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Related‐variables selection in temporal disaggregation
Author(s) -
Fukuda Kosei
Publication year - 2009
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.1115
Subject(s) - feature selection , cointegration , econometrics , bayesian information criterion , model selection , bayesian probability , feature (linguistics) , statistics , series (stratigraphy) , selection (genetic algorithm) , mathematics , computer science , artificial intelligence , paleontology , linguistics , philosophy , biology
Two related‐variables selection methods for temporal disaggregation are proposed. In the first method, the hypothesis tests for a common feature (cointegration or serial correlation) are first performed. If there is a common feature between observed aggregated series and related variables, the conventional Chow–Lin procedure is applied. In the second method, alternative Chow–Lin disaggregating models with and without related variables are first estimated and the corresponding values of the Bayesian information criterion (BIC) are stored. It is determined on the basis of the selected model whether related variables should be included in the Chow–Lin model. The efficacy of these methods is examined via simulations and empirical applications. Copyright © 2008 John Wiley & Sons, Ltd.