عربي
Forecasting Oil Supply And Demand: Difficulties And Challenges
by A.F. Alhajji
The following article summarizes Dr Alhajji’s speech at the launch of the Arabic version of the International Energy Outlook 2005 (IEO 2005) on 13 November 2005 by the Gulf Research Centre (GRC) in Dubai.
Dr Alhajji is the George Patton Chair of Business and Economics at the College of Business Administration at Ohio Northern University and the Moderator of the Gulf Energy Program at the GRC.
Forecasting energy demand, supply, and prices is an intricate, complex, difficult, and challenging endeavour. In fact, forecasting is an art in itself. Forecasters should not only be equipped with basic training in economics, statistics, and a strong background in energy, but they must also be aware of all the political, economic, technical, legal, and natural factors that affect the production and consumption of various energy resources in regions around the world. Consistent monitoring of events throughout the world is an integral part of the energy forecasting business.
Failureto be responsive to accommodate changes in these variables leads to erroneous forecasts. What happened in 2004 and 2005 is a clear example of such failure. All forecasts by all concerned organizations, companies, and investment banks were wrong.
We should not view this failure as a black mark in the record of the forecasting agency. Instead, we should view failure as evidence that forecasting is a complex and difficult undertaking — one that requires considerable resources and time, international and interdisciplinary cooperation, and even (dare I say it, being an academic?) humility.
After all, a crucial part of arriving at the right answers is to constantly reassess our data and interpretations and to acknowledge the fact that we can be wrong. Learning from past mistakes and eliciting cooperation among various forecasting entities will benefit everyone, including governments and businesses throughout the world. However, such cooperation is a challenge in itself.
Why did the forecasts for 2004 and2005 fail? One of the main answers is related to the prevailing conventional wisdom inherited from the 1970s that higher oil prices decrease economic growth, reduce the demand for oil, and stimulate additional oil supplies.
While the conventional wisdom is still valid today, forecasters failed to see the differences between the conditions that prevailed in the 1970s and those that have prevailed in the last two years. For example, the last two years are the only period in the history of OECD countries when oil prices, economic growth, military expenditures, and government expenditures have all increased, while interest rates, inflation, and the dollar value have all decreased.
Forecasters and analysts failed to see that dollar devaluation increased the demand for oil around the world and limited the effect of high oil prices to the US. Oil prices reached records only in dollar terms, but not in euro and yen. The demand for oil in the rest of the world continued to grow as prices in dollars continued toincrease.
But even in the US, increasing oil prices have not slowed down economic growth. The effect of expansionary monetary and fiscal polices was so great that their impact on the economy was much larger than any negative impact of high oil prices on the economy.
Effects of exchange rates
This is not the first time that forecasters and analysts have overlooked the impact of exchange rates on the oil industry. They ignored the impact of rouble devaluation on the Russian oil industry. It wasn’t until the recent boom in Russia’s production that started in 1999 that analysts realized the impact of rouble devaluation.
The lesson is clear: forecasting models should start to focus on the impact of dollar exchange rates on the oil industry.
The IT industry has grown substantially in the last decade. Forecasters failed to realize that, as almost everything has become computerized, energy consumption in manufacturing these items has increased. Unlike traditional industries, IT manufacturing is energy intensive.
Forecasters also failed to realize the impact of the migration of IT technology on emerging economies such as China and India. Since IT manufacturing is energy intensive, migration shifted much of the growth in oil demand from the West to the East.
Therefore, China’s economic growth is not solely responsible for the sharp increase in China’s demand for oil. This “transferred demand” is also responsible for the increase. In other words, the increase in China’s demand for oil can be divided into two parts, one that is related to accelerated economic growth and the other one is related to the migration of IT manufacturing from the US, Europe, and Japan as companies from these countries moved to China.
If these companies did not move, the demand for oil in these countries would have increased and the gap in the growth in oil demand between China and these countries would have been much less than the actual difference in 2004.
This is not the first time that forecasters and analysts overlooked the impact of growth in energy intensive industries. They also overlooked the impact of the growth in the petrochemical industries in the 1970s and the migration of these industries to developing nations in the 1980s.
This migration shed some doubts on the causes of the decline in the demand for oil by OECD countries in the early 1980s. Therefore, forecasters face more difficulties and challenges as they start to focus on the long-term trends in technology, its energy intensity, and its migration.
The conventional wisdom of the 1970s regarding the impact of high oil prices applies only when prices hit the business cycle near its peak. This is the only period in history when rising energy prices hit the business cycle near at its bottom. In all other cases high oil prices hit the business cycle near its peak.
Forecasters and analysts failed to realize that oil prices, the demand for oil, and economic growth had no other way to go but up after OECD economies hit rock-bottom after the recession of 2001 and the terrorist attacks of 11 September. Current forecasting models do not consider the timing of the increase in energy prices relative to the age of the business cycle. However, the literature in this area is still very weak, which creates an additional set of challenges for energy forecasters.
Dollar devaluation
On the supply side, forecasters and analysts failed to see the impact of dollar devaluation on the oil industry. Dollar devaluation usually limits supply, especially in the North Sea where companies pay their costs in euros while they sell their oil in dollars. They failed to predict a slowdown in Russia and Mexico and several non-OPEC nations. They also failed to see the impact of rosy predictions regarding oil production in central Asia, Russia, and West Africa on plans to expand OPEC’s production capacity.
Such predictions by reputable research centres and consultants convinced the national oil companies of OPEC members that the world would be awash withoil within a few years and avoided expending capacity on the fear that the market could collapse. In other words, these research centres and consultants — many of them inadvertently — fooled OPEC, or at least gave OPEC members an excuse not to invest. Now we are paying the price of such erroneous, in some cases irresponsible, predictions.
Finally, forecasting models still use outdated assumptions regarding OPEC behaviour. Forecasting models predict world demand and non-OPEC supply based on behavioural variables and assume that OPEC will supply the difference between world demand and non-OPEC supply. Recent data prove that OPEC’s production has not been able to fill this gap.
Future plans for expanding capacity are nowhere near the expectations of the IEA, the EIA, and others. The current situation in the oil market illustrates the need for modelling OPEC behaviour based on behavioural variables. The recent reduction of predicted OPEC production by more than 5 mm bpd in 2025 by the EIA is a welcome step in that direction. However, the reduction was not based on behavioural variables. Rather, it was based on strong a conviction that the old prediction was not correct.
Incorporating the above-mentioned factors improves forecasting methods, but it will not eliminate the difficulties and challenges. In fact, it might add to the challenges, since an increased number of variables could easily affect the reliability of already questionable models.
Evaluating the conventional forecasting method and its results creates more challenges, especially if it involves comparisons among various forecasts by various forecasting groups.
Difficulties of forecasting comparisons
One of the main challenges that we face today is that we cannot even compare the forecasts of different groups. For example, it is difficult to make comparisons among the forecasts of the EIA, the IEA, PIRA, PEL, and others. They do not use the same terminology, geographical definitions, or even time periods.
As the IEO 2005 points out, regional breakouts among the forecasting groups vary. Is Mexico in North America? Latin America? Central America? South America? Or a member of OECD? Is Australia part of OECD Pacific? Mature Market Asia? Or Asia Pacific? Is Turkey in the Middle East? Western Europe? Or OECD Europe?
What is “petroleum”? What liquids and gases should be included? What are “proven reserves”? Should all energy used be included or only marketable energy? Should we use tons? Barrels? Barrels of oil equivalent? Or use Btu to measure energy use and production?
Comparison requires various groups to use the same time frame. Some forecasts are up to 2025, others are up to 2030. Some recent forecasts go up to 2050.
In order to eliminate differences, or at least minimize them, I am pleased to announce on behalf of the Gulf Energy Program at the GRC that we will be happy to host all concerned groups and other energy professionals in one or two-day meetings to discuss how to standardize regions, measurements, technical terms, and other related matters.
We strongly believe that such standardization is needed as a first step to improve forecasting of energy demand, supply, and prices.
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