Optimizing time-series forecasts for inflation and interest rates using simulation and model averaging

Motivated by economic-theory concepts – the Fisher hypothesis and the theory of the term structure – we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector autoregressions (VAR) in levels and in differences, a cointegrated VAR and a non-linear VAR with threshold cointegration based on data from Germany, Japan, UK and the US. Following a traditional comparative evaluation of predictive accuracy, we subject all structures to a mutual validation using parametric bootstrapping. Ultimately, we utilize the recently developed technique of Mallows model averaging to explore the potential of improving upon the predictions through combinations. While the simulations confirm the traded wisdom that VARs in differences optimize one-step prediction and that error correction helps at larger horizons, the model-averaging experiments point at problems in allotting an adequate penalty for the complexity of candidate models.     

Market testing of a major cassava flour product in the Accra metropolitan area*

The current study analyses consumer judgements about some existing as well as newly introduced brands of a processed cassava food product (fufu flour) by means of market testing. Except for colour, the respondents’ evaluation of the sensory attributes of the various fufu flours that they have consumed did not have much significant differences. Also, no significant differences in sensory attributes were identified when the fufu flour brands were compared with traditional fufu, except for hand‐feel, smoothness and elasticity. In addition, no significant differences were observed in the prices of the various fufu flour brands in comparison with the equivalent price of traditional fufu. The study finds diversification of the marketing outlet for fufu flour as a major factor that could contribute to improving the awareness of the product.