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Ex post and ex ante prediction of unobserved multivariate time series: a structural‐model based approach
Author(s) -
Nieto Fabio H.
Publication year - 2007
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1017
Subject(s) - unobservable , multivariate statistics , econometrics , context (archaeology) , ex ante , extrapolation , computer science , series (stratigraphy) , consistency (knowledge bases) , benchmarking , statistics , mathematics , economics , artificial intelligence , paleontology , macroeconomics , management , biology
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.