical tests are developed for checking online the ocurrence of extreme values of the unobserved time series and consistency of future benchmarks with the present and past observed information. The procedure is based on structural or unobserved component models, whose assumptions and specification are validated with the data alone. Copyright © 2007 John Wiley & Sons, Ltd.

Downloads: (external link)
http://hdl.handle.net/10.1002/for.1017 Link to full text; subscription required (text/html)

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

Access Statistics for this article

Journal of Forecasting is edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Series data maintained by Christopher F. Baum ().