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Forecasting zero-inflated price changes with a Markov switching mixture model for autoregressive and heteroscedastic time series

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  • Kömm, Holger
  • Küsters, Ulrich

Abstract

The weekly changes in prices of several German milk-based commodities exhibit not only traditional patterns such as mean dependence and volatility clustering, but also a high frequency of zero changes that cannot be explained by well-known ARIMA-GARCH models. We therefore develop a new mixture model which combines the elements of zero-inflated models that are common in microeconometrics and intermittent demand forecasting with a traditional ARIMA(1,1,0)-GARCH(1,1) model. We describe the model components, the data generation processes, the maximum likelihood estimation techniques, and the generation of forecasting distributions and point forecasts by resampling techniques. The model is applied to a low frequency weekly time series of skimmed whey powder (SWP). Competing submodels are compared using the Akaike information criterion (AIC). Furthermore, in addition to the evaluation of the out-of-sample forecasting performance, several coverage and independence tests are also computed.

Suggested Citation

  • Kömm, Holger & Küsters, Ulrich, 2015. "Forecasting zero-inflated price changes with a Markov switching mixture model for autoregressive and heteroscedastic time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 598-608.
    • Handle: RePEc:eee:intfor:v:31:y:2015:i:3:p:598-608
    DOI: 10.1016/j.ijforecast.2014.10.008
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