Monthly Labor Review May 2005 41

Price Transmission

Price transmission within the PPI

for intermediate goods

Jonathan Weinhagen I

n 1978, the Bureau of Labor Statistics began

emphasizing the Producer Price Index (PPI)

commodity based stage-of-processing

system as its main analytical structure for

tracking changes in wholesale prices. This

system organizes manufacturers’ selling prices

according to the level of processing the

commodity has undergone. The three main

stage-of-processing categories are crude

goods, intermediate goods, and finished

goods. 1 For purposes of analysis, the

Consumer Price Index (CPI) could be added to

the structure as a fourth “stage of processing.”

The Bureau’s main aggregate PPI prior to

1978, the PPI for All Commodities, includes

price changes from all stages of wholesale

processing. However, the all commodities index

is criticized as a measure of aggregate inflation

because multiple counting of prices for

transactions at various stages of processing

within the index may result in a magnified and

distorted measurement of wholesale inflation.

Multiple counting problems arise when price

changes for basic commodities are passed

forward through the stages-of-processing. For

example, a price change in wheat that is passed

forward to prices for flour and bread would be

overemphasized in an aggregate index that

includes price changes for all three

commodities. The stage-of-processing system

was developed, in part, to improve multiple

counting issues within the PPI. Additionally, the

stage-of-processing system allows for analysis

of price transmission among the stages.

Several authors have examined the causal

relationships between prices within the stage-ofprocessing system. S. Brock Blomberg and Ethan

Harris investigated the connection between the

core CPI index, on the one hand, and the

Commodity Research Bureau spot index, the

Journal of Commerce index, the PPI for crude

goods, the National Association of Purchasing

Managers Price Index, and the Federal Reserve of

Philadelphia’s prices paid index, on the other

hand.2 Fred Furlong and Robert Ingenito analyzed

causality between CPI inflation and the

Commodity Research Bureau’s indexes for all

commodities and raw materials.3 Todd Clark

studied the relationships between PPI stage-ofprocessing indexes and the CPI by building

forecast models of the CPI using a vector

autoregression approach.4 Tae-Hwy Lee and

Stuart Scott used vector error correction models

to examine price transmission within the stage-ofprocessing system. 5

 Jonathan Weinhagen

studied the connection between the PPIs for

crude goods, intermediate goods, and finished

goods and the CPI by constructing impulse

response functions and variance decompositions

from vector autoregression models.6 In general,

previous studies found statistically significant

causal relationships from prices for less processed

A more detailed stage-of-processing classification model

and empirical analysis are used to examine causal price

relationships; results indicate that changes in component prices

produce significant movements in the PPI for finished goods

Jonathan Weinhagen

is an economist in the

Office of Prices and

Living Conditions.

The opinions

expressed in this

article are those

of the author’s and

do not necessarily

reflect those of the

Bureau of

Labor Statistics.
Price Transmission

42 Monthly Labor Review May 2005

Table 1. Relative importance values of the intermediate

 goods index, December 2001

Relative

importance

Intermediate materials, supplies, and components ....... 100.0

Materials and components for manufacturing ............. 47.0

Materials for food manufacturing ............................ 2.9

Materials for nondurable manufacturing ................... 14.1

Materials for durable manufacturing ........................ 9.2

Components for manufacturing ............................... 20.9

Materials and components for construction ............... 13.1

Processed fuels and lubricants ............................... 15.1

Manufacturing industries ....................................... 6.1

Nonmanufacturing industries .................................. 9.0

Containers ............................................................ 3.4

Supplies ............................................................... 21.4

Manufacturing industries ....................................... 4.7

Nonmanufacturing industries .................................. 16.7

Feeds ................................................................ 1.1

Other supplies .................................................... 15.6

 Index

goods to prices for more complete goods. However, Blomberg

and Harris; Furlong and Ingenito; Clark; and Weinhagen

discovered that the relationships deteriorated in the late 1980s.7

This article contributes to the literature on price

transmission by providing a more detailed stage-ofprocessing classification structure than previous analyses

and empirically examining the causal price relationships

within this structure. Additional detail is added to the current

stage-of-processing structure by subdividing the intermediate goods index into two separate stages—one that

includes prices for relatively less processed intermediate

goods and one that is comprised of relatively more processed

intermediate goods prices. A significant finding of this study

is that the expanded stage-of-processing classification

structure yields clear forward flowing causal pricing

relationships during the 1990s.

This article examines the composition of the intermediate

goods PPI to identify if it is feasible to subdivide the index

into multiple stages of processing. It then separates the

intermediate goods index into two stages of processing and

uses econometric techniques to study the causal

relationships between price changes at these stages. Next, it

develops an economywide empirical model of price

transmission within the stage-of-processing system that

incorporates prices from two intermediate stages of

processing. The final section concludes the discussion.

Intermediate goods

The first step of this analysis is to examine the composition

of the PPI for intermediate goods in detail to determine if it is

feasible to subdivide the index into multiple intermediate

stages by separating out prices for relatively less and more

processed commodities.

Composition. The PPI for intermediate goods comprises

prices for fully or partially processed commodities that will

be used as inputs to produce other goods. Specifically, the

index includes prices for five types of commodities: materials,

components, supplies, processed fuels and lubricants, and

containers. Materials are partially processed commodities

that will be further processed into other commodities.

Examples of materials include plastic resins, steel wire, and

treated wood. Components consist of complete commodities

purchased by businesses for assembly with other

commodities as inputs to production. Tires, capacitors, and

metal doors are examples of components. Supplies are

defined as fully processed commodities purchased by firms

to be consumed during the production process, but not as

part of the product being manufactured. Examples of supplies

include pens, business forms, and small cutting tools.

Processed fuels and lubricants are energy based commodities

used by firms during production; for example, gasoline,

commercial natural gas, and jet fuels. Containers are

commodities such as boxes, barrels, and steel cans, which

firms use during production. Table 1 presents the relative

importance values of the subcategories within the

intermediate goods index.

Feasibility. The subcategories of the PPI for intermediate

goods include prices for commodities that have undergone

differing levels of processing and are potentially inputs to

each other, leading to the possibility of price-pass-through

within the aggregate index. The most probable source of

price transmission within the intermediate goods PPI is

from materials prices to components prices. Materials are

relatively less processed than components and are often

used as inputs to the production of components,

suggesting that price changes in materials may pass

forward to prices for components. In addition to pricepass-through from intermediate materials to components,

price movements in all other categories within the

intermediate goods index could potentially pass through

to other categories within the intermediate goods index to

which they are inputs.

Empirical investigation

The next step in this analysis is to conduct an empirical

examination of internal flow within the PPI for intermediate goods

by analyzing the causal price relationships between the

subcategories of the index using a vector autoregression model.

Vector autoregressions model the variables of a system of

equations as a linear function of the past values of all

variables in the system.

8
Monthly Labor Review May 2005 43

 This empirical examination focuses on estimation of a

vector autoregression model using separate time series data

for prices for intermediate materials and intermediate

components, because materials are relatively less processed

than components and are often used as inputs to their

production. The time frame, 1990 to 2002, was chosen for

estimation of the vector autoregression model because

studies by Blomberg and Harris, Clark, Furlong and Ingenito,

and Weinhagen found a significant change in the price

transmission relationships within the PPI and CPI in the midto late 1980s.9

The current PPI structure includes materials indexes for

nondurable manufacturing, durable manufacturing, and food

manufacturing; however, an aggregate materials index is not

available. Accordingly, an aggregate index comprised of the

PPIs for materials for durable and nondurable manufacturing

was constructed using current BLS index methodology.10 The

PPI for food manufacturing was excluded from the

aggregation because commodities included in this index are

less likely to be used as inputs to components. The weights

used in the construction of the index are from the Census of

Manufacturing and were adjusted in each period by the

movements of the indexes. The constructed materials index

and the PPI for components were both seasonally adjusted

and converted to annualized percentage growth form.

Dickey-Fuller tests were conducted to determine if the indexes

expressed in annualized percentage growth form are

stationary.11 A time series is stationary if the mean, variance,

and covariance of the series are not dependent on time. Using

nonstationary time series to estimate a vector autoregression

invalidates conventional significance tests of models’

coefficients and can treat insignificant correlations between

variables as significant, even if both variables follow mostly

independent trends. The Dickey-Fuller tests included trend

and intercept terms and sufficient lags to ensure white-noise

residuals. The tests indicate that both time series are

stationary.

To determine the appropriate lag length of the vector

autoregression, the Schwarz criterion was implemented.12 The

criterion suggests that a first-order vector autoregression

model is optimal. Accordingly, a vector autoregression with

one lag of the monthly seasonally adjusted price indexes for

intermediate materials and intermediate components was

estimated using data from 1990 to 2002.

The unrestricted vector autoregression model was used

to conduct bidirectional Granger causality tests. A variable is

said to Granger cause a second variable when adding past

values of the variable to an autoregressive model of a second

variable improves the predictability of the second variable.

Wald statistics were used to test the null hypothesis of no

Granger causality. Wald tests are based on measuring the

extent to which the unrestricted estimates fail to satisfy the

restrictions of the null hypothesis.13 A small p (probability)

value of the Wald statistic rejects the null hypothesis of no

feedback to the dependent variable and a large p value of the

Wald statistic implies that the null hypothesis is not rejected.

A p value of less than .01 indicates rejection of the null

hypothesis at the 99-percent confidence level, whereas a p

value of .05 or less indicates rejection of the null hypothesis

at the 95-percent confidence level. Values of p that are greater

than .05 suggest acceptance of the null hypothesis of no

Granger causality. The following tabulation presents the

results of the Granger causality tests.

Null hypothesis: P value

PPI for materials does not Granger cause

PPI for components................................................... 0.027

PPI for components does not Granger

cause PPI for materials .............................................. .241

The Wald statistics indicate that, at the 95-percent

confidence level, changes in materials prices Granger cause

component prices, but changes in component prices do not

Granger cause materials prices. The presence of Granger

causality between prices for materials and components

suggests that internal price flow and multiple counting may

be present in the index for intermediate goods.

Vector autoregression coefficients are difficult to interpret

due to the multivariate nature of the models. Accordingly,

impulse response functions and variance decompositions

were developed to assist in interpretation of vector

autoregressions. Impulse response functions measure the

effect of a one-standard-deviation innovation of a variable in

a system of equations on current and future values of all

variables in the system. Variance decompositions show the

percentage of forecast error variance in one variable of the

vector autoregression explained by innovations to all

variables within the vector autoregression. 14

Because innovations within a vector autoregression are

generally not contemporaneously independent of each other,

a random innovation to one variable often occurs simultaneously with innovations to other variables. To overcome

this problem, the residuals may be orthogonalized by a

Cholesky decomposition in which the covariance matrix of

the residuals is lower triangular. Therefore, an innovation to

one variable in the system contemporaneously affects only

variables ordered after that variable in the vector autoregression.15

The residuals of the vector autoregression were

orthogonalized by a Cholesky decomposition in which

materials prices were ordered prior to components prices.

This ordering was chosen because materials are often used

as inputs to the production of components. In addition, the

Wald tests indicate that materials prices Granger cause

components prices. Subsequent to orthogonalization of the
Price Transmission

44 Monthly Labor Review May 2005

residuals, impulse response functions and variance

decompositions were constructed from the vector autoregression coefficients.

Chart 1 presents the accumulated impulse response

functions of one-standard-deviation shocks to materials and

components prices. Using EVIEWS 4.0, standard error bands

were constructed to represent the statistical significance of

the impulse response functions.16 The impulse responses

are significant at the 95-percent confidence level when both

standard error bands are simultaneously above or below zero

on the y-axis.

The accumulated impulse response functions in chart 1

show that price changes for materials transmit forward to

components prices. Specifically, a 4.5-percent (one-standarddeviation) unanticipated increase in materials prices results

in a 1.4-percent increase in prices for components. By

contrast, changes in prices for components do not result in

significant movements in materials prices. The impulse

Chart 1. Accumulated impulse response functions

Shock to PPI for materials

Response of PPI

for materials

Shock to PPI for components

Response of PPI

for components

Response function Plus or minus 2 standard deviations

1 5 9 13 17 21

-10

0

10

20

30

1 5 9 13 17 21

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

1 5 9 13 17 21

-10

0

10

20

30

1 5 9 13 17 21

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4
Monthly Labor Review May 2005 45

response functions imply a significant causal relationship

from materials prices to components prices, suggesting

multiple counting in the intermediate goods prices index.

The variance decompositions presented in the following

tabulation also indicate a causal relationship from materials

prices to components prices. After 24 months, innovations

to the PPI for materials account for nearly 17 percent of the

component price index’s forecast error variance, whereas

innovations to components prices explain less than 2 percent

of the forecast error variance of the PPI for materials.

 Variance decompositions after 24 months

Decomposition variable Percent of forecast errors due to:

PPI for materials PPI for components

PPI for materials ........ 98.17 1.83

PPI for components... 16.91 83.09

As discussed earlier, in principle there is a circular inputoutput relationship between prices for materials and

components, as well as other pair-wise stage-of-processing

prices; however, in practice this relationship is overwhelmingly forward flowing. The results from the impulse

response functions and variance decompositions support

this conclusion, as both tests indicate only forward price

transmission from materials to components.

An economywide model

Previous empirical analyses by Lee and Scott and Weinhagen

of the causal price relationships within the stage-ofprocessing system utilizing PPI data have included price

indexes for crude goods, intermediate goods, finished goods,

and the CPI.

17 This section develops a vector autoregression

model of price transmission within a stage-of-processing

framework in which prices for intermediate materials and

intermediate components are included in the regression

equations, as opposed to the aggregate intermediate goods

index.

Table 2. P values of Granger causality tests

 Test P value

Dependent variable: PPI for crude ..............................................................................................................

Null hypothesis ........................................................................................................................................

PPI for materials does not Granger cause PPI for crude ............................................................................... 0.180

PPI for components does not Granger cause PPI for crude ........................................................................... .415

PPI for finished does not Granger cause PPI for crude ................................................................................. .050

CPI does not Granger cause PPI for crude .................................................................................................. .088

PPI for materials/PPI for components/PPI for finished/ CPI do not Granger cause PPI for crude ........................... .100

..............................................................................................................................................................

Dependent variable: PPI for materials .......................................................................................................

Null hypothesis ........................................................................................................................................

PPI for crude does not Granger cause PPI for materials ............................................................................... .005

PPI for components does not Granger cause PPI for materials ...................................................................... .363

PPI for finished does not Granger cause PPI for materials ............................................................................ .774

CPI does not Granger cause PPI for materials ............................................................................................. .378

PPI for components/PPI for finished/ CPI do not Granger cause PPI for materials .............................................. .490

..............................................................................................................................................................

Dependent variable: PPI for components ....................................................................................................

Null hypothesis ........................................................................................................................................

PPI for crude does not Granger cause PPI for components ........................................................................... .458

PPI for materials does not Granger cause PPI for components ...................................................................... .015

PPI for crude/PPI for materials do not Granger cause PPI for components ....................................................... .042

PPI for finished does not Granger cause PPI for components ........................................................................ .342

CPI does not Granger cause PPI for components ........................................................................................ .261

PPI for finished/ CPI do not Granger cause PPI for components ...................................................................... .240

..............................................................................................................................................................

Dependent variable: PPI for finished ..........................................................................................................

Null hypothesis ........................................................................................................................................

PPI for crude does not Granger cause PPI for finished ................................................................................. .589

PPI for materials does not Granger cause PPI for finished ............................................................................ .669

PPI for components does not Granger cause PPI for finished ........................................................................ .036

PPI for crude/PPI for materials/PPI for components do not Granger cause PPI for finished ................................. .203

CPI does not Granger cause PPI for finished ............................................................................................... .634

..............................................................................................................................................................

Dependent variable: CPI ..........................................................................................................................................................................................

Null hypothesis ........................................................................................................................................

PPI for crude does not Granger cause CPI ................................................................................................. .073

PPI for materials does not Granger cause CPI ............................................................................................. .827

PPI for components does not Granger cause CPI ........................................................................................ .086

PPI for finished does not Granger cause CPI ............................................................................................... .005

PPI for crude/PPI for materials/PPI for components/PPI for finished do not Granger cause CPI ............................ .004

..............................................................................................................................................................
Price Transmission

46 Monthly Labor Review May 2005

Chart 2. Accumulated impulse response functions

Response function

Response of PPI

for crude

Response of PPI

for materials

Response of PPI

for components

Response of PPI

for finished

Response of CPI

Shock to PPI for crude Shock to PPI for materials

1 5 9 13 17 21

-30

-20

-10

0

10

20

30

40

50

1 5 9 13 17 21

-30

-20

-10

0

10

20

30

40

50

1 5 9 13 17 21

-10

-5

0

5

10

15

20

25

1 5 9 13 17 21

-10

-5

0

5

10

15

20

25

1 5 9 13 17 21

-0.5

0

0.5

1

1.5

2

2.5

1 5 9 13 17 21

-2

-1

0

1

2

3

4

1 5 9 13 17 21

-2

-1

0

1

2

3

4

1 5 9 13 17 21

-2

-1

0

1

2

3

1 5 9 13 17 21

-2

-1

0

1

2

3

Plus or minus 2 standard deviations

1 5 9 13 17 21

-0.5

0

0.5

1

1.5

2

2.5
Monthly Labor Review May 2005 47

Chart 2. Continued —Accumulated impulse response functions

 Shock to PPI for components Shock to PPI for finished Shock to CPI

1 5 9 13 17 21

-30

-20

-10

0

10

20

30

40

50

1 5 9 13 17 21

-30

-20

-10

0

10

20

30

40

50

1 5 9 13 17 21

-10

-5

0

5

10

15

20

25

1 5 9 13 17 21

-10

-5

0

5

10

15

20

25

1 5 9 13 17 21

-10

-5

0

5

10

15

20

25

1 5 9 13 17 21

-0.5

0

0.5

1

1.5

2

2.5

1 5 9 13 17 21

-0.5

0

0.5

1

1.5

2

2.5

1 5 9 13 17 21

-0.5

0

0.5

1

1.5

2

2.5

1 5 9 13 17 21

-2

-1

0

1

2

3

4

1 5 9 13 17 21

-2

-1

0

1

2

3

4

1 5 9 13 17 21

-2

-1

0

1

2

3

4

1 5 9 13 17 21

-2

-1

0

1

2

3

1 5 9 13 17 21

-2

-1

0

1

2

3

1 5 9 13 17 21

-2

-1

0

1

2

3

Response function Plus or minus 2 standard deviations

1 5 9 13 17 21

-30

-20

-10

0

10

20

30

40

50
Price Transmission

48 Monthly Labor Review May 2005

The vector autoregression model was constructed using

monthly PPI data for crude goods, intermediate materials,

intermediate components, and finished goods, and the CPI

for commodities from 1990 to 2002 as published by BLS. The

indexes excluded food and energy prices, and were

seasonally adjusted and converted to annualized percentage

growth form. Dickey-Fuller tests indicate that the indexes in

annualized percentage growth form are all stationary. To

determine the correct lag structure of the vector autoregression model, the Schwarz information criterion was

implemented. The criterion suggests that a vector autoregression whose equations have one lag is optimal; therefore, the vector autoregression was estimated using one lag

of all variables.

Wald tests were performed to examine Granger causality

among the indexes. Table 2 (page 45) presents the results of

the Granger causality tests. In addition to testing for Granger

causality from individual indexes to the dependent variable,

this analysis tested the joint lagged values of all variables at

stages of processing prior and subsequent to the dependent

variable for Granger causality. For example, the null

hypothesis that prices for crude goods and intermediate

materials do not jointly Granger cause intermediate components prices is indicated in table 2 as PPI for crude/PPI for

materials do not Granger cause PPI for components.

The Granger causality tests in all of the equations suggest

significant correlation at the 95-percent confidence level

between commodity prices at stages of processing prior to

the dependent variable. The Wald statistics show that in the

equations for materials prices, components prices, and the

CPI for commodities, the combined lagged values of prices at

stages of processing prior to the dependent variable are

jointly significant. In the finished goods prices equation,

although the combined lagged values of prices for crude

goods, materials, and components are not jointly significant,

the lagged values of components prices are statistically

significant. Alternatively, with the exception of lagged values

of finished goods prices in the crude goods equation, the

Wald statistics indicate that lagged values of prices at stages

of processing subsequent to the dependent variable are not

significantly correlated with the dependent variable at the

95-percent confidence level.

Impulse response functions and variance decompositions

were constructed from the orthogonalized set of residuals.

The residuals were orthogonalized using a Cholesky

decomposition with the following ordering: PPI crude, PPI

materials, PPI components, PPI finished goods, CPI commodities. This ordering was chosen because economic

theory predicts prices to transmit forward through the stages

of processing as input price changes are passed on to higher

level producers. In addition, the Granger causality tests

suggest that this ordering is correct. The accumulated

impulse response functions are presented in chart 2. (See

pages 46 and 47.)

The accumulated impulse response functions indicate that

price changes within the stage-of-processing system are

passed forward through the stages. This result is most clearly

illustrated by examining the stage-ahead responses of price

shocks throughout the stage-of-processing system. In every

instance, an unanticipated price shock results in a

statistically significant change in prices one-stage-ahead of

where the shock occurred. For example, a shock to materials

prices results in a significant movement in the components

PPI, a components price shock causes the index for finished

goods prices to change, and a random innovation in the

finished goods PPI causes a change in the commodities CPI.

In addition, the analysis indicates that a shock to the

components PPI also significantly affects the CPI for

commodities. The response of the commodities CPI to components prices is of particular interest because a previous

study, using data from 1990 to 2001, found that price changes

of intermediate goods do not cause price changes in the CPI

for commodities.18 By contrast, the impulse response

functions imply that price movements are not passed

backwards through the stages of processing. In no instances

do prices shocks within the stage-of-processing system

produce statistically significant price movements of indexes

at stages of processing prior to the stage at which the shock

originated.

The variance decompositions also indicate that price

changes are transmitted forward through the stages of

processing. Table 3 shows that the percentage of forecast

error variance due to price innovations occurring one-stageprior to the decomposition variable is substantial in every

case. Innovations to crude goods prices account for

approximately 38 percent of the forecast error variance in

materials prices; shocks to the materials PPI explain around

14 percent of the forecast error variance in components prices,

innovations to components prices account for close to 9

percent of the forecast error variance in the finished goods

PPI, and unanticipated changes in finished goods prices

explain approximately 9 percent of the forecast error variance

in the CPI for commodities. In addition, whereas one study

showed that only a small portion of the forecast error

Table 3. Variance decompositions after 24 months

Percentage of forecast errors due to:

Decomposition variable

CPI

PPI for crude ............. 94.55 1.51 0.97 1.47 1.50

PPI for materials ........ 38.32 58.63 2.35 .47 .23

PPI for components ... 5.00 14.52 78.66 1.19 .63

PPI for finished ......... 2.55 .44 8.88 87.97 .16

 . ......................... 1.43 2.16 13.37 9.52 73.53

PPI

for

crude

PPI

for

materials

PPI

for

components

CPI

PPI

for

finished
Monthly Labor Review May 2005 49

variance in the CPI for commodities can be accounted for

by intermediate goods price shocks, by subdividing the

overall intermediate goods index into two separate stages,

intermediate prices at the component level explain more

than 13 percent of the forecast error variance in the

commodities CPI.

19

Alternatively, the variance decompositions do not

indicate that price changes are passed backward through the

stages of processing.

Conclusion

The PPI for intermediate goods comprises prices for commodities

that have undergone varying degrees of processing. This article

used a vector autoregression methodology to examine the causal

relationships between prices for relatively less processed goods

and relatively more complete commodities within the PPI for

intermediate goods. The analysis began by constructing a

bivariate vector autoregression model using data from 1990 to

2002 of the PPIs for intermediate materials and components.

Impulse response functions and variance decompositions

implied that a price shock to materials caused a significant

positive change in the PPI for components, but that an

innovation to components prices did not result in a change

in the materials PPI. These results indicate forward price

flow within the PPI for intermediate goods and suggest that

multiple counting may occur within the aggregate index.

Utilizing the fact that the aggregate intermediate goods index

can be divided into multiple stages, an economywide vector

autoregression model, which included the PPIs for crude goods,

intermediate materials, intermediate components, finished

goods, and the CPI for commodities was developed to study the

causal price relationships between various stages of processing.

Impulse response functions and variance decompositions

constructed from the vector autoregression’s coefficients

implied that price changes pass forward through the stageof-processing system. In all instances, the impulse response

functions showed that price shocks within the system

caused statistically significant changes in prices at the stage

one-step-ahead of where the shock occurred. The findings

that changes in component prices result in significant

movements in both the PPI for finished goods and the CPI for

commodities are of particular interest because previous

analysis suggested that the causal relationships from the PPI

for intermediate goods to the PPI for finished goods and the

CPI deteriorated during the 1990s.20 

Notes

1

 For more information, see Bureau of Labor Statistics Handbook

of Methods, ch. 14, “Producer Price Indexes,” on the Internet at

www.bls.gov/opub/hom/pdf/homch14.pdf.

2

 S. Brock Blomberg and Ethan Harris, “The Commodity-Consumer

Price Connection: Fact or Fable?” Federal Reserve Bank of New York

Economic Policy Review, October 1995, pp. 21–38.

3

 Fred Furlong and Robert Ingenito, “Commodity Prices and

Inflation,” Federal Reserve Bank of San Francisco Economic Review,

1996, no. 2, pp. 27–47.

4

 Todd Clark, “Do Producer Prices Lead Consumer Prices?”

Federal Reserve Bank of Kansas Economic Review, third quarter,

1995, pp. 26–39.

5

 Tae-Hwy Lee and Stuart Scott, “Investigating Inflation Transmission by Stages of Processing,” in Robert Engle and Halbert White,

eds., Cointegration, Causality, and Forecasting: A Festschrift in Honor

of Clive W.J. Granger, ch. 12 (England, Oxford University Press,

August 1999), pp. 283–300.

6

Jonathan Weinhagen, “An Empirical Analysis of Price Transmission by Stage of Processing,” Monthly Labor Review, November 2002,

pp 3–11.

7 Harris and Blomberg, “The Commodity-Consumer Price Connection: Fact or Fable?” 1995, Furlong and Ingenito, “Commodity

Prices and Inflation,” 1996; Clark, “Do Producer Prices Lead

Consumer Prices?” 1995; and Weinhagen, “An Empirical Analysis of

Price Transmission,” 2002.

8

 William Greene, Econometric Analysis (Upper Saddle River,

New Jersey, Prentice-Hall Inc., 1997), pp. 815–16.

9 Blomberg and Harris, “The Commodity-Consumer Price Connection:

Fact or Fable?” 1995; Clark, “Do Producer Prices Lead Consumer Prices?”

1995; Furlong and Ingenito, “Commodity Prices and Inflation,” 1996; and

Weinhagen, “An Empirical Analysis of Price Transmission,” 2002.

10 See Handbook of Methods, on the Internet at www.bls.gov/

opub/hom/pdf/homch14.pdf.

11 David Dickey and Wayne Fuller, “Distribution of the Estimators

for Autoregressive Time Series with a Unit Root,” Journal of the

American Statistical Association, vol. 74, 1979, pp. 427–31.

12 Philip Hans Franses, Time Series Models for Business and

Economic Forecasting (Cambridge, United Kingdom, Cambridge

University Press, 1998).

13 Greene, Econometric Analysis, 1997, p. 161.

14 John Dinardo and Jack Johnston, Econometric Methods (New

York, McGraw Hill, College Division, 1996), pp. 289–301.

15 Dinardo and Johnston, Econometric Methods,1996.

16 EVIEWS 4.0 is a software program.

17 Lee and Scott, “Investigating Inflation Transmission by Stages

of Processing,” 1999 and Weinhagen, “An Empirical Analysis of

Price Transmission,” 2002.

18 Weinhagen, “An Empirical Analysis of Price Transmission,” 2002.

19 Ibid.

20 Ibid. Weinhagen showed these causal relationships.