Taking cue from Dees et.al, 2007; Kaufman (1994); and Gately (2004, 2007), and in the light of the findings of the exploratory exercise, a behavioral crude price equation has also been estimated based on potential supply and demand side behavioral variables that could have perceptible influence on crude price.
The price function is considered as:
REALP = f (CAPUTILOPEC, STKSDD)
CAPUTILOPEC indicates capacity utilization of OPEC (defined as production over capacity of OPEC14) and is a potential supply side behavioral variable that is usually observed as linked with movements of real crude prices and has often been observed to explain to a large extent the movement of crude prices. CAPUTILOPEC accounts for both compliant and non-compliant behavior of OPEC. An increase or decrease in capacity utilization may either be strategically decided (say on the basis of expected market share) or may just be for complying as a residual equilibrating producer.
STKSDD is the ratio of stock/inventory of crude oil over the demand for crude. The ratio indicates the days of forward consumption of stocks i.e. the days the stock/inventory would be able to sustain demand. Thus, any increase or decrease in demand or any increase or decrease in the level of inventories would alter the ratio and hence would exert influence upon crude price. As it is difficult to obtain accurate and consistent data on inventories of crude oil for the world as a whole, the data for OECD stocks or inventories as a proportion of its demand has been considered here instead as proxy. The choice of the proxy is also vindicated by the fact that OECD countries have historically been the largest consumer of crude oil in the international market.
14 The capacity of OPEC at a particular point in time is determined primarily by its proven reserve position.
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1.4 Data Sources and Methodology
1.4.1 Sources of Data
For estimation purpose data and information have been collected from several sources. The estimation has been carried out on the basis of annual data for the sample time period 1975 to 2004. The data on crude production (supply), consumption (demand) and proven reserves have been collected from British Petroleum’s Statistical Review of World Energy 2006. The International Yearbook of Energy Statistics, published by United Nations has also been consulted for information on the above variables for some years. The data on crude oil prices have been obtained from Platts (website: www.platts.com) and British Petroleum’s Statistical Review of World Energy 2006 (website: www.bp.com). The data on GDP at PPP has been obtained from World Bank’s ‘World Development Indicators’ through WDI online portal of the World Bank (www.worldbank.org/data/onlinedatabases/onlinedatabases.html).
The data on OECD Stocks has been obtained from IEA, ‘Oil Market Report’ for various years. The data on production capacity of OPEC has been obtained from the various issues of Oil and Gas Journal published by Penn Well Petroleum Group (website: www.ogj.com) and statistical supplement of IMF for World Economic Outlook, 2005.
1.4.2 Methodology
The estimation that has been carried out in the paper is based on Cointegration and VECM (Vector Error Correction Model), which are techniques normally used in multivariate time-series analysis.
The approach which is often used for quantitative modeling of the demand and supply are Structural Equation Approach (SEA). The SEA is founded on economic theory to describe the relationships between several variables of interest. On the basis of the underlying theory simultaneous structural equations based model is specified in order to explain the functioning of an economic system. Thus the SEA begins by pre-judging an endogenous-exogenous divide of the variables. The model so specified is then estimated, and used to test the empirical relevance of the theory on which the modeling is founded. On the contrary, the
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multivariate time series approach does not presume an underlying structural or theoretical framework. In this approach, a set of variables that seems to potentially reflect agent’s decision is considered as jointly endogenous and are thus conferred symmetrical treatment.
The current realizations and / or future expectations of these selected variables are thus contingent upon the currently available information set15.
Before explaining the methodology that has been used in this study namely cointegration and VECM, a brief discussion on some of the basic concepts of Time Series Analysis that are relevant to the exercise, would be useful.
Time Series
A time series is defined as a set of quantitative observations arranged in chronological order.
Thus it is basically a sequence of numerical data in which each item is associated with a particular instant in time. It is possible to quote numerous examples of time series like monthly unemployment, weekly measures of money supply, daily closing price of stock indices and so on.
Stochastic Process
Formal models for time series are however developed on the basis of probability theory. Let the T-dimensional vector of random variables X1, X2, ... XT be given with the corresponding multivariate distribution. Such a collection of random variables {Xt}
t ~ 1 … . . T is called a stochastic process or a data generating process. There may not just be one realisation of such a process, but, in principle, an arbitrary number of realisations are possible which all have the same statistical properties as they all result from the same data generating process. A time series is usually considered as one realisation of the underlying stochastic process.
15 This short discussion is based on Coondoo and Mukherjee (2005)
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