There have been considerable efforts contributed to the development of effective energy demand forecast models due to its critical role for economic development and environmental protection. This study focused on the adoption of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models for energy consumption forecasting in Hong Kong over the period of 1975-2010. Four predictors were considered, including population, GDP, exports, and total visitor arrivals. The results show most ANN models demonstrate acceptable forecast accuracy when single predictor is considered. The best single input model is the case with GDP as predictor. Population and exports are the next proper single inputs. The model with total visitor arrivals as sole predictor does not perform satisfactorily. This indicates that tourism development demonstrates a different pattern from that of energy consumption. In addition, the forecast accuracy of ANN does not improve considerably as the number of predictors increase. Findings imply that with the ANN approach, choosing appropriate predictors is more important than increasing the number of predictors. On the other hand, ARIMA generates forecasts as accurate as some good cases by ANN. Results suggest that ARIMA is not only a parsimonious but effective approach for energy consumption forecasting in Hong Kong.

This paper establishes a duopoly model with product differentiation and outsourcing in order to analyze the equilibrium competition strategies (choice of prices versus quantities) when the outsourcer outsources its intermediate good to a final product competitor. We show that: (1) both firms choose the quantity strategy when the cost efficiency of the subcontractor is low; (2) the choice of competition strategy is the price strategy for the subcontractor and the quantity strategy for the outsourcer when the cost efficiency of the subcontractor is moderate; (3) both firms choose the price strategy when the cost efficiency of the subcontractor is sufficiently high.