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Forecasting and Interpolation Using Vector Autoregressions with Common Trends

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  • F. Javier Fernandez Macho
  • Andrew C. Harvey
  • James H. Stock

Abstract

A modification of the vector autoregressive model is to include a stochastic trend component in each equation. It is argued that this formulation will lead to a more parsimonious model than traditional vector autoregressions formulated in terms of levels or differences. Common trends, or factors, may be introduced into the model. This leads to certain of the variables being co-integrated and, as shown in Granger and Engle [1987], the model then has an error correction representation. Estimation of the model can be carried out by casting it in state space form and applying the Kalman filter. This enables estimation to be carried out for a very general situation in which observations may be missing, temporally aggregated or observed at different time intervals. The common trends may also be extracted using smoothing techniques. Missing observations can also be estimated and the model is likely to be useful if this is the main objective.

Suggested Citation

  • F. Javier Fernandez Macho & Andrew C. Harvey & James H. Stock, 1987. "Forecasting and Interpolation Using Vector Autoregressions with Common Trends," Annals of Economics and Statistics, GENES, issue 6-7, pages 279-287.
    • Handle: RePEc:adr:anecst:y:1987:i:6-7:p:279-287
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    File URL: http://www.jstor.org/stable/20075657
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    Cited by:

    1. Stefan Gerlach & Matthew S. Yiu, 2004. "A Dynamic Factor Model for Current-Quarter Estimates of Economic Activity in Hong Kong," Working Papers 162004, Hong Kong Institute for Monetary Research.
    2. Fabio H. Nieto, 2007. "Ex post and ex ante prediction of unobserved multivariate time series: a structural-model based approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 53-76.
    3. Ko, Byoung Wook, 2010. "An application of dynamic factor model to dry Bulk Market - focusing on the analysis of synchronicity and idiosyncrasy in the sub-markets with different ship - size," MPRA Paper 32572, University Library of Munich, Germany.
    4. Peter Fuleky & Carl Bonham, 2010. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 2010-17R1, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2013.
    5. Juan Carlos Carlo Santos, 2019. "Pronósticos del PIB mediante modelos de factores dinámicos," Revista de Análisis del BCB, Banco Central de Bolivia, vol. 30(1), pages 125-174, January -.
    6. Peter Fuleky & Carl, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 2013-5, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    7. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc.
    8. Francisco J. Goerlich-Gisbert, 1999. "Shocks agregados versus shocks sectoriales. Un análisis factorial dinámico," Investigaciones Economicas, Fundación SEPI, vol. 23(1), pages 27-53, January.

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