本研究的主要目的是预估石油产品的需求，使用随机动态的方法来达到获得改进和更加稳定估计的价格和收入弹性的目的。这项研究作为一个两步的随机过程指定随机趋势模型的石油产品的需求。通过与卡尔曼滤波应用最大似然在一起得到每个石油产品在尼日利亚的模型参数的估计。研究表明，引入随机趋势减少了相对于没有趋势模型的三个石油产品滞后的因变量的系数的估计。因此，石油产品需求的价格和收入弹性较高，在短期和长期相对于变截距模型。引入随机趋势导致内样本的预测均方误差的改进。
Key words: Elasticity, Kalman filter, Maximum likelihood and Energy Demand.
引言——INTRODUCTION
It is widely accepted among analysts that the quantity demanded of a good or service has an inverse relationship with the price. This general perception derives as much from common sense as from economic theory and basic data observation. Given the significance of this phenomenon, economists have developed a specific concept called price elasticity which measures the relative change (%) in quantity demanded for a good or a service, in response to a relative change (%) in price. Price elasticities can be useful for studying the expected demand growth of a good or service, and for analysing the impact of different government actions with respect to prices such as tariffs, taxes or consumption-related subsidies. The positive link between the consumption of a good or service and the income is also widely acknowledged. That is the relative change (%) in quantity demanded which results from a relative change (%) in the income.
The total energy demand, either with respect to the whole economy or to a specific sector, has received widespread attention in the last thirty five years as a result of the international oil crises of 1973 and 1979. Today, this topic is still of interest due to global warming, associated with greenhouse gases and their link to energy consumption. Previous studies which present a synthesis of previous works on total energy demand models are Ziemba et al (1980), Donnelly (1987) and Hawdon(1992)
The quest for more accurate estimates of such key energy parameters is critical importance in the projection of future energy demand in particular, the energy market trends in general. Second is the role of these parameters in the design of policies for dealing with the negative environment externalities of the energy sector. Third is the fact that understanding energy demand dynamic through improve and robust estimates of energy demand parameters is essential for more informed and successful energy policy decision making and implementation. (Iwayemi, et 2007).
The objective of this paper is to estimate petroleum products demand in Nigeria using random trend approach. Specifically compare the result of modelling the intercept as random trend with constant intercept model. To achieve this objective, this study applied the approach introduced by Hunt et al (2003) to model the intercept in petroleum product demand function as random trend. The last fifteen years or so there has been an over-reliance on the co-integration technique, which is not always the right tool for the job of estimating energy demand function. Harvey (1997) states in general, the emphasis on unit roots, vector auto regression and co-integration has focused too much attention on tackling un-interesting problems by flawed methods. But this study will not dwell on that due to scope constraint. The structural time series model used to represent the random shift in demand relies only on a few parameters and yet it is quite general in the sense that it rests several well known models such as random walks, fixed trends or trends at all (Andrews 1999; Khalaf and Kichian, 2005).
The random trend model will be used to estimate price and income elasticity for petroleum products using aggregates and disaggregates approach using annual data covers 1977-2005. The specific products that will be considered are automotive gas oil (Diesel), premium motor spirit (petrol) and dual purpose kerosene (household kerosene) measured in tonnes per capita. Other econometric tests will be performed. In section two, the estimation of the demand equation is described. Section three discusses empirical results. Our findings are summarized in section four.
研究方法——RESEARCH METHODOL
