46 Monthly Labor Review December 2006

Petroleum-to-Plastics Price Transmission Petroleum-to-Plastics Price Transmission

Price transmission: from crude

petroleum to plastics products

A structural vector autoregression model is used to analyze

the effects of crude-petroleum supply shocks on the market

for organic chemicals and plastics products; the analysis

demonstrates that changes in crude-petroleum prices

are passed on to prices and quantities of organic chemicals

and plastics products

Jonathan C.

Weinhagen Crude petroleum is an important input used

in the production of organic chemicals,

which are in turn used as inputs into the

production of more-processed goods, such as

plastics products. Prices for plastics products

compose a substantial portion of several aggregate producer price indexes (PPI’s), and these

indexes are often looked to as early indicators of

consumer inflation. Price changes in crude petroleum, which are transmitted to prices for plastics

products, would affect these aggregate PPI’s. A

thorough knowledge of the relationship between

prices for crude petroleum and prices for plastics

products, therefore, would help economists understand and explain movements of aggregate

PPI’s.

The main aggregate PPI’s that include prices

for plastics products are the indexes for All

Commodities, Finished Goods, Finished Goods

Excluding Foods and Energy, Intermediate

Goods, and Intermediate Goods Excluding Foods

and Energy. In December 2005, plastics products

accounted for 2.5 percent of the All Commodities

PPI. (PPI commodity weights are derived from

the 1997 Census of Manufactures and are updated by changes in PPI commodity indexes.) In

addition, plastics products accounted for 1.2 percent of the Finished Goods PPI, 2.0 percent of

the PPI for Finished Goods Excluding Foods and

Energy, 4.6 percent of the Intermediate Goods

PPI, and 6.1 percent of the PPI for Intermediate

Jonathan C.

Weinhagen is an

economist in the

Division of Index

Methods, Office of

Prices and Living

Conditions, Bureau of

Labor Statistics. E-mail:

weinhagen.jonathan

@bls.gov

Goods Excluding Foods and Energy. Two of these

main aggregate PPI’s exclude prices for energy,

insulating them somewhat from energy price

inflation; however, price changes in crude petroleum that are transmitted forward to organic

chemicals and then to plastics products represent one way in which aggregate PPI’s, which

exclude energy prices, may still be affected by

energy price shocks. In 2005, crude petroleum

accounted for 16.8 percent of the Crude Goods

PPI and organic chemicals accounted for 4.2

percent of the Intermediate Goods PPI.

The relationship between prices for crude

petroleum and prices for plastics products has

not been thoroughly examined, but price transmission within the gasoline market is well documented. Using January 1987–August 1996 weekly data on the spot price for West Texas intermediate crude oil, the New York Harbor spot price

for unleaded regular motor gasoline, and the selfservice pump price for unleaded regular motor

gasoline, with and without taxes, Nathan Balke,

Stephen Brown, and Mine Yucel estimated a

series of bivariate vector autoregression and vector error correction models.1

 Their study indicates

that crude petroleum price changes are passed

forward to consumer gasoline prices, but that

the response of gasoline prices to positive and

negative crude petroleum price shocks may be

asymmetric. In another study, Michael Burdette

and John Zyren used Department of Energy spot
Monthly Labor Review December 2006 47

and retail gasoline price data to estimate regression models.2

Their results show that most of the movement in retail prices

is determined by previous movements in spot prices. In an

earlier Monthly Labor Review article, Jonathan Weinhagen

used the PPI’s for Crude Petroleum and Gasoline, the CPI for

Gasoline, the quantity of domestically consumed gasoline,

and the Federal Reserve’s index of industrial production to

estimate a structural vector autoregression model of the

gasoline market.3 His study reveals that crude petroleum

supply shocks significantly affect prices for producer and

consumer gasoline. Because the relationship between prices

for crude petroleum and prices for plastics products is an area

that has seen little research, the analysis that follows attempts

to fill the gap by empirically examining price transmission

from crude petroleum to plastics products. Toward that end, a

structural vector autoregression (VAR) approach is used to

examine the effects of crude petroleum price shocks on the

market for plastics products from 1974 through 2003.

Price movements of three commodities

The analysis begins with an examination of historical price

movements of the PPI’s for Crude Petroleum, Organic Chemicals, and Plastics Products. In determining whether crude

petroleum price changes are transmitted to prices for organic

chemicals and plastics products, the relative timing of price

changes for those three commodities in response to five crudepetroleum supply shocks is examined visually in the form of a

chart. For each supply shock, the beginning of the acceleration, the peak, and the trough for the three time series presented are analyzed.

Chart 1 shows annual percentage changes in the PPI’s for

Crude Petroleum, Organic Chemicals, and Plastics Products

on a monthly basis from 1978 through 2003. The mean annual

percent changes in the PPI’s for those commodities over the

sample period are, respectively, 8.5, 3.7, and 2.5. The respective

standard deviations are 36.2, 10.6, and 3.2 percent, indicating

that both the standard deviations and the means of these

three series decline as the degree of processing of the product

increases.

The first crude-petroleum supply shock in the sample

period occurred as a result of the Iranian Revolution in

conjunction with the Iran-Iraq War.4

 In January 1978, Iranian

students began protesting against the shah of Iran, and on

January 16, 1979, the shah left Iran permanently. Then, on

September 23, 1980, Iraq invaded Iran. By 1981, the Organization of Petroleum Exporting Countries’ (OPEC’s)

production of oil decreased by 7 million barrels per day,

reducing the world oil supply 11.6 percent from its 1978

average.

Chart 1. Annual percent changes in the January PPI's for Crude Petroleum, Organic Chemicals, and

 Plastics Products

–

–

–

–

Percent Percent

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

-100

-50

0

50

100

150

200

250

-100

-50

0

50

100

150

200

250

PPI for Crude Petroleum PPI for Plastics Products PPI for Organic Chemicals

Iraq-Iran War

Iranian Revolution

OPEC production cut

Gulf War OPEC production cut OPEC production cut

PPI for Crude

Petroleum

PPI for Plastics

Products

PPI for Organic

Chemicals
48 Monthly Labor Review December 2006

Petroleum-to-Plastics Price Transmission

The second crude-petroleum supply shock occurred in

1986 and can be traced to an OPEC agreement to reduce

production. On August 4, 1986, reports surfaced of a probable

decrease in OPEC production, and on December 19, 1986,

OPEC agreed to reduce petroleum production by 7 percent in

the first half of 1987.

The third supply interruption occurred when Iraq invaded

Kuwait on August 2, 1990. In response to the Iraqi attack, the

United States invaded Iraq on January 16, 1991. The Gulf War

resulted in a production decline of approximately 4.3 million

barrels of petroleum per day from Iraq and Kuwait combined,

reducing world oil production approximately 7.2 percent from

its 1989 average level.

The fourth supply shock followed on the heels of a March

1999 OPEC agreement to cut production by 1.7 million barrels

per day, an amount that represented a 2.5-percent decline in

world oil production from the 1998 average.

The final major petroleum supply shock during the period

examined began at the end of 2001, when OPEC reduced

production and fears of a greater reduction due to probable

conflict in the Middle East drove prices higher. Average daily

OPEC production of crude oil fell by approximately 2.2 percent

from 2000 to 2001 and an additional 6.1 percent from 2001 to

2002.

For the three time series, the initial accleration, the peak,

and the trough resulting from each of the five supply shocks

were examined. The lone exception was the trough in the last

supply shock, which had not yet occurred at the end of 2003.

Exhibit 1 indicates the month in which each series began

accelerating, peaked, and reached a trough in response to the

five supply shocks.

In 9 out of 14 instances, the initial acceleration, the peak, or

the trough in the PPI for Crude Petroleum occurred prior to the

same event in the PPI for Organic Chemicals. In only 2 instances

did the initial acceleration, peak, or trough in the PPI for Organic

Chemicals precede the same event in the PPI for Crude Petroleum,

and in 3 instances the initial accelerations, peaks, or troughs of

the two series coincided. In 12 out of 14 instances, the initial

acceleration, the peak, or the trough in crude-petroleum prices

preceded that of plastics products prices. In only 1 instance did

the initial acceleration, peak, or trough in the PPI for Plastics

Products occur prior to the same event in the PPI for Crude

Petroleum, and only once over the entire period studied did the

initial accelerations, peaks, or troughs in the PPI’s for Plastics

Products and Crude Petroleum coincide.

In the majority of the cases examined in exhibit 1, price

changes for crude petroleum preceded price changes for

organic chemicals and plastics products. A visual analysis,

therefore, suggests that petroleum price shocks are passed

forward to prices for organic chemicals and plastics. In fewer

cases, movements in the indexes for organic chemicals and

plastics products coincided with or preceded changes in

crude petroleum prices. The anticipation of crude-petroleum

price changes may explain instances in which price changes

for plastics or chemicals preceded changes in petroleum

prices. For example, firms manufacturing organic chemicals

may contract with plastics firms to sell chemicals at a fixed

price over a given period. If petroleum prices are expected to

increase, the contract may stipulate a higher price for chemicals currently, which in turn could lead to a current increase in

plastics prices.

Empirical model of the plastics market

To examine price transmission from crude petroleum to organic

chemicals and plastics more rigorously, a structural vector

autoregression model of supply and demand within the

plastics market is estimated. The model examines only cases

in which price changes are passed forward through the stages

of production. However, a vector autoregression model also

can be used to examine the effect of expectations on variables

in the model. For example, Takatoshi Ito used a VAR model to

test the uncovered interest parity hypothesis, a hypothesis

which asserts that the interest rate spread between two substitutable assets is equal to the difference between the expected future exchange rate and the current exchange rate.5

 Ito

expresses the null hypothesis of the uncovered interest parity

hypothesis in the form of nonlinear cross-equational restrictions on a VAR system and uses Wald tests to test these

restrictions.

A VAR is a system of equations in which each variable is

expressed as a linear function of lagged values of itself and all

other variables in the system.6 Sufficiently identifying restrictions on the covariances of the error terms of an unrestricted

VAR allows the structural model to be estimated from the

reduced-form model.7

Unrestricted vector autoregression

The unrestricted VAR was estimated with historical monthly data

on the PPI’s for Crude Petroleum, Organic Chemicals, and Plastics

Products, and the Federal Reserve’s indexes for Plastics Production and overall Industrial Production, for the period from January

1974 through December 2003. All time series were expressed in

percentage growth form by taking first differences of the natural

logarithms of the data. In addition, seasonally adjusted data were

used when available. Seasonally adjusted data were not available

for the PPI’s for Crude Petroleum and Organic Chemicals, which

the Bureau of Labor Statistics has determined exhibit no statistically significant seasonal patterns. To avoid redundancy

between time series, the plastics components of the index for

Overall Industrial Production was removed through the use

of monthly relative importance values provided by the Federal

Reserve.
Monthly Labor Review December 2006 49

Accelerations, peaks, and troughs, crude petroleum, organic chemicals,

and plastics products, 1978–2003

 Supply shock Acceleration Peak Trough

 Crude petroleum

Iranian Revolution/ Iran-Iraq War December 1978 January 1980 April 1982

OPEC production cut, 1986 September 1986 August 1987 October 1988

Gulf War August 1990 October 1990 October 1991

OPEC production cut, 1999 March 1999 February 2000 November 2001

OPEC production cut, 2001 December 2001 February 2003 —

 Organic chemicals

Iranian Revolution/ Iran-Iraq War July 1978 January 1980 September 1982

OPEC production cut, 1986 September 1986 January 1989 April 1990

Gulf War June 1990 November 1990 November 1991

OPEC production cut, 1999 April 1999 July 2000 February 2002

OPEC production cut, 2001 March 2002 February 2003 —

 Plastics products

Iranian Revolution/ Iran-Iraq War August 1978 January 1980 March 1983

OPEC production cut, 1986 March 1987 August 1988 April 1990

Gulf War October 1990 February 1991 February 1992

OPEC production cut, 1999 April 1999 November 2000 March 2002

OPEC production cut, 2001 April 2002 April 2003 —

NOTE: Dash indicates trough not yet discernible.

Exhibit 1.

A time series is considered stationary if the mean, variance,

and covariance of the series exist and are independent of

time. Estimation of a VAR with nonstationary data invalidates

the tests used to determine the statistical significance of the

model’s coefficients and can indicate statistically significant

correlations between unrelated variables within the model.

These correlations are only the result of the underlying trends

in the variables, not the result of a related generating mechanism.8

 To test for stationarity, augmented Dickey-Fuller tests

were implemented.9 The tests included a trend and an intercept, and the Schwarz criterion was used to select the

optimal lag length.10 The augmented Dickey-Fuller tests

indicated that all of the time series are stationary in percentage

growth form.

Information criteria can be used to select the appropriate

lag length for a VAR. These criteria weigh the costs and

benefits of including additional lags in a model by rewarding

the increase in fit resulting from the additional lags, but

penalizing the loss of degrees of freedom. The three most

common information criteria used in econometric modeling

are the Akaike, Schwarz, and Hannan-Quinn criteria. Of these,

the Akaike criterion is the least strict in terms of penalizing

loss of degrees of freedom, whereas the Schwarz criterion is

strictest. The Hannan-Quinn information criterion was chosen

to select the optimal length of the VAR, because it falls between

the Akaike and Schwarz criteria in terms of strictness. Moreover, the Akaike criterion was not chosen, because it tends to

asymptotically overstate the optimal lag length for models.11

The Hannan-Quinn criterion suggested that a VAR whose

equations have one lag is optimal; therefore, the one-lag specification was chosen, and the unrestricted VAR was estimated

with ordinary least squares.

In addition to Dickey-Fuller tests, which determine whether

individual time series are stationary, a VAR can be tested for

stationarity by calculating the absolute eigenvalues from the

matrix of the VAR’s coefficients. A VAR is stationary when each

of its absolute eigenvalues is less than unity.12 The absolute

eigenvalues from the one-lag VAR estimated here fall between

0.67 and 0.11, indicating that the VAR is stationary and

reinforcing the earlier results from the Dickey-Fuller tests.

Structural vector autoregression

The residuals of a VAR are mutually contemporaneously

correlated, so a random innovation to one variable is likely to

occur simultaneously with innovations to other variables.
50 Monthly Labor Review December 2006

Petroleum-to-Plastics Price Transmission

Therefore, to determine meaningful economic conclusions

from the residuals, it is necessary to orthogonalize them.13

Orthogonalization is customarily achieved by a Cholesky

decomposition.14 This approach has been criticized because

it is often not supported by economic theory, leading to a set

of orthogonalized residuals that have no particular meaning.15

Alternatively, orthogonalization of the residuals can be

achieved by placing theoretically plausible contemporaneous

restrictions on an unrestricted VAR’s residuals, thereby allowing the structural disturbances to be estimated from the

reduced-form VAR and meaningful economic conclusions to

be drawn from the model.16 This is the approach used here.

The estimated variance-covariance matrix of the unrestricted VAR’s residuals contains n(n + 1)/2 distinct elements

(where n is the number of variables included in the VAR). To

obtain the structural disturbances from the reduced-form VAR

requires estimating an n × n matrix of coefficients that relates

the residuals to the orthogonal disturbances. Therefore, at

least n2 – n(n + 1)/2 = n(n – 1)/2 additional restrictions are

required to estimate the structural disturbances. Imposing

n(n – 1)/2 restrictions results in an exactly identified model, while

imposing more than n(n – 1)/2 yields an overidentified model.

The following equations describe the system of contemporaneous interactions among the VAR’s innovations that was

estimated:

(1) PCP = (â 1 × QIP) + upcp;

(2) POC = (â2 × PCP) + upoc;

(3) PPP = (â 3 × QPP) + (â4 × POC) + uppp;

(4) QPP = (–â 5 × PPP) + (â6 × QIP) + uqpp;

(5) QIP = uqip.

The estimated reduced-form VAR includes 5 variables, yielding

a variance-covariance matrix with 15 distinct elements. As

discussed earlier, at least 52 – 5(5 + 1)/2 = 10 additional

restrictions are required to estimate the 5-variable structural

model. The model described in equations (1)–(5) provides 14

restrictions and thus is overidentified.

All of the â coefficients of the five equations are presumed

to be positive, and PCP, POC, PPP, QPP, and QIP refer,

respectively, to innovations in the PPI’S for Crude Petroleum,

Organic Chemicals, and Plastics Products and the Federal

Reserve indexes of plastics production and industrial production. The u’s are mutually and serially uncorrelated error

terms.

The 14 restrictions in the system given by the model are

derived by assuming an upward-sloping supply curve and a

downward-sloping demand curve in the market for plastics

products. Equation (1) imposes 3 restrictions, allowing only

the quantity of industrial production to contemporaneously

affect the price of crude petroleum. Equation (2) provides an

additional 3 restrictions by assuming that the price of organic

chemicals is affected in the current period by only the price of

crude petroleum. Equation (3) imposes 2 more restrictions,

permitting the price of plastics products to be contemporaneously affected by only the quantity of plastics and the

price of organic chemicals. The positive sign of the coefficient

â3 indicates an upward-sloping supply curve. Equation (4)

allows the quantity of plastics to be affected in the current

period by only the price of plastics products and the quantity

of industrial production, resulting in 2 restrictions. The

negative sign in –â5 indicates a downward-sloping demand

curve. Finally, equation (5) imposes 4 additional restrictions

by assuming that industrial production is contemporaneously exogenous. In this system of equations, upcp, upoc, and

uppp are supply shocks, uqpp is a demand shock, and uqip is a

simultaneous shock to supply and demand.

The estimation results for the system of structural coefficients is shown in the following equations, where (1) indicates

statistical significance at the level of p = .0001 and (2) indicates

statistical significance at the level of p = .05:

(6) PCP = (.23 × QIP) + upcp

(7) POC = (.05 × PCP(1)) + upoc

(8) PPP = (.10 × QPP(2)) + (.11 × POC(1)) + uppp

(9) QPP = (–.29 × PPP) + (.97 × QIP(1)) + uqpp

(10) QIP = uqip

All of the signs of the structural coefficients are as anticipated.

Equation (6) indicates that shocks to the quantity of industrial

production affect crude petroleum price innovations positively. Equation (7) shows a weaker positive correlation between unanticipated changes in the price of crude petroleum

and unanticipated changes in the price of organic chemicals.

Equation (8) reflects the upward slope of the plastics supply

curve and indicates that shocks to the price of organic chemicals also are positively related to innovations in plastics prices. Equation (9) shows the downward slope of the plastics

demand curve and indicates that unanticipated changes in

the quantity of industrial production are positively correlated

with plastics quantity innovations.

The overidentification of the system allowed the likelihood

ratio test for overidentification to be applied. This test is a
Monthly Labor Review December 2006 51

 test of the validity of the system’s restrictions, where the null

hypothesis asserts that the identifying restrictions are valid.17

A p-value of less than 0.05 is required to reject the null

hypothesis. The test’s chi-square statistic and p-value were

3.8 and 0.43, respectively. The null hypothesis of the 14

overidentifying restrictions, therefore, was not rejected.

In addition to the system estimated, two alternative

specifications were attempted. The first omitted the insignificant QIP variable from equation (6); however, this

exclusion did not yield any changes to the structural coefficients as originally estimated, nor did it affect any of the

subsequent analyses (the impulse response functions or

variance decompositions). Because allowing a contemporaneous relationship between the price of crude petroleum

and the quantity of industrial production seems more theoretically plausible than not allowing the relationship, and

because both specifications yield identical estimates of equations (6) through (10), QIP was included in equation (6) of the

final specification.

The second alternative specification excluded the insignificant PPP variable from equation (9). This specification only

minimally altered the remaining structural coefficients, leading

to no large change in the sign or significance of any structural

coefficient. In the end, the PPP variable in equation (9) was

included in the final model, for two reasons. First, assuming

that the demand curve for plastics is downward sloping, as

opposed to completely inelastic, seems theoretically more

plausible. Second, the p-value of the coefficient for PPP in

equation (9) is 0.12, indicating that the coefficient is close to

being significant at the 10-percent level.

Impulse response functions

The orthogonalized set of residuals estimated in equations

(6) through (10) and the coefficients from the unrestricted

VAR were used to construct accumulated impulse response

functions. Impulse response functions measure the dynamic

effects of a one-standard-deviation shock to a variable in a

system on the current and future values of all variables in the

system.18 In addition, standard error bands were constructed

around the impulse response functions. An impulse response

function is considered significant when its upper band and

lower band are both above zero or both below zero. Chart 2

(pages 40 and 41) presents these impulse response functions.

Chart 2 illustrates the dynamic responses of all of the

variables in the VAR to the structural disturbances. Recall that

upcp, upoc, and uppp are supply shocks, uqpp is a demand shock,

and uqip is a simultaneous shock to supply and demand. The

first column in the chart demonstrates that a shock to upcp, the

price of crude petroleum, leads to increases in the prices of

crude petroleum, organic chemicals, and plastics products;

leads to a decrease in the quantity of plastics products; and

does not affect the quantity of industrial production. The

second column shows that a shock to upoc, the price of organic

chemicals, does not affect the price of crude petroleum,

increases the prices of organic chemicals and plastics products, and decreases the quantities of plastics products and

industrial production. The third column indicates that a shock

to uppp, the price of plastic, does not affect the price of crude

petroleum, increases the prices of organic chemicals and

plastics products, and decreases the quantities of plastics

products and industrial production. The fourth column demonstrates that a shock to uqpp, the quantity of plastics, does

not affect the prices of crude petroleum or organic chemicals

and leads to increases in the price of plastics products, the

quantity of plastics products, and the quantity of industrial

production. Finally, the last column of the chart indicates that

a shock to uqip, industrial production, tends to increase the

prices of crude petroleum, organic chemicals, and plastics

products and to increase the quantities of plastics products

and industrial production.

Variance decompositions

The orthogonalized set of residuals also was used to decompose variances. Variance decompositions show the percentage of variance in the forecast error in one variable of the

vector autoregression caused by innovations in the other

variables.19 The variance decompositions after 24 months are

presented in the following tabulation:

 Percent of forecast error due to—

Decomposition variable upcp upoc uppp uqpp uqip

Price of crude

petroleum ................. 98.52 0.09 0.05 0.35 0.99

Price of organic

chemicals .................. 16.91 73.75 7.32 .18 1.84

Price of plastics.......... 6.17 36.44 51.78 2.34 3.27

Quantity of plastics ... .31 2.88 2.90 65.26 28.65

Quantity of industrial

production ................ .57 1.67 1.71 9.28 86.77

The first row of the tabulation indicates that only shocks to

crude-petroleum prices explain a significant portion of the

variance in the forecast error for crude-petroleum prices. The

second row shows that, although innovations to prices for

organic chemicals are the most important factor in explaining

the variance in the forecast error for prices of organic

chemicals, shocks to the price of crude petroleum and to

plastics prices account for approximately 17 percent and 7

percent, respectively, of the variance. The third row demonstrates that unanticipated changes in the price of crude

petroleum, in the price of organic chemicals, and in plastics

prices all explain a substantial amount of the variance in the
52 Monthly Labor Review December 2006

Petroleum-to-Plastics Price Transmission

1 3 5 7 9 11 13 15 17 19 21 23 -0.040

0.000

0.040

0.080

0.120

1 3 5 7 9 11 13 15 17 19 21 23 -0.010

0.000

0.010

0.020

0.030

0.040

0.050

1 3 5 7 9 11 13 15 17 19 21 23 -0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.012

-0.008

-0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.006

-0.004

-0.002

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

1 3 5 7 9 11 13 15 17 19 21 23 -0.040

0.000

0.040

0.080

0.120

1 3 5 7 9 11 13 15 17 19 21 23 -0.010

0.000

0.010

0.020

0.030

0.040

0.050

1 3 5 7 9 11 13 15 17 19 21 23 -0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.012

-0.008

-0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.006

-0.004

-0.002

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

Chart 2. Impulse response functions

Period Period

Shock to —

Response of —

Price of

organic chemicals

Price of

plastics products

Price of

crude petroleum

Quantity of

plastics products

Quantity of

industrial production

__________ Response function ........................ Plus or minus two standard deviations

Percent

change

Percent

change u pcp u poc
Monthly Labor Review December 2006 53

1 3 5 7 9 11 13 15 17 19 21 23 -0.040

0.000

0.040

0.080

0.120

1 3 5 7 9 11 13 15 17 19 21 23 -0.010

0.000

0.010

0.020

0.030

0.040

0.050

1 3 5 7 9 11 13 15 17 19 21 23 -0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.012

-0.008

-0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.006

-0.004

-0.002

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

1 3 5 7 9 11 13 15 17 19 21 23 -0.040

0.000

0.040

0.080

0.120

1 3 5 7 9 11 13 15 17 19 21 23 -0.010

0.000

0.010

0.020

0.030

0.040

0.050

1 3 5 7 9 11 13 15 17 19 21 23 -0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.012

-0.008

-0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.006

-0.004

-0.002

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

1 3 5 7 9 11 13 15 17 19 21 23 -0.040

0.000

0.040

0.080

0.120

1 3 5 7 9 11 13 15 17 19 21 23 -0.010

0.000

0.010

0.020

0.030

0.040

0.050

1 3 5 7 9 11 13 15 17 19 21 23 -0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.012

-0.008

-0.004

0.000

0.004

0.008

0.012

0.016

0.020

1 3 5 7 9 11 13 15 17 19 21 23 -0.006

-0.004

-0.002

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

Period Period Period

Chart 2. Continued—Impulse response functions

Shock to —

__________ Response function ........................ Plus or minus two standard deviations

Percent

change

Percent

change

Percent

change u ppp u qpp u qip

NOTE: See text, p. 38, for definitions of upcp, upoc, uppp, uqpp, and uqip.
54 Monthly Labor Review December 2006

Petroleum-to-Plastics Price Transmission

forecast error for plastics prices, accounting for 6 percent, 36

percent, and 52 percent, respectively. Innovations to the

quantity of plastics account for only 2 percent of the variance

in the forecast error for plastics prices, while industrial production shocks explain only 3 percent of the variance. The

fourth row shows that unanticipated changes in the quantity

of plastics and in industrial production are the most important

factors in explaining the variance in the forecast error for the

quantity of plastics products. The former accounts for about

65 percent, the latter for approximately 29 percent, of the

variance. Price innovations in plastics also explain a much

smaller amount of the variance of the forecast error for the

quantity of plastics.

Two scenarios

Crude-petroleum prices are often extremely volatile and can

significantly affect the plastics market. Industrial production

also deviates substantially from its trend during periods of

economic downturn and upturn, and in these periods the

plastics market can be affected. Given the importance of crudepetroleum prices and industrial production to the plastics

market, the analysis next considers two scenarios in substantial detail.

Scenario 1: An unanticipated change in the price of crude

petroleum. A one-standard-deviation shock to crudepetroleum prices is approximately 8.2 percent. A monthly

percent change in crude petroleum prices of this magnitude is

common, especially during periods of acceleration or deceleration in the price of crude petroleum. The Iranian Revolution,

for example, caused oil prices to rise substantially from January 1978 through February 1979. During this period, monthly

price increases ranged from 0.3 percent to 19.6 percent, and

the average price increase was 4.4 percent.

A positive price shock to crude petroleum originates

through the error term in equation (6). The crude-petroleum

price shocks then lead to higher input costs for firms producing organic chemicals, causing the organic-chemicals

market supply curve to shift upwards. This shift in the supply

curve results in higher prices for organic chemicals, as is captured in equation (7). Increased prices for organic chemicals

then cause plastics firms’ input costs to rise and the plastics

market supply curve, described in equation (8), to shift upwards. This upward shift in the plastics supply curve in turn

leads to increased plastics prices and decreased quantities of

plastics.

According to the estimated model, in the month of its

occurrence a one-standard-deviation, 8.2 percent, positive

crude-petroleum price shock increases the prices of organic

chemicals and plastics products 0.4 percent and 0.04 percent,

respectively, and decreases the quantity of plastics 0.01 percent. The effects of the crude-petroleum price shock, however,

continue beyond that initial month, because, in the model,

each variable is a function of lagged values of all variables in

the system. The petroleum price shock affects organicchemicals prices for approximately 14 months, eventually

leading to a 2.02-percent price increase. The petroleum price

shock increases plastics prices approximately 0.6 percent after

14 months and decreases the quantity of plastics products

by about 0.2 percent after 11 months.

Scenario 2: An unanticipated change in industrial production. A one-standard-deviation shock to industrial production is approximately 0.6 percent. A monthly change of this

magnitude is common during periods of considerable acceleration or deceleration in industrial production. From December 2000 to December 2001, for example, the index for industrial

production fell significantly. Over this period, 1-month percent

declines ranged from 0.2 percent to 0.9 percent, and the average price decline was 0.4 percent.

A positive shock to industrial production originates

through the error term in equation (10). Equation (6) shows

that the industrial production shock contemporaneously

increases crude-petroleum prices, which rise as a result of

increased demand. The rise in crude-petroleum prices leads

to higher input costs for organic-chemicals firms, causing the

organic-chemicals market supply curve to shift upwards,

thereby increasing prices for organic chemicals, as indicated

in equation (7). Equation (8) shows that the increase in

organic-chemicals prices shifts the supply curve for plastics

products upwards, causing plastics prices to rise and quantities to fall. Finally, equation (9) demonstrates that the rise in

industrial production increases demand for plastics products,

causing both prices and quantities of plastics to increase.

Note that although the industrial production shock unambiguously increases plastics prices, its effect on the quantity of

plastics is uncertain, because the outward shift in the demand

curve increases quantity, but the upward shift in the supply

curve decreases quantity.

A positive one-standard-deviation, 0.6-percent shock to

industrial production contemporaneously increases plastics

prices and quantities approximately 0.06 percent and 0.6

percent, respectively. However, the VAR model implies that

shocks to industrial production can affect variables beyond

the current period. Indeed, the industrial production shock

continues to change the prices of plastics products for

approximately 12 months and eventually leads to a 0.44-

percent increase in the PPI for Plastics Products. The industrial production shock results in an approximate 1.3-percent

increase in the quantity of plastics after 6 months.

THIS ARTICLE HAS CONDUCTED AN EMPIRICAL INVESTIGATION

of price transmission from crude petroleum to organic chemicals
Monthly Labor Review December 2006 55

and plastics products. The analysis began with a visual

examination of price index data. The examination suggests

that in 21 out of 28 cases the initial acceleration, peak, or

trough in crude-petroleum prices, resulting from 5 crude

petroleum supply shocks, preceded the similar event in prices

for organic chemicals or plastics products. Thus, the visual

evidence supports the hypothesis that crude-petroleum price

changes are passed forward to prices for organic chemicals

and plastics products.

In order to analyze price transmission from crude petroleum to organic chemicals and plastics products more rigorously, a structural VAR model of supply and demand in the

plastics market was developed and estimated. The model

included the PPI’s for Crude Petroleum, Organic Chemicals,

and Plastics Products, as well as the Federal Reserve indexes

for plastics production and industrial production. Impulse

response functions and variance decompositions, calculated

from the structural VAR, showed that crude-petroleum price

shocks are transmitted forward to prices for organic chemicals

and plastics products.

Impulse response functions indicate that an unanticipated

change in the price of crude petroleum results in significant

positive changes in the prices for both organic chemicals and

plastics products and also tends to negatively affect the quantity of plastics products. In particular, a one-standard-deviation, 8.2-percent, positive crude-petroleum price shock eventually increases the prices for organic chemicals and plastics

products by 2.02 percent and 0.6 percent, respectively. The crude

petroleum price shock begins affecting prices for both organic

chemicals and plastics products in the same period in which the

shock occurred, and the effects continue for 14 months in each

case. The crude-petroleum price shock causes the quantity of

plastics products to fall 0.24 percent after 11 months.

The impulse response functions also demonstrate that

organic-chemicals price shocks significantly affect both the

price and quantity of plastics. A one-standard-deviation, 1.6-

percent, positive shock to organic-chemicals prices begins

affecting plastics prices in the current period, and the effects

of the shock continue for 12 months, after which the shock

leads to a 1.3-percent increase in plastics prices. The organicchemicals price shock begins affecting the quantity of plastics

products in the current period and results in a 0.7-percent

decrease in the quantity of plastics after 12 months.

The variance decompositions also confirm that prices for

crude petroleum and organic chemicals have important effects

on the market for plastics. The variance decompositions show

that 6.2 percent and 36.4 percent of the variance in the forecast

error for plastics prices can be explained by price shocks to

crude petroleum and organic chemicals, respectively. In

addition, the variance decompositions indicate that crude

petroleum price shocks explain approximately 17 percent of

the forecast error variance in prices for organic chemicals. 

Notes

1 Nathan Balke, Stephen Brown, and Mine Yucel, “Crude Oil and

Gasoline Prices: An Asymmetric Relationship,” Federal Reserve Bank

of Dallas Economic Review, first quarter 1998, pp. 1–11.

2 Michael Burdette and John Zyren, Gasoline Price Pass-through

(U.S. Department of Energy, January 2003), on the Internet at

www.eia.doe.gov/pub/oil_gas/petroleum/feature_articles/2003/

gasolinepass/gasolinepass.htm.

3

 Jonathan C. Weinhagen, “Consumer gasoline: an empirical investigation,” Monthly Labor Review, July 2003, pp. 3–10.

4

 Historical explanations of all but the final crude-petroleum supply

shock are from Petroleum Chronology of Events 1970–2000 (Energy Information Administration, May 2002), on the Internet at

www.eia.doe.gov/pub/oil_gas/petroleum/analysis_publications/

chronology/petroleumchronology2000.htm.

5

 Takatoshi Ito, “Use of (Time-Domain) Vector Autoregression to

Test Uncovered Interest Parity,” Review of Economics and Statistics

(May 1988), pp. 206–305.

6 William Greene, Econometric Analysis (Upper Saddle River, NJ,

Prentice Hall, 1997), esp. pp. 815–16.

7 See Ben Bernanke, “Alternative Explanations of the MoneyIncome Correlation,” in Karl Brunner and Allan Meltzer, eds., Real

Business Cycles, Real Exchange Rates, and Actual Policies, CarnegieRochester Conference Series on Public Policy, vol. 25, autumn 1986

(Amsterdam, North-Holland, 1986), pp. 49–99; and Christopher Sims,

“Are Forecasting Models Usable for Policy Analysis?” Federal Reserve

Bank of Minnesota Quarterly Review, winter 1986, pp. 2–16.

8

 Greene, Econometric Analysis, p. 846.

9

 For a detailed explanation of augmented Dickey-Fuller testing,

see Green, Econometric Analysis, pp. 848–51.

10 For a discussion of the Schwarz criterion, see Jack Johnston and

John Dinardo, Econometric Methods (New York, McGraw Hill College

Division, 1996), p. 74.

11 Ibid., p. 787.

12 Johnston and Dinardo, p. 288.

13 Ibid., p. 299.

14 Christopher Sims, “Macroeconomics and Reality,” Econometrica,

January 1980, pp. 1–48.

15 Sims, “Are Forecasting Models Usable?”; Bernanke, “MoneyIncome Correlation.”

16 Ibid.

17 See Quantitative Micro Software’s EViews 5.0 Users Guide, p.

723.

18 Johnston and Dinardo, Econometric Methods, pp. 299–300.

19 Ibid., p. 301.