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Modelling and forecasting time series sampled at different frequencies

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Author Info

  • José Casals

    (Universidad Complutense de Madrid, Spain)

  • Miguel Jerez

    (Universidad Complutense de Madrid, Spain)

  • Sonia Sotoca

    (Universidad Complutense de Madrid, Spain)

Abstract

This paper discusses how to specify an observable high-frequency model for a vector of time series sampled at high and low frequencies. To this end we first study how aggregation over time affects both the dynamic components of a time series and their observability, in a multivariate linear framework. We find that the basic dynamic components remain unchanged but some of them, mainly those related to the seasonal structure, become unobservable. Building on these results, we propose a structured specification method built on the idea that the models relating the variables in high and low sampling frequencies should be mutually consistent. After specifying a consistent and observable high-frequency model, standard state-space techniques provide an adequate framework for estimation, diagnostic checking, data interpolation and forecasting. An example using national accounting data illustrates the practical application of this method. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1112
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 28 (2009)
Issue (Month): 4 ()
Pages: 316-342

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Handle: RePEc:jof:jforec:v:28:y:2009:i:4:p:316-342

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
  2. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886, July.
  3. Elena Marquez & Belen Nieto, 2011. "Further international evidence on durable consumption growth and long-run consumption risk," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 195-217.

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