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Multivariate Trend‐Cycle‐Seasonal Decompositions with Correlated Innovations

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  • Jing Tian
  • Jan P.A.M. Jacobs
  • Denise R. Osborn

Abstract

Multivariate analysis can help to focus on important phenomena, including trend and cyclical movements, but any economic information in seasonality is typically ignored. The present paper aims to more fully exploit time series information through a multivariate unobserved component model for quarterly data that exhibits seasonality together with cross‐variable component correlations. We show that economic restrictions, including common trends, common cycles and common seasonals can aid identification. The approach is illustrated using Italian GDP and consumption data.

Suggested Citation

  • Jing Tian & Jan P.A.M. Jacobs & Denise R. Osborn, 2024. "Multivariate Trend‐Cycle‐Seasonal Decompositions with Correlated Innovations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(5), pages 1260-1289, October.
    • Handle: RePEc:bla:obuest:v:86:y:2024:i:5:p:1260-1289
    DOI: 10.1111/obes.12602
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