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Forecasting Compositional Time Series with Exponential Smoothing Methods

Author

Listed:
  • Anne B. Koehler
  • Ralph D. Snyder
  • J. Keith Ord
  • Adrian Beaumont

Abstract

Compositional time series are formed from measurements of proportions that sum to one in each period of time. We might be interested in forecasting the proportion of home loans that have adjustable rates, the proportion of nonagricultural jobs in manufacturing, the proportion of a rock's geochemical composition that is a specific oxide, or the proportion of an election betting market choosing a particular candidate. A problem may involve many related time series of proportions. There could be several categories of nonagricultural jobs or several oxides in the geochemical composition of a rock that are of interest. In this paper we provide a statistical framework for forecasting these special kinds of time series. We build on the innovations state space framework underpinning the widely used methods of exponential smoothing. We couple this with a generalized logistic transformation to convert the measurements from the unit interval to the entire real line. The approach is illustrated with two applications: the proportion of new home loans in the U.S. that have adjustable rates; and four probabilities for specified candidates winning the 2008 democratic presidential nomination.

Suggested Citation

  • Anne B. Koehler & Ralph D. Snyder & J. Keith Ord & Adrian Beaumont, 2010. "Forecasting Compositional Time Series with Exponential Smoothing Methods," Monash Econometrics and Business Statistics Working Papers 20/10, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2010-20
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2010/wp20-10.pdf
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    Cited by:

    1. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.

    More about this item

    Keywords

    compositional time series; innovations state space models; exponential smoothing; forecasting proportions;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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