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

Holger Kömm and Ulrich Küsters

International Journal of Forecasting, 2015, vol. 31, issue 3, 598-608

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.

Keywords: Agriculture; ARIMA models; GARCH models; Mixture models; Price forecasting; Time series; Volatility forecasting; Zero-inflated models (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:31:y:2015:i:3:p:598-608

DOI: 10.1016/j.ijforecast.2014.10.008

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