the price of crude oil on the nymex

8/14/2019 The Price of Crude Oil on the NYMEX

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Explaining the real price of crude oil on the NYMEX

Will C. Hambly

December 2, 2005Economics 272

Professor Studenmund


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Background Information

Being the lifeblood of the worlds industrialized economies, crude oil is the most actively

traded commodity. The world consumes roughly 80 million barrels of crude oil per day and uses

petroleum products for a multitude of applications, including transportation, heating, and plastic

production. Because oil is such an essential input in the production process, its price is closely

followed and reported daily by the financial press. Also, most of the worlds heaviest consumers

of petroleum rely on imports from Middle Eastern oil-producing nations. Since the formation of

an international petroleum cartel, the Organization of Petroleum Exporting Countries (OPEC),

the political importance of oil has escalated. In an effort to insulate the American economy from

oil shocks, the U.S. government began stockpiling emergency oil reserves in 1977 as a national

security policy.

The question of whether the price of oil is high or low based on market fundamentals is a

contentious debate. Currently, oil is trading at about $60 per barrel in 2005 dollars, a relatively

high price compared to historical averages. Many justify this price and remain bullish, adhering

to the idea that the supply of petroleum is fixed and that increased demand from developing

countries will drive the price higher as they accelerate growth. Others dismiss the current price

as being irrational and the result of increased speculative activity by large alternative investment

funds. This paper seeks to explain what determines the price of oil.

Several different types of crude oil are produced and receive different market prices. For

instance, North Sea crude, generally known as Brent crude, commands about a $1 premium to

the OPEC Basket Price, which includes various blends of Dubai, Saharan, and Venezuelan

crudes. The price quoted on the New York Mercantile Exchange, however, is for light-sweet, or

West Texas Intermediate (WTI) crude. WTI is the most easily and widely refined crude in

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Much of the academic literature pertaining to the price of oil surrounds the impact of oil

prices on the macroeconomy. Understanding the Impact of Oil Shocks,published by the

Federal Reserve Bank of St. Louis, examines oil price shocks in the 1970s and shows

how they contributed to a drop in real GDP and an increase in the price level. This study,

however, analyzes oil prices as an independent variable and does not describe how oil

prices are set; nevertheless it confirms the correlation between economic growth and oil

prices. Another scholarly article, The Cyclical Behavior of NYMEX Energy Prices,

published in Energy Economics, explains that oil prices are procyclical. That is, an

expansion of real GDP is usually accompanied by an increase in the price of crude.

Besides a review of the scholarly literature, relevant commodities articles in both the Wall

Street Journal and the Financial Times offer daily insight into what may be moving the

market. Additionally, information provided by Phil Flynn, an oil trader with Alaron

Trading Co., offered a perspective on fundamental evaluators, such as real GDP, housing

starts, and industrial production. In the interview conducted, he stressed the importance

of commercial crude oil stocks and economic growth.

A Theoretical Model

A review of the literature pertaining to oil prices makes clear that as economic growth

increases, demand for oil increases. The economic theory behind the relationship between

economic growth and oil consumption is strong. In a model seeking to explain changes in price,

there is no question that a measure of demand is necessary. Another important aspect of the oil

market is the role of OPEC, the international petroleum cartel. While OPECs role in reducing

petroleum production has diminished over time, the cartel is still likely to have a significant

impact on the price of oil. Additionally, the role of stockpiles of crude oil should have an impact

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on the price of crude. As stocks are depleted, there may be a fear that the commodity is in short

supply and traders will bid up the price. Also, a shock to the production of crude must have an

impact on the price. If there is a significant decline in oil production in a specific area, the

market will likely react to the prospect of reduced availability of oil by bidding up the price.

Lastly, to reflect current trends in the market for crude oil, a time-series model with very frequent

observations should be used. For practical reasons concerning the publishing of economic data,

a monthly model from January of 2002 through June of 2005 will be used.

Additionally, because crude oil on the NYMEX is measured in dollars, inflation must have an

effect on the price of oil. To measure real impacts on the price, inflation must be filtered out of

the dependent variable.

The Independent Variables, Functional Form, and Expected Signs of Coefficients

While it is clear that many variables affect the price of crude oil, determining the correct

variables for an equation is difficult because there are several ways to measure a single

phenomenon. Below are the independent variables and a detailed explanation of why each was

chosen and what it means:

Industrial Production Index: As the worlds economies grow, industrial production

expands and global demand for oil increases. Because the United States economy

consumes roughly 25% of the worlds crude oil, and meticulous monthly data is

collected by the government, the Industrial Production Index was chosen to explain

demand for oil in developed countries. The Industrial Production Index measures the

monthly physical output of the manufacturing, mining, gas, and electricity industries.

Other ways of measuring output, such as real GDP, are inferior to the Industrial

Production Index for this model because real GDP measures output in the service and

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technology sectors, which consume less petroleum than heavy industries. Theory

suggests that the relationship between industrial output and crude prices should be

linear. Increases in industrial output should mean that oil demand has increased and

that the price should rise. A positive (+) sign is expected.

Non-OECD Consumption of Petroleum: This variable is a measure of oil

consumption in the developing world. As the developing world industrializes, the

world economys demand for petroleum accelerates. Both China and India are two of

the fastest growing nations and consume large amounts of oil. Almost all literature

concerning the price of oil cites Chinese demand as a driver of prices. The

relationship between non-OECD consumption and the price of oil should be linear as

well. As the consumption of a non-renewable resource increases, price should rise, so

the expected sign of this coefficient is positive (+).

Change in Crude Stocks: Changes in the commercial stocks of crude oil are an

important driver behind changes in price. Quantities of crude oil stocks are stocks of

oil held at refineries, in pipelines, in bulk terminals, or any quantities in transit to the

aforementioned destinations. If this variable were the absolute level of crude stocks,

an inverse function form would be theoretically accurate, because the impact of the

stock levels on price would diminish as they increased. Because it is the change in

stocks, only linear is appropriate. An increase in crude stocks should ease the

markets fear of a shortage, so the expected sign of this coefficient is negative (-).

Change in U.S. Field Production: A disruption in U.S. field production should have

a large impact on prices. Because the U.S. consumes more petroleum than it

produces, it is forced to import crude oil from abroad. As more oil is produced in the

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United States, fears of a shortage will diminish and the price should fall. The

relationship between changes in field production and the price of oil should be linear.

The expected sign of this coefficient is negative (-).

Change in OPEC Output: By restricting output, OPEC has been able to raise the

price of oil. OPECs share of world oil production has decreased since the 1970s

because new oil fields have come on-line and market power has eroded; nevertheless,

OPECs pricing power still exists. Theory suggests that the relationship between

changes in OPEC output and the price of oil is linear. The expected sign of this

coefficient is negative (-) because as OPEC increases output, the price of oil should

fall.

For this model it is reasonable to assume a functional form in which the equation is linear

in both the coefficients and the variables. Theory does not suggest that the relationship between

the variables described above and the price of oil should be anything other than linear.

Discussion of the Data

Below is a discussion of the dependent variable and each independent variable. A

description of how the data is expressed, sources, and any irregularities found are offered.

Real Price of NYMEX Crude: Daily data on the price of crude oil is available from

the U.S. Energy Information Administration website. These prices are, however, in

nominal prices. Because there has been persistent inflation throughout the last three

years, prices quoted on the NYMEX were converted to January, 2002 dollars, when

the Consumer Price Index, Less Energy was equal to 186. Each monthly observation

is the real closing price (in January, 2002 dollars) of the contract of nearest expiration

on the first trading day of the month.

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Industrial Production Index: Data was obtained from the Federal Reserve Bank of

St. Louis Federal Reserve Economic Data website. This data is seasonally adjusted,

of monthly frequency, and has a base year of 2002.

Non-OECD Consumption of Petroleum: Monthly time series data concerning

petroleum consumption and economic growth for countries not belonging to the

Organisation for Economic Co-operation and Development is not accessible.

Because data on oil consumption is not readily available, it was calculated as a

residual. Non-OECD consumption was calculated as the difference between total

world production per day per month of crude oil and OECD consumption per day per

month. Because a portion of the oil produced could enter stockpiles, this variable

may not be precise, however the theory behind the idea that developing countries

consume large amounts of oil as they industrialize is very strong, so this variable

must be included. It is expressed as the percentage change in consumption from the

same month in the previous year to adjust for seasonality. Data was obtained from

the U.S. Energy Information Administration websites section on international

petroleum.

Change in Crude Stocks: Changes in the crude oil stocks are measured as a

percentage change in the quantity of commercial crude oil stocks of the same month

from the previous year to eliminate any seasonal trends. This data is available on the

U.S. Energy Information Administrations website in the supply and disposition

section.

Change in U.S. Field Production: Data on U.S. field production of crude oil is

available at the U.S. Energy Information Administrations website as well. This

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variable is calculated as the percentage change in field production from the same

month of the previous year to avoid seasonality.

Change in OPEC Output: This variable is measured as the percentage change in

output from the same month of the previous year to eliminate any seasonal patterns.

Statistics on OPEC production are also available from the U.S. Energy Information

Administration.

Estimation and Evaluation of the Equation

Using Ordinary Least Squares and the variables discussed above to estimate an equation

yields the following results (t-scores in parenthesis):

Equation 1:

NYMEX = -263.354 + 2.894 INDPROD + 0.061 NONOECD - 0.176 STOCKS

(11.600) (0.794) (-2.210)

+ 0.212 FIELDPROD + 0.062 OPEC

(0.9053) (0.676)

N = 42 Adjusted-R 2 = 0.8795 DW = 1.400

INDPROD = the Industrial Production Index

NONOECD = the percentage change in Non-OECD Consumption

STOCKS = the percentage change in commercial stocks

FIELDPROD = the percentage change in U.S. production

OPEC = the percentage change in OPEC output

Note: See Appendix Equation 1 for Regression Output, Correlation Matrix, Residuals,

and Data.Hypothesis Tests for Each of the Coefficients in Equation 1:

Degrees of Freedom = 36

5% One-Sided Test: tc = 1.697

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HO: INDPROD 0 tINDPROD = 11.600 | tINDPROD| > tc and the sign is in the correct

direction. Reject HO.

HA: INDPROD > 0

HO: NONOECD 0 tNONOECD = 0.794 | tNONOECD| < tc even thought the sign is in the

correct direction.

HA: NONOECD > 0 Fail to Reject HO.

HO: STOCKS 0 tSTOCKS = -2.209 | tSTOCKS| > tc and the sign is in the correct


HA: STOCKS < 0

HO: FIELDPROD 0 tFIELDPROD = 0.905 | tFIELDPROD| < tc and the sign is in the wrong

direction. Fail to Reject HO.

HA: FIELDPROD < 0

HO: OPEC 0 tOPEC = 0.676 | tOPEC| < tc and the sign is in the wrong

direction. Fail to

Reject HO.

HA: OPEC < 0

Of the five coefficients, two are significant in the expected direction. INDPROD andSTOCKS

were significant and in the hypothesized directions. NONOECD had the expected positive sign,

however it was not significant. FIELDPROD andOPEC were insignificant in the unexpected direction.

Of the insignificant coefficients, the economic theory behind OPEC andNONOECD is indisputable.

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The impact of OPECs output and the developing worlds demand for crude oil is well

documented and strongly supported by economic theory. By reducing output, OPEC is able to

increase the price of oil. Also, as countries not in the OECD, or the worlds developing

countries, industrialize they will increase demand for petroleum products and the price of oil will

rise. Both OPEC and NONOECD belong in the equation.

After rethinking the theory behind the regression, FIELDPROD, which was insignificant in

the wrong direction, could be an irrelevant variable. Although there is some theory behind the

idea that as U.S. field production increases, the price of oil should fall, this variable may not

belong because U.S. production is a small fraction of the world total. In fact, increases in field

production may be highly correlated with increases in the price of oil. As the price rises, it

becomes economically viable to drill in harsh environments, therefore increasing the production

of crude oil. In other words, field production does not have a significant impact on the price of

crude oil. The possibility of it being irrelevant must be investigated. It is now dropped from the

model based on theory and the coefficients are re-estimated.

The following is Equation 2 (t-scores in parenthesis):

NYMEX = - 262.886 + 2.889 INDPROD + 0.057 NONOECD - 0.169 STOCKS

(11.612) (0.737) (-2.139)

+ 0.064 OPEC

(0.702)

N = 42 Adjusted-R 2 = .8801 DW = 1.368





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Note: See Appendix Equation 2 for Regression Output, Correlation Matrix, Residuals,

and Data.

Hypothesis Tests for Each of the Coefficients in Equation 2:

Degrees of Freedom = 37

5% One-Sided Test: tc = 1.697

HO: INDPROD 0 tINDPROD = 11.612 | tINDPROD| > tc and the sign is in the correct


HA: INDPROD > 0

HO: NONOECD 0 tNONOECD = 0.737 | tNONOECD| < tc even thought the sign is in the

correct direction. Fail to

HA: NONOECD > 0 Reject HO.

HO: STOCKS 0 tSTOCKS = -2.139 | tSTOCKS| > tc and the sign is in the correct


HA: STOCKS < 0

HO: OPEC 0 tOPEC = 0.702 | tOPEC| < tc and the sign is in the wrong

direction. Fail to

Reject HO.

HA: OPEC < 0

After re-estimating the equation excluding the suspected irrelevant variable, the four

specification criteria must be applied to the results.

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Theory: The theory behind the idea that U.S. field production affects the price of

crude oil is valid, however the United States only produces a small fraction of the

worlds oil, so this variable may not be belong. Additionally, field production may

not be affecting the price, but the price may be inducing producers to produce more.

Also, U.S. oil supplies may be insulated from periodic disruptions in field production

because of the Strategic Petroleum Reserves held by the government.

T-test: The t-score for the coefficient of field production was insignificant in

Equation 1. Two out of the four coefficients became more significant, but only one

became more significant in the expected direction. This is a relatively weak sign that

field production may be an irrelevant variable.

Adjusted-R2: Adjusted-R2 increased from 0.8795 to 0.8801. This is a small change in

adjusted-R2, nevertheless it increased. This is further evidence that field production

was an irrelevant variable.

Changes in the Coefficients: None of the coefficients changed significantly. This is

evidence that that field production is an irrelevant variable and its exclusion is not

causing any bias.

Based on the specification criteria, field production is removed from the model. Moving

forward with Equation 2, the equation must be tested to determine if any econometric maladies

afflict it.

Omitted Variables

The equation almost certainly has an omitted variable. A variable for political concern

over future oil supply should be included, however, political tension is not easily quantified. A

dummy variable for political events concerning the oil market would also be appropriate,

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however observations of this variable would indicate that in each month there was a political

event, which would prove useless for the model. Also, since every month has a political event in

the oil market, deciding which event should be considered important injects human error and the

psychological phenomenon of confirmation bias into the equation.

Another possible omitted variable is a gauge of the developing worlds economic growth.

The Non-OECD Consumption of Petroleum variable contains imperfect data. Data on the

developing worlds economic growth or expectations of growth would enhance the accuracy of

the model and may result in a significant t-score for NONOECD.

Irrelevant Variables

Based on the four specification criteria, the field production variable was eliminated from

the model. The economic theory behind the existing variables is strong and in two of the four

coefficients, it is supported by significant t-scores. Because the variables included are supported

by strong theory, none is irrelevant. The regression results show that NONOECD andOPEC have a

very small impact on the price of oil and the coefficients are insignificant. Despite this

weakness, theory is strong and both variables must be included.

Functional Form

There is no reason to suspect that any functional form besides linear in the coefficients as

well as in the variables is appropriate. Including an intercept dummy or slope dummy for

political events may be theoretically appropriate, however, in every month there are several

political events of importance involving oil supply, so determining which events to include

would be of dangerously subjective nature.

Multicollinearity

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Because the t-scores for NONOECD andOPEC are insignificant, and OPEC has an unexpected

sign, the equation could be afflicted with multicollinearity, which would result in high standard

errors and low t-scores. The Equation 2 Correlation Matrix included in the Appendix shows

the simple correlation coefficients between the independent variables.

All simple correlation coefficients are below 0.80. This is evidence that the equation does not

have multicollinearity. To further investigate multicollinearity in the equation, calculating the

variance inflation factors is necessary. Computer output for each estimated auxiliary equation

used to compute the VIF is included in the Appendix. Each VIF is presented below:

INDPROD NONOECD STOCKS OPEC

R-squared 0.5879 0.0147 0.4586 0.4736

VIF 2.43 1.01 1.85 1.90

None of the variables have a variance inflation factor above the threshold of 5. This is

further evidence that the equation does not have multicollinearity.

Serial Correlation

Being a time-series model, there is a high probability that the equation may have pure

positive serial correlation. This would bias the estimates of the standard errors negative and

increase the probability of a Type I error, making hypothesis testing unreliable.

The Durbin-Watson statistic for the equation is 1.368. A 5% one-sided Durbin-Watson

Test requires the appropriate critical values for an equation in which K = 4 and N = 42. These

values are as follows: dL = 1.29, dU = 1.72.

HO: 0 (no positive serial correlation)

HA: > 0 (positive serial correlation)

Because dL 1.368 dU, the resultsof the Durbin-Watson Test are inconclusive. The

existence of positive impure serial correlation cannot be detected. Even though a time series

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model suggests serial correlation, because the Durbin-Watson test is inconclusive no remedy

should be applied.

Heteroskedasticity

Because this is a time-series model and there are not huge differences in size of the

dependent variable, heteroskedasticity is not extremely likely. Nevertheless, having

heteroskedasticity could lead to unreliable hypothesis testing because standard errors will be

biased negative, inflating the t-scores, and increasing the probability of a Type I error. The

existence of heteroskedasticity should be investigated. Running a Park Test requires a

proportionality factor, Z. It is reasonable to choose the Industrial Production Index as the

proportionality factor for this equation. The Industrial Production Index measures the physical

output of U.S. industry, and serves as a measure of the United States oil demand. As industrial

output increases, it is reasonable to assume that there may be a higher variance in the price of

crude oil. Thus, the Industrial Production Index is an appropriate proportionality factor.

Running the Park Test requires the generation of three new variables: the squared residuals, the

natural logarithm of the squared residuals, and the natural logarithm of the proportionality factor

Z, which is the Industrial Production Index. These variables are included in the Appendix. An

estimation of the regression to be used in the Park Test is as follows (t-scores in parenthesis):

LNRESIDSQ = -78.974 + 17.255 LNZ

(1.448)

N = 42 Adjusted-R 2 =0.026

LNZ = the natural logarithm of the Industrial Production Index

LNRESIDSQ = the natural logarithm of the residuals squared

Note: Regression output is included in the Appendix.

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To run two-sided 1% t-test on the estimated coefficient of LNZ, the critical value of 2.704 is

needed.

HO: lnZ = 0 tlnZ = 1.448 | tlnZ| < tc

HA: lnZ 0 Fail to Reject the Null Hypothesis.

Because the Null Hypothesis cannot be rejected, there is no evidence of heteroskedasticity.

Results

Of the six major econometric diseases investigated, the only outstanding possibility of a

problem with the equation is the existence of an omitted variable. The quality of the equation

should not be judged by the adjusted-R2 statistic, but being able to explain approximately 88% of

the variation in oil prices with the independent variables used is satisfying. Although OPEC and

NONOECD have insignificant coefficients and the sign of OPEC is inthe unexpected direction, the

theory behind these variables commands that they must be included. The tests performed above

also show that the equation is not afflicted with multicollinearity or heteroskedasticity, however

the existence of serial correlation is inconclusive. The final equation (Equation 2) is presented

below:

NYMEX = - 262.886 + 2.889 INDPROD + 0.057 NONOECD - 0.169 STOCKS

(11.612) (0.737) (-2.139)

+ 0.064 OPEC

(0.702)

N = 42 Adjusted-R 2 = .8801 DW = 1.368




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FIELDPROD = the percentage change in U.S. production


Note: See Appendix Equation 2 for Data, Regression Output, Residuals, and Correlation

Matrix

Discussion and Conclusions

The equation shows that industrial output and the change in crude stocks have

significantly large impacts on the price of crude oil traded on the NYMEX. For each one-unit

increase in the Industrial Production Index, the price of crude should rise almost $3, holding all

other independent variables in the equation constant. Also, for each 1% increase in crude stocks

compared to the same month of the previous year, the price of crude should fall nearly $0.17,

holding constant all other variables in the equation. OPEC output and oil consumption in the

developing countries are also likely to be important drivers behind the price of crude, but in this

equation they are insignificant. The model can be used for judging the markets response to

changes in oil fundamentals, assessing the current price of oil, and creating an oil trading

strategy. Because the financial press devotes thousands of pages each year to covering the price

of oil, the equation above can be used to evaluate analysts interpretations of what moves the

market. Further research on possible proxies for political tension should be examined because

political concern is likely to have a positive impact on oil prices. Also, finding more accurate

and extensive monthly data on the developing worlds economic growth would increase the

precision of the model. As the developing worlds economic growth accelerates in the future and

data becomes available, another estimation of the equation with an increased sample size should

generate more robust results.

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Bibliography

Augilar-Conraria, Luis. Understanding the Impact of Oil Shocks. Federal Reserve Bank of St.

Louis. Jun. 2005.

Coimbra, Carlos. Oil Price Assumptions in Macroeconomic Forecasts. OPEC Review 28

(1976): 87-107.

Farivar, Maswood. Crude-Oil Futures Edge Higher As Inventories Post Sharp Drop. Wall

Street Journal, 1 Dec. 2005.

Flynn, Phil. Alaron Trading Company. Personal Interview. 20 Oct. 2005.

Goodman, Leah. Crude Drops Below $57 a Barrel. Wall Street Journal, 18 Nov. 2005.

Hoyos, Carola. IEA Urges Calm Over Energy Prices. Financial Times, 10 Nov. 2005.

McKay, Peter. Energy Prices Finally Calm Down. Wall Street Journal, 22 Nov. 2005.

McNutty, Sheila. US Grapples with The Age of Energy Insecurity. Financial Times, 21 Nov.

2005.

Padgett, Gary. Great Pacific Trading Company. Personal Interview. 3 Aug. 2005.

Petroleum. Worldbook Encyclopedia. 2000.

Pindyck, Robert. Volatility and Commodity Price Dynamics. Journal of Futures Markets 24

(1981): 1029-1047.

Serletis, Apostolos. The Cyclical Behavior of Monthly NYMEX Energy Prices. Energy

Economics 20 (1998): 265-270.

Studenmund, A.H. Using Econometrics: A Practical Guide. New York: Addison Wesley, 2006.

United States Energy Information Administration. Monthly Energy Review. Nov. 2005.

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United States Energy Information Administration. This Week in Petroleum. Sept-Dec. 2005.

Appendix

Equation 1.

Regression Results for Equation 1.

Dependent Variable: NYMEXMethod: Least SquaresDate: 11/30/05 Time: 20:41Sample: 2002:01 2005:06Included observations: 42

Variable Coefficient Std. Error t-Statistic Prob.

C -263.3541 25.45925 -10.34414 0.0000INDPROD 2.894178 0.249490 11.60038 0.0000NONOECD 0.061189 0.077049 0.794155 0.4323

STOCKS -0.176052 0.079707 -2.208745 0.0336OPEC 0.061559 0.091025 0.676293 0.5032

FIELDPROD 0.211721 0.233857 0.905347 0.3713

R-squared 0.894220 Mean dependent var 33.83429Adjusted R-squared 0.879529 S.D. dependent var 8.689565S.E. of regression 3.016061 Akaike info criterion 5.177344Sum squared resid 327.4785 Schwarz criterion 5.425583Log likelihood -102.7242 F-statistic 60.86590Durbin-Watson stat 1.400394 Prob(F-statistic) 0.000000

Correlation Matrix for Equation 1.

STOCKS OPEC NONOECD INDPROD FIELDPRODSTOCKS 1.000000 -0.153512 -0.100230 0.487337 0.111060

OPEC -0.153512 1.000000 0.083053 0.510144 -0.006444NONOECD -0.100230 0.083053 1.000000 -0.006297 -0.076037INDPROD 0.487337 0.510144 -0.006297 1.000000 0.046509

FIELDPROD 0.111060 -0.006444 -0.076037 0.046509 1.000000

Residuals for Equation 1.

obs Actual Fitted Residual Residual Plot

2002:01 21.0100 19.2563 1.75370 | . | * . |

2002:02 20.3400 18.0703 2.26971 | . | *. |2002:03 22.3300 22.3629 -0.03290 | . * . |2002:04 26.7400 24.7487 1.99125 | . | *. |2002:05 26.5800 26.5426 0.03737 | . * . |2002:06 24.8900 27.7317 -2.84168 | * | . |2002:07 26.5700 27.1877 -0.61772 | . *| . |2002:08 26.1600 28.4176 -2.25762 | .* | . |2002:09 27.4200 28.7411 -1.32105 | . * | . |2002:10 30.4000 28.0324 2.36763 | . | *. |

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2002:11 26.7100 29.6928 -2.98283 | * | . |2002:12 26.7900 27.6288 -0.83877 | . *| . |2003:01 31.2800 31.1533 0.12668 | . * . |2003:02 32.1200 31.0965 1.02351 | . |* . |2003:03 35.1800 31.6285 3.55152 | . | .* |2003:04 29.2000 27.1463 2.05366 | . | *. |2003:05 25.4700 27.5295 -2.05954 | .* | . |2003:06 30.0200 27.1727 2.84728 | . | * |2003:07 29.6500 27.5438 2.10619 | . | *. |2003:08 31.4600 28.9880 2.47200 | . | *. |2003:09 28.6300 28.4652 0.16475 | . * . |2003:10 28.5500 29.1924 -0.64236 | . *| . |2003:11 28.0400 32.5991 -4.55914 | * . | . |2003:12 29.0100 33.5845 -4.57450 | * . | . |2004:01 32.6700 35.0387 -2.36866 | .* | . |2004:02 33.7800 35.2113 -1.43131 | . * | . |2004:03 35.4900 34.7842 0.70581 | . |* . |2004:04 32.9200 37.3893 -4.46929 | * . | . |2004:05 36.6200 40.0361 -3.41605 | *. | . |2004:06 40.5000 37.5816 2.91837 | . | * |

2004:07 37.0100 40.9628 -3.95281 | *. | . |2004:08 41.8400 41.4789 0.36113 | . |* . |2004:09 41.9000 41.1784 0.72161 | . |* . |2004:10 47.6100 44.4186 3.19142 | . | * |2004:11 47.5200 44.2003 3.31972 | . | .* |2004:12 43.0800 44.3408 -1.26079 | . * | . |2005:01 39.8100 45.9339 -6.12389 | * . | . |2005:02 44.4700 46.4533 -1.98332 | .* | . |2005:03 48.6000 46.7094 1.89056 | . | *. |2005:04 53.7700 46.1458 7.62417 | . | . *2005:05 47.7400 46.7030 1.03704 | . |* . |2005:06 51.1600 47.9609 3.19914 | . | * |

Data for Equation 1.

obs FIELDPROD NONOECD NYMEX OPEC STOCKS2002:01 -0.677000 -4.470000 21.01000 -11.20200 8.8250002002:02 0.406000 -2.408000 20.34000 -10.01400 15.892002002:03 0.206000 5.127000 22.33000 -11.19300 8.0900002002:04 -0.421000 1.528000 26.74000 -13.02000 -1.7930002002:05 1.110000 9.017000 26.58000 -9.268000 -0.4070002002:06 -0.141000 -5.182000 24.89000 -5.321000 3.0190002002:07 -2.452000 -4.453000 26.57000 -7.312000 -2.7520002002:08 0.709000 0.852000 26.16000 -9.498000 -3.8050002002:09 -6.880000 3.367000 27.42000 -3.703000 -12.493002002:10 -0.889000 3.549000 30.40000 -0.538000 -6.948000

2002:11 4.350000 -3.619000 26.71000 0.526000 -7.7270002002:12 1.834000 -12.92300 26.79000 -2.691000 -11.015002003:01 1.501000 8.131000 31.28000 2.508000 -14.442002003:02 0.100000 -1.827000 32.12000 7.787000 -17.189002003:03 0.449000 17.59900 35.18000 9.162000 -15.567002003:04 -0.733000 -1.495000 29.20000 10.80600 -10.249002003:05 -0.703000 4.311000 25.47000 7.320000 -12.692002003:06 -0.565000 -6.310000 30.02000 5.339000 -10.379002003:07 -3.063000 -0.112000 29.65000 3.430000 -6.360000

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2003:08 1.244000 4.409000 31.46000 6.149000 -5.6450002003:09 1.579000 -1.316000 28.63000 4.132000 5.9150002003:10 -0.858000 1.179000 28.55000 3.248000 1.0890002003:11 -1.316000 5.370000 28.04000 2.745000 -2.3750002003:12 0.327000 -6.411000 29.01000 12.30400 -3.1480002004:01 -0.150000 7.504000 32.67000 10.35300 -0.8900002004:02 -0.258000 -5.883000 33.78000 4.832000 4.8800002004:03 0.928000 1.212000 35.49000 2.306000 5.5970002004:04 -1.444000 7.039000 32.92000 5.407000 4.0930002004:05 0.389000 7.190000 36.62000 4.500000 6.6490002004:06 -2.698000 -0.989000 40.50000 11.93300 7.1160002004:07 1.099000 -0.059000 37.01000 13.00700 3.3350002004:08 -2.283000 -2.749000 41.84000 10.15000 -0.3040002004:09 -5.078000 0.778000 41.90000 10.56100 -4.7810002004:10 1.861000 1.878000 47.61000 8.388000 -2.7080002004:11 4.648000 -4.525000 47.52000 6.528000 2.4930002004:12 0.321000 -8.431000 43.08000 5.099000 6.2730002005:01 -0.359000 11.11200 39.81000 4.242000 6.2690002005:02 1.383000 -5.102000 44.47000 4.881000 6.7910002005:03 0.530000 4.556000 48.60000 5.843000 7.198000

2005:04 -0.175000 8.464000 53.77000 6.379000 8.9930002005:05 0.118000 5.156000 47.74000 6.631000 9.2290002005:06 -1.214000 -9.968000 51.16000 2.897000 7.992000

Sources: Energy Information Administration website, Federal Reserve Economic Data website

Equation 2.

Regression Results for Equation 2.

Dependent Variable: NYMEXMethod: Least Squares

Date: 11/30/05 Time: 21:43Sample: 2002:01 2005:06Included observations: 42


C -262.8857 25.39188 -10.35314 0.0000INDPROD 2.889269 0.248822 11.61177 0.0000

NONOECD 0.056550 0.076691 0.737384 0.4655STOCKS -0.169321 0.079166 -2.138824 0.0391

OPEC 0.063705 0.090772 0.701815 0.4872

R-squared 0.891812 Mean dependent var 33.83429Adjusted R-squared 0.880116 S.D. dependent var 8.689565S.E. of regression 3.008702 Akaike info criterion 5.152238Sum squared resid 334.9346 Schwarz criterion 5.359103

Log likelihood -103.1970 F-statistic 76.24912Durbin-Watson stat 1.367571 Prob(F-statistic) 0.000000

Correlation Matrix for Equation 2.

STOCKS OPEC NONOECD INDPRODSTOCKS 1.000000 -0.153512 -0.100230 0.487337

OPEC -0.153512 1.000000 0.083053 0.510144NONOECD -0.100230 0.083053 1.000000 -0.006297

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INDPROD 0.487337 0.510144 -0.006297 1.000000

Residuals for Equation 2.


2002:01 21.0100 19.4402 1.56979 | . | * . |2002:02 20.3400 18.0661 2.27392 | . | *. |2002:03 22.3300 22.3067 0.02332 | . * . |2002:04 26.7400 24.7696 1.97035 | . | *. |2002:05 26.5800 26.2203 0.35970 | . |* . |2002:06 24.8900 27.7670 -2.87704 | * | . |2002:07 26.5700 27.6676 -1.09756 | . * | . |2002:08 26.1600 28.1915 -2.03151 | .* | . |2002:09 27.4200 30.0642 -2.64418 | * | . |2002:10 30.4000 28.1324 2.26762 | . | *. |2002:11 26.7100 28.7126 -2.00259 | .* | . |2002:12 26.7900 27.1976 -0.40762 | . *| . |2003:01 31.2800 30.6808 0.59923 | . |* . |

2003:02 32.1200 30.9595 1.16049 | . | * . |2003:03 35.1800 31.3423 3.83772 | . | .* |2003:04 29.2000 27.2420 1.95795 | . | *. |2003:05 25.4700 27.5684 -2.09838 | .* | . |2003:06 30.0200 27.2416 2.77843 | . | * |2003:07 29.6500 28.1335 1.51653 | . | * . |2003:08 31.4600 28.6551 2.80491 | . | * |2003:09 28.6300 28.1582 0.47181 | . |* . |2003:10 28.5500 29.3548 -0.80482 | . *| . |2003:11 28.0400 32.8102 -4.77021 | * . | . |2003:12 29.0100 33.5166 -4.50658 | * . | . |2004:01 32.6700 35.0161 -2.34613 | .* | . |2004:02 33.7800 35.2967 -1.51671 | . * | . |

2004:03 35.4900 34.5864 0.90361 | . |* . |2004:04 32.9200 37.6593 -4.73931 | * . | . |2004:05 36.6200 39.9279 -3.30787 | *. | . |2004:06 40.5000 38.1869 2.31310 | . | *. |2004:07 37.0100 40.7337 -3.72368 | *. | . |2004:08 41.8400 41.9461 -0.10606 | . * . |2004:09 41.9000 42.1930 -0.29298 | . * . |2004:10 47.6100 43.9645 3.64551 | . | .* |2004:11 47.5200 43.2158 4.30421 | . | . * |2004:12 43.0800 44.3094 -1.22944 | . * | . |2005:01 39.8100 45.9530 -6.14299 | * . | . |2005:02 44.4700 46.1817 -1.71167 | . * | . |2005:03 48.6000 46.5786 2.02137 | . | *. |

2005:04 53.7700 46.1600 7.60998 | . | . *2005:05 47.7400 46.6714 1.06864 | . |* . |2005:06 51.1600 48.2609 2.89912 | . | * |

Data for Equation 2.

obs FIELDPROD INDPROD NONOECD NYMEX OPEC STOCKS2002:01 -0.677000 98.56700 -4.470000 21.01000 -11.20200 8.8250002002:02 0.406000 98.43900 -2.408000 20.34000 -10.01400 15.89200

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2002:03 0.206000 99.32800 5.127000 22.33000 -11.19300 8.0900002002:04 -0.421000 99.71200 1.528000 26.74000 -13.02000 -1.7930002002:05 1.110000 100.0660 9.017000 26.58000 -9.268000 -0.4070002002:06 -0.141000 100.9930 -5.182000 24.89000 -5.321000 3.0190002002:07 -2.452000 100.6500 -4.453000 26.57000 -7.312000 -2.7520002002:08 0.709000 100.7140 0.852000 26.16000 -9.498000 -3.8050002002:09 -6.880000 100.6760 3.367000 27.42000 -3.703000 -12.493002002:10 -0.889000 100.2590 3.549000 30.40000 -0.538000 -6.9480002002:11 4.350000 100.5310 -3.619000 26.71000 0.526000 -7.7270002002:12 1.834000 100.0670 -12.92300 26.79000 -2.691000 -11.015002003:01 1.501000 100.5450 8.131000 31.28000 2.508000 -14.442002003:02 0.100000 100.5590 -1.827000 32.12000 7.787000 -17.189002003:03 0.449000 100.3760 17.59900 35.18000 9.162000 -15.567002003:04 -0.733000 99.60600 -1.495000 29.20000 10.80600 -10.249002003:05 -0.703000 99.53900 4.311000 25.47000 7.320000 -12.692002003:06 -0.565000 99.81300 -6.310000 30.02000 5.339000 -10.379002003:07 -3.063000 100.2780 -0.112000 29.65000 3.430000 -6.3600002003:08 1.244000 100.3520 4.409000 31.46000 6.149000 -5.6450002003:09 1.579000 101.0140 -1.316000 28.63000 4.132000 5.9150002003:10 -0.858000 101.1160 1.179000 28.55000 3.248000 1.089000

2003:11 -1.316000 102.0380 5.370000 28.04000 2.745000 -2.3750002003:12 0.327000 102.2570 -6.411000 29.01000 12.30400 -3.1480002004:01 -0.150000 102.6790 7.504000 32.67000 10.35300 -0.8900002004:02 -0.258000 103.4980 -5.883000 33.78000 4.832000 4.8800002004:03 0.928000 103.2110 1.212000 35.49000 2.306000 5.5970002004:04 -1.444000 104.0040 7.039000 32.92000 5.407000 4.0930002004:05 0.389000 104.9560 7.190000 36.62000 4.500000 6.6490002004:06 -2.698000 104.3770 -0.989000 40.50000 11.93300 7.1160002004:07 1.099000 104.9950 -0.059000 37.01000 13.00700 3.3350002004:08 -2.283000 105.3170 -2.749000 41.84000 10.15000 -0.3040002004:09 -5.078000 105.0620 0.778000 41.90000 10.56100 -4.7810002004:10 1.861000 105.8230 1.878000 47.61000 8.388000 -2.7080002004:11 4.648000 106.0350 -4.525000 47.52000 6.528000 2.493000

2004:12 0.321000 106.7430 -8.431000 43.08000 5.099000 6.2730002005:01 -0.359000 106.9480 11.11200 39.81000 4.242000 6.2690002005:02 1.383000 107.3610 -5.102000 44.47000 4.881000 6.7910002005:03 0.530000 107.3120 4.556000 48.60000 5.843000 7.1980002005:04 -0.175000 107.1840 8.464000 53.77000 6.379000 8.9930002005:05 0.118000 107.4340 5.156000 47.74000 6.631000 9.2290002005:06 -1.214000 108.2900 -9.968000 51.16000 2.897000 7.992000

Sources: Energy Information Administration website, Federal Reserve Economic Data website

Auxiliary Equations Used in Calculating VIFs.

Dependent Variable: INDPRODMethod: Least SquaresDate: 11/30/05 Time: 22:27Sample: 2002:01 2005:06Included observations: 42


C 102.0284 0.326063 312.9100 0.0000NONOECD 0.000973 0.049999 0.019461 0.9846

STOCKS 0.211323 0.038583 5.477105 0.0000

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OPEC 0.246962 0.043557 5.669907 0.0000

R-squared 0.587933 Mean dependent var 102.5887Adjusted R-squared 0.555401 S.D. dependent var 2.941805S.E. of regression 1.961543 Akaike info criterion 4.275733Sum squared resid 146.2108 Schwarz criterion 4.441225Log likelihood -85.79040 F-statistic 18.07268

Durbin-Watson stat 0.472237 Prob(F-statistic) 0.000000

Dependent Variable: NONOECDMethod: Least SquaresDate: 11/30/05 Time: 22:28Sample: 2002:01 2005:06Included observations: 42


C -0.508813 53.71062 -0.009473 0.9925INDPROD 0.010243 0.526324 0.019461 0.9846STOCKS -0.070717 0.167063 -0.423296 0.6745OPEC 0.057427 0.191781 0.299439 0.7662

R-squared 0.014745 Mean dependent var 0.740381

Adjusted R-squared -0.063038 S.D. dependent var 6.172633S.E. of regression 6.364216 Akaike info criterion 6.629652Sum squared resid 1539.123 Schwarz criterion 6.795144Log likelihood -135.2227 F-statistic 0.189566Durbin-Watson stat 2.432060 Prob(F-statistic) 0.902852

Dependent Variable: STOCKSMethod: Least SquaresDate: 11/30/05 Time: 22:29Sample: 2002:01 2005:06Included observations: 42


C -213.0143 38.89989 -5.475960 0.0000

INDPROD 2.087632 0.381156 5.477105 0.0000NONOECD -0.066365 0.156781 -0.423296 0.6745OPEC -0.607943 0.157707 -3.854900 0.0004

R-squared 0.458641 Mean dependent var -0.569786Adjusted R-squared 0.415903 S.D. dependent var 8.066933S.E. of regression 6.165256 Akaike info criterion 6.566129Sum squared resid 1444.394 Schwarz criterion 6.731621Log likelihood -133.8887 F-statistic 10.73125Durbin-Watson stat 0.619896 Prob(F-statistic) 0.000030

Dependent Variable: OPECMethod: Least SquaresDate: 11/30/05 Time: 22:30

Sample: 2002:01 2005:06Included observations: 42


C -187.9143 33.61505 -5.590184 0.0000INDPROD 1.855699 0.327289 5.669907 0.0000NONOECD 0.040992 0.136895 0.299439 0.7662STOCKS -0.462417 0.119956 -3.854900 0.0004

R-squared 0.473559 Mean dependent var 2.753167Adjusted R-squared 0.431997 S.D. dependent var 7.134466

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S.E. of regression 5.376958 Akaike info criterion 6.292515Sum squared resid 1098.644 Schwarz criterion 6.458008Log likelihood -128.1428 F-statistic 11.39425Durbin-Watson stat 0.571854 Prob(F-statistic) 0.000018

Park Test Data.

obs RESIDSQ LNZ LNRESIDSQ2002:01 2.464240 4.590737 0.9018832002:02 5.170714 4.589437 1.6430112002:03 0.000544 4.598428 -7.5171332002:04 3.882282 4.602286 1.3564232002:05 0.129385 4.605830 -2.0449622002:06 8.277362 4.615051 2.1135242002:07 1.204643 4.611649 0.1861832002:08 4.127040 4.612285 1.4175602002:09 6.991669 4.611907 1.9447192002:10 5.142083 4.607757 1.6374582002:11 4.010386 4.610466 1.388887

2002:12 0.166152 4.605840 -1.7948502003:01 0.359080 4.610605 -1.0242102003:02 1.346734 4.610745 0.2976822003:03 14.72810 4.608923 2.6897572003:04 3.833571 4.601222 1.3437972003:05 4.403182 4.600550 1.4823282003:06 7.719650 4.603298 2.0437692003:07 2.299865 4.607946 0.8328512003:08 7.867526 4.608684 2.0627442003:09 0.222607 4.615259 -1.5023502003:10 0.647728 4.616268 -0.4342842003:11 22.75490 4.625345 3.1247802003:12 20.30926 4.627489 3.011077

2004:01 5.504349 4.631608 1.7055382004:02 2.300400 4.639552 0.8330832004:03 0.816511 4.636775 -0.2027152004:04 22.46104 4.644429 3.1117822004:05 10.94198 4.653541 2.3926062004:06 5.350435 4.648009 1.6771782004:07 13.86581 4.653913 2.6294262004:08 0.011249 4.656975 -4.4874942004:09 0.085839 4.654551 -2.4552762004:10 13.28975 4.661768 2.5869932004:11 18.52621 4.663769 2.9191872004:12 1.511521 4.670424 0.4131162005:01 37.73628 4.672343 3.6306222005:02 2.929806 4.676197 1.0749362005:03 4.085945 4.675740 1.4075532005:04 57.91187 4.674547 4.0589222005:05 1.141997 4.676877 0.1327792005:06 8.404904 4.684813 2.128815

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Regression Results for Park Test

Dependent Variable: LNRESIDSQMethod: Least Squares

Date: 12/05/05 Time: 11:06Sample: 2002:01 2005:06Included observations: 42


C -78.97407 55.17128 -1.431435 0.1601LNZ 17.25491 11.91497 1.448170 0.1554

R-squared 0.049818 Mean dependent var 0.921850Adjusted R-squared 0.026063 S.D. dependent var 2.202682S.E. of regression 2.173788 Akaike info criterion 4.437268Sum squared resid 189.0142 Schwarz criterion 4.520014Log likelihood -91.18262 F-statistic 2.097197Durbin-Watson stat 2.081217 Prob(F-statistic) 0.155363

Park Test Residuals


2002:01 0.90188 0.23868 0.66321 | . |* . |2002:02 1.64301 0.21625 1.42676 | . | *. |2002:03 -7.51713 0.37138 -7.88852 |* . | . |2002:04 1.35642 0.43796 0.91846 | . |* . |2002:05 -2.04496 0.49911 -2.54407 | * | . |2002:06 2.11352 0.65822 1.45530 | . | *. |2002:07 0.18618 0.59952 -0.41334 | . *| . |2002:08 1.41756 0.61049 0.80707 | . |* . |2002:09 1.94472 0.60398 1.34074 | . | *. |

2002:10 1.63746 0.53236 1.10510 | . |* . |2002:11 1.38889 0.57911 0.80978 | . |* . |2002:12 -1.79485 0.49928 -2.29414 | * | . |2003:01 -1.02421 0.58151 -1.60572 | .* | . |2003:02 0.29768 0.58391 -0.28623 | . * . |2003:03 2.68976 0.55248 2.13727 | . | * |2003:04 1.34380 0.41961 0.92419 | . |* . |2003:05 1.48233 0.40800 1.07433 | . |* . |2003:06 2.04377 0.45543 1.58834 | . | *. |2003:07 0.83285 0.53563 0.29722 | . * . |2003:08 2.06274 0.54836 1.51439 | . | *. |2003:09 -1.50235 0.66181 -2.16416 | * | . |2003:10 -0.43428 0.67923 -1.11351 | . *| . |

2003:11 3.12478 0.83585 2.28893 | . | * |2003:12 3.01108 0.87284 2.13824 | . | * |2004:01 1.70554 0.94390 0.76164 | . |* . |2004:02 0.83308 1.08099 -0.24790 | . * . |2004:03 -0.20272 1.03307 -1.23579 | .* | . |2004:04 3.11178 1.16514 1.94664 | . | * |2004:05 2.39261 1.32237 1.07024 | . |* . |2004:06 1.67718 1.22691 0.45026 | . |* . |2004:07 2.62943 1.32878 1.30065 | . | *. |2004:08 -4.48749 1.38161 -5.86911 | * . | . |

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2004:09 -2.45528 1.33978 -3.79506 | * . | . |2004:10 2.58699 1.46432 1.12268 | . | *. |2004:11 2.91919 1.49885 1.42034 | . | *. |2004:12 0.41312 1.61368 -1.20056 | .* | . |2005:01 3.63062 1.64678 1.98384 | . | * |2005:02 1.07494 1.71329 -0.63835 | . *| . |2005:03 1.40755 1.70541 -0.29786 | . * . |2005:04 4.05892 1.68482 2.37410 | . | * |2005:05 0.13278 1.72502 -1.59224 | .* | . |2005:06 2.12882 1.86195 0.26686 | . * . |

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the price of crude oil on the nymex

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