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Forecasting a large set of disaggregates with common trends and outliers

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  • Carlomagno, Guillermo
  • Espasa, Antoni

Abstract

This paper deals with macro variables which have a large number of components and our aim is to model and forecasts all of them. We adopt a basic statistical procedure for discovering common trends among a large set of series and propose some extensions to take into account data irregularities and small samples issues. The forecasting strategy consists on estimating single-equation models for all the components, including the restrictions derived from the existence of common trends. An application to the disaggregated US CPI shows the usefulness of the procedure in real data problems.

Suggested Citation

  • Carlomagno, Guillermo & Espasa, Antoni, 2015. "Forecasting a large set of disaggregates with common trends and outliers," DES - Working Papers. Statistics and Econometrics. WS ws1518, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws1518
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    References listed on IDEAS

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    1. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    2. Pierre Perron & Gabriel Rodríguez, 2003. "Searching For Additive Outliers In Nonstationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 193-220, March.
    3. Mark Bils & Peter J. Klenow, 2004. "Some Evidence on the Importance of Sticky Prices," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 947-985, October.
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