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Compositional Time Series: Past and Perspectives

Author

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  • Juan M.C. Larrosa

    (Universidad Nacional del Sur (UNS). Argentina)

Abstract

Este trabajo revisa contribuciones académicas que se centran en el análisis de series dinámicas composicionales, un tema poco investigado a pesar de la amplia disponibilidad de datos en ciencias sociales. Explora las opciones disponibles y divide los artículos de investigación en dos enfoques principales de probabilidad, frecuentista y bayesiano, y enumera varias transformaciones y técnicas específicas de datos. Como conclusión, esta rama del análisis estadístico de la composición requiere una actualización profunda y, por esta misma razón, es un campo fértil para la investigación futura.

Suggested Citation

  • Juan M.C. Larrosa, 2017. "Compositional Time Series: Past and Perspectives," Economic Analysis Working Papers (2002-2010). Atlantic Review of Economics (2011-2016), Colexio de Economistas de A Coruña, Spain and Fundación Una Galicia Moderna, vol. 1, pages 1-1, June.
    • Handle: RePEc:eac:articl:03/16
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    References listed on IDEAS

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    1. Peter Dawson & Paul Downward & Terence C. Mills, 2014. "Olympic news and attitudes towards the Olympics: a compositional time-series analysis of how sentiment is affected by events," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1307-1314, June.
    2. Petra Kynčlová & Peter Filzmoser & Karel Hron, 2015. "Modeling Compositional Time Series with Vector Autoregressive Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 303-314, July.
    3. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    4. Jakob Bergman & Björn Holmquist, 2014. "Poll of Polls: A Compositional Loess Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 301-310, June.
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