Regime Switching in Cointegrated Time Series

Volatile commodities and markets can often be difficult to model and forecast given significant breaks in trends through time. To account such breaks, regime switching methods allow for models to accommodate abrupt changes in behavior of the data. However, the difficulty often arises in beginning the process of choosing a model and its associated parameters with which to represent the data and the objects of interest. To improve model selection for these volatile markets, this research examines time series with regime switching components and argues that a synthesis of vector error correction models with regime switching models with ameliorate financial modeling. Using futures prices from dairy markets as the chief data of interest, it will be shown that the traditional methods applied to these kind of series are not consistent and the need for a synthesis of models is needed