the asymmetric transmission of oil prices to in ation ... · abstract otunderstand the response of...

The asymmetric transmission of oil prices to

in�ation: Evidence for the Euro area

Guillaume L'oeillet & Julien Licheron ∗

UNIVERSITY OF RENNES 1-CREM

Abstract

To understand the response of the monetary authorities regarding oil prices shifts, we have to

pay attention to their e�ects on the economy, and especially on in�ation and output. This paper

focus on the relationship between energy prices and the in�ation rates in Euro area between

1970 and 2007. It evaluates the asymmetry of the relationship and its stability over time. We

estimate an augmented Phillips curve with oil prices under linear and nonlinear forms to check

for asymmetry. On the whole period we �nd that oil prices signi�cantly a�ect in�ation, but

not systematically in an asymmetric way. The structural stability is then assessed by classical

econometric tests which display a break date during the third quarter of 1981, like Hooker (2002)

for the U.S. From this date, oil's in�uence in in�ation dynamics decreases substantially. The

asymmetry pattern is enhanced in the second period, after 1981. Three potential explanations

are tested in order to justify this evolution: the lower oil intensity, a new kind of oil shock and

the end of second-run e�ects in Euro area associated to an improvement in monetary policy.

The �rst and the third assumptions appear more relevant than the second.

J.E.L classi�cation: E31, E32, F41, Q43

Keywords: Monetary policy, Oil shocks, Nonlinearity

∗Université de Rennes 1, 7, Place Hoche - CS 86514 35065 Rennes Cedex, Corresponding author:[email protected]; Tel: 02.23.23.35.58

1

1 Introduction

Recent movements in oil prices conducts monetary authorities to revise their position regarding

oil prices since it constitutes a supply shock which could push up the in�ation rate while the

production falls under the potential level. The response of a Central Bank to this shock is

always a complicated question because low in�ation and high growth rate can not be achieved

simultaneously.

Svensson (2005) explains that the relationship between oil prices and the monetary policy

is not direct. The "principles of good monetary policy" lead authorities to assess the impacts

of an oil shock on their two aims: stabilize prices and maintain the level of output around

its potential. He suggests performing a forecast targeting in the case of oil price movements.

The model is in three steps. First, it consists in predicting future oil prices on the basis of

past and current prices. The second time of the analysis assesses the impact of the unexpected

change in oil price on in�ation and output gap forecasts. Finally, the modi�cations in the

expectations of the two variables conduct the authorities to revise their instrument and de�ne

a new interest-rate plan following the monetary rule.

In this paper, we focus on the second step and especially on the link between oil prices

and overall in�ation since price stability is considered as the �rst concern of the ECB. We

investigate a potential nonlinear transmission into in�ation which could explain an asymmetric

response of the authorities regarding oil prices.

Indeed, after the large decline in oil prices in the mid-eighties, Mork (1989), Mory (1993)

and Mork et al. (1994) show a nonlinear relationship between oil prices and activity. The

expected positive e�ects of a decline in energy prices are signi�cantly lower than the negative

impacts of rising oil prices. Monetary policy could play a role since policymakers would not

react in the same extent according to the direction of the energy prices' variation. In this

context, the negative e�ects of an oil price shock on output are enhanced by the restrictive

monetary policy, like Bernanke et al. (1997) suggest. Nevertheless, the Central Bank may have

an optimal reaction if the link between oil prices and overall in�ation is already asymmetric.

In�ationary pressures are more probable following an upsurge in oil prices than de�ation after

a drop in the presence of price rigidities.

2

Many papers deal with the link between oil prices and in�ation by estimating an extended

Phillips curve with oil prices and also investigate both nonlinearity and the stability of the

relationship. Hooker (2002) and Leblanc & Chinn (2004) use asymmetric oil prices indicators

and conclude to a signi�cant and immediate e�ect of crude oil prices on headline in�ation, for

the United States and the G-5 countries respectively. The �rst paper �nd that the structural

break speci�cation performs better than asymmetric speci�cations in order to characterize the

relationship. Leblanc & Chinn (2004) apply recursive estimates of the coe�cients associated to

oil prices, and conclude to a clear decline, especially from 1999. More recently, Van Den Noord

& Christophe (2007) and De Gregorio, Landerretche & Neilson (2007a) also work out on this

relationship and look for structural shifts in a large set of countries. Those papers insist

on the reduction of the energy intensity as the main potential explanation for the declining

relationship between oil prices and in�ation. Other hypotheses are tested and/or provided.

Hooker (2002) proposes for instance the deregulation of the energy-producing and consuming

sectors and monetary policy. He �nally concludes that the more convincing argument was the

Taylor (2000)'s one. The declining pass-through would be mainly due to the move to a new

low in�ation regime. Leblanc & Chinn (2004) list other reasons like the reduced formal wage

indexing arrangements and the strenghtening of competition in markets. The exchange rate has

also played a role according to De Gregorio, Landerretche & Neilson (2007a) who extend the

study to a large set of countries. Chen (2009) estimates a time-varying pass-through coe�cient

for 19 industrialized countries. He includes an error correction term in the extended Phillips

curve considering that CPI, oil prices and output are likely cointegrated. The appreciation of

the domestic currency, a more active monetary policy and a higher degree of trade openness

help to explain the declining pass-through between oil prices and in�ation rates. Others authors

also introduce commodity prices (Goodhart & Hofman (2005)) or oil prices (Roberts (1995)) in

a new-keynesian Phillips Curve in order to create a supply shock (see Mehra (2004)). It allows

achieving the signi�cance of the output gap parameter of the aggregate supply curve.

The main objective of this paper is to disentangle the nonlinearity occuring between oil

prices and overall in�ation from the supposed asymmetric reaction of the policymaker. For

this, we proceed into two steps. In a �rst step, we estimate this extended aggregate supply

curve with and without in�ation expectations for three oil price speci�cations. Firstly, we use

the growth rate of oil prices. Secondly, we introduce two nonlinear variables in order to detect a

potential nonlinear link between energy prices and in�ation. We separate positive from negative

3

variation of oil prices and then we keep the biggest oil price increases. We check robustness

with alternative indicators of in�ation and output. In a second step, we assess the stability of

the relationship for each speci�cation used with Bai & Perron (1998) and Andrews & Ploberger

(1994) tests. At last, we explore three potential explanations for the break found in 1981:3:

the role of the lower oil addiction in european economies, the nature of the last demand driven

oil shock, and the role of monetary policy through the second round e�ects.

The regressions of the augmented Phillips curve display a signi�cant in�uence of oil prices

on in�ation and especially the bigger increases of oil prices, what denotes a partial nonlinear

transmission. The speci�cation of the Phillips curve, with or without term interest rates spread,

does not modify this result. Our results are quite unsensitive to alternative indicator of activity

but di�ers when we change the in�ation measure. Break tests performed indicate that the

relationship weakened around the third quarter of 1981 during the peak of the second oil shock.

Moreover, the asymmetry seems higher during the second period since the oil prices decreases do

not enter signi�cantly anymore. The ECB would thus not create asymmetry between oil prices

and activity but only translates the one existing between crude oil prices and the aggregate

price level. The assessment of the hypotheses indicates that the declining oil dependence in

Europe accounts de�nitly for the unstability of the relationship. The investigation of the wage-

price spiral is convincing since we observe a slight di�erence in the response of wages to an

oil price shock between both periods, while the GDP de�ator remains sensitive to a growth in

wages. Finally, the last explanation remains open since the distinction between the nature of

oil shocks during seventies and the more recent shock is disputable.

The rest of the paper is organized as follows. The second section deepens the implications

of oil prices spikes on in�ation, and the role of the Central Bank. Then, the third section

proceeds to the estimation of the augmented Phillips curve including asymmetric indicators of

oil prices. Section 4, completes the empirical work and provides some potential explanations.

Finally, section 5 concludes and provides the insights for future works.

2 Theoretical aspects and stylised facts

Rising oil prices are expected to translate into higher in�ation rates through at least three

mechanisms, that are closely related and interdependent. Those channels are described in

4

Figure (1). Firstly, a direct impact occurs by the transmission from crude oil prices to the

prices of re�ned products (such as heating oil and fuels for transport) that are directly included

in the Consumer Price Index (CPI). Energy is indeed part of the households' consumer basket.

Eurostat sets a weight equal to 8.60% for energy in the overall index. Brook et al. (2004)

set the weight of transport, fuel and lubricants in the CPI to 4.2% in euro area1. Secondly,

oil price increases have an indirect impact on consumer prices through producer prices. Since

oil is an input for �rms, they may adjust the prices of �nal goods and services to shifts in

energy prices, which would �nally a�ect CPI in�ation. Thirdly, there may be further medium-

term repercussions for headline in�ation if oil price increases translate into higher in�ation

expectations and higher wages. Oil price increases may therefore trigger a wage-price spiral

and in turn generate "second-round" e�ects: households would take into account the direct and

indirect e�ects of oil prices on in�ation (�rst-round e�ects) in the subsequent wage-bargaining

process, so as to compensate for the decline in real income. A strong downward rigidity of

wages and a high negotiation power of labour unions should at least prevent wage adjustments

intended to compensate the rise in production cost due to higher oil prices2.

Figure 1: Transmission channels between oil prices and in�ation

Source: ECB

1It respectively reaches 3.1% in U.S and 1.8% in Japan.2Blanchard & Gali (2007) show, within a New-Keynesian model, that the reduction in real rigidities and a

greater �exibility on labour markets may partly explain the lower impact of oil prices on in�ation and outputnowadays compared to the seventies.

5

Table (1) provides some estimates from previous papers measuring the impact of oil shocks

on in�ation rate3. We observe that coe�cients di�er among the countries selected. The United

States and the Euro area su�er from larger in�ation pressures than in Japan for all papers but

Leblanc & Chinn (2004)'s one. Some di�erences of e�ects on in�ation arise according to the

indicator of the level of prices (core in�ation or GDP de�ator) as show Huntington (2005) for

U.S., or Christophe & Pierdzioch (2002).

Table 1: Impact of a 50% oil price shock onin�ation extracted from several papers, inpercentage points

U.S. Euro Area Japan

IMF (2000) 2,5 2,2 1,55Hunt et al. (2001) 1,3 1,3 0,6Brook et al. (2004) 1,15 0,7 0,4

Leblanc & Chinn (2004) 4 2,5a 5,5Huntington (2005) (U.S) 1,1 - -

a The �gure is for Germany here.

In�ation would thus be the unavoidable aftermath of an oil price increase. However, the

stylised facts reported in Figure (2) and Figure (3) reveal that the oil prices-in�ation relationship

is not as clear-cut as expected in the euro area.

3We calculate for each of them the e�ect of a 50% oil shock in order to compare easily the response ofin�ation.

6

Figure 2: Oil prices and headline in�ation in the euro area (1970:I - 2007:III)

Sources: OECD Economic Outlook and EIA.

Figure 3: Oil prices (in U.S. dollars) and headline in�ation in the euro area (1970:I - 2007:III)

Sources: OECD Economic Outlook and EIA.

The �rst graph represents the long term evolution of oil prices in dollars and in euros

expressed in nominal terms while the second reports the short term relationship between oil

prices in dollars and GDP de�ator change rates. The latter graph shows that the two big oil

shocks of the seventies (1973 and 1979) were indeed followed by stronger in�ation rates. The

oil shock arising in 1991 had however more limited e�ects on the GDP de�ator in the euro

area. Above all, the large and continuous increase in crude oil prices since the beginning of

the 2000's has not translated into a renewal of in�ation, as measured by the GDP de�ator

7

(Figure (2)). This observation suggests a smaller in�uence from oil prices on the dynamics of

in�ation nowadays, and that other factors should play a greater role. Most notably, the process

of globalisation may have slowed down the in�ationary pressures with the expansion of "low-

cost" goods in international trade, as emphasized by Borio & Filardo (2007). The improvement

in monetary policy and the anchoring of in�ation expectations at low levels may have limited

in�ation recoveries, especially by lowering second-round e�ects. The recent appreciation of the

euro versus the U.S. dollar has also alleviated the cost of imported goods bought in dollars,

such as oil. Finally, industrial innovations lead euro area countries to reach a reduced energy

intensity, with more energy-e�cient capital goods

We perform bivariate granger causality tests (see Granger (1969)) between oil prices change

rates under di�erent forms and three in�ation indicators: Harmonized Index Consumer Price,

the GDP de�ator and the Personal Consumption Expenditure (PCE) de�ator4. Oil prices

would Granger-cause in�ation if lagged values of both variables provide statistically signi�cant

information about future values of the general level of price. We test four combinations of Brent

spot prices: real and nominal prices in U.S dollars and in domestic currency. We select four

and eight lags because we work on quarterly variables and oil prices do not "granger cause"

signi�cantly in�ation before four periods. Statistics of the test suggest that the choice of the

indicator of in�ation matters5 in the relationship since only the GDP de�ator is signi�cantly

"Granger caused" by oil prices during the �rst and second years. While the HICP indicator is

highly "caused" by oil prices, the PCE de�ator is only "caused" by oil price �uctuations in the

second year. We also note that the currency in�uences the causal relationship since oil prices in

euros help to predict in�ation in a greater extent, when GDP de�ator is considered, while only

oil prices in dollars "Granger cause" the HICP when we take a longer term horizon (8 lags).

The di�erence in the signi�cance level between both timing means that the pass-through of oil

price into oil prices in euros is strong at the end of the �rst year and then tend to disappear,

except for the PCE de�ator. Inversely, the di�usion of oil prices on PCE de�ator is longer.

4It measures in�ation based on changes in personal consumption. Unlike the CPI, which is based on a �xedbasket of goods, the PCE de�ator takes into account the shifts in domestic personal consumption.

5Christophe & Pierdzioch (2002) explain that it is important to distinguish between di�erent price indicatorswhen modeling the link between oil prices and overall in�ation, because it has non-negligeable implications onthe monetary policy. They found that targeting a GDP de�ator objective is an inferior strategy.

8

Table 2: Granger Causality Test on oil prices and In�ation - 1970:I -2007:IIIa

π = HICP π = GDP DEFLATOR π =PCE De�atorLags=4

Real oil prices in euros do not Granger Cause π 2.47685* 3.47707*** 1.77193

Nominal oil prices in euros do not Granger Cause π 2.49853* 3.47872*** 1.76694

Real oil prices in USD do not Granger Cause π 2.96237** 3.25935** 1.49085

Nominal oil prices in USD do not Granger Cause π 2.98685** 3.27052** 1.48962

Lags=8

Real oil prices in euros do not Granger Cause π 1.68366 2.61862** 2.01547**

Nominal oil prices in euros do not Granger Cause π 1.69630 2.63866** 2.02425**

Real oil prices in USD do not Granger Cause π 2.22230* 2.47398** 1.58701

Nominal oil prices in USD do not Granger Cause π 2.22882* 2.49378** 1.59225

a The statistic is a F-test with respectively 46 for HICP (only available from 1990) and 146 observations for theGDP de�ator and PCE de�ator with 4 lags, and 42 and 142 observations with 8 lags.

3 Oil-augmented Phillips curve

3.1 The framework

The equation is assumed to capture the short run �uctuations of the in�ation dynamic. Seem-

ingly to Hooker (2002), Leblanc & Chinn (2004) and De Gregorio et al. (2007a) we modify

the speci�cation of the usual Phillips curve by introducing the exogenous crude oil prices. We

compare two kind of speci�cations. One includes an interest spread modelling in�ation expec-

tations and another one which has a pure backward-looking form. The estimated equations

take the following forms:

πt = α +j∑i=1

βiπt−i +k∑i=1

κist−i +l∑

i=1

γiyt−i +m∑i=1

ψioilt−i + εt (1)

and

πt = α +j∑i=1

βiπt−i +l∑

i=1

γiyt−i +m∑i=1

ψioilt−i + εt (2)

9

where π is the level of in�ation measured by the GDP de�ator, s is the spread between

the short-term interest rate (3 months) and the long-term interest rate (10 years) constituting

in�ation expectations, y is the output gap calculated with a Hodrick-Prescott �lter and ∆ oil is

the energy prices variable. Finally, εt is the aggregate supply shock assumed to be independantly

and identically distributed with homoskedastic variance σ2. We regress the annualised quarterly

change rate of all variables. The construction of the variables is detailed in Appendix A.

The use of an interest rate spread to design expectations of in�ation is justi�ed to avoid a

multicollinearity problem that could exist between leads of in�ation and contemporaneous oil

prices. The former could already contain the e�ects of the latter variable. This speci�cation

allows for distinguishing the impact of current oil price variation from the e�ect of expected

in�ation on the current in�ation rate. Moreover, there is no forecast data of in�ation on the

whole period and it appears that the term structure is really informative on the future level of

in�ation rate according to Gerlach (1997) and Mishkin (1990).

The key point of our study deals with oil prices (oil) and their speci�cation. We test three

speci�cations. First, we use the oil price change rate to get a linear form. Then we test the

potential nonlinear pattern with two asymmetric indicators. On the one hand, we separate

positive (∆oil+) from negative (∆oil−) variation of oil prices. On the other hand, we use the

Net Oil Price Index (NOPI ) from Hamilton (1996) which distincts the biggest increases in oil

prices from the increases that only compensate an earlier decrease. Oil prices are expressed in

the domestic currency to take into account the value of the bilateral exchange rate with the

U.S Dollar which can smooth or aggravate oil price movements. Those prices are also used in

real terms.

We do not apply the same number of lags to each variable as De Gregorio et al. (2007a).

We rather seek the "best" speci�cation of the equation according to the Akaike criteria. For

this, we select the optimal lag (between 1 and 4) for each variable successively added in the

equation. For each oil price's speci�cation, we obtain the same speci�cation:

πt = α +4∑i=1

βiπt−4 +2∑i=1

κist−2 +3∑i=1

γiyt−3 +3∑i=1

ψioilt−3 + εt

10

and

πt = α +4∑i=1

βiπt−4 +3∑i=1

γiyt−3 +3∑i=1

ψioilt−3 + εt

3.2 Estimation results

We regress quarterly variables in a sample which begins in 1970:I and ends in 2007:III. The

econometric method used is Generalized Least Squares (GLS). Table (3) reports the estimation

results. The quality of the model is very satisfying with an adjusted R-squared around 0.90. The

serial correlation tests show that regressions are not a�ected by autocorrelation. We observe

that oil prices play a signi�cant role in in�ation dynamics. The two speci�cations tested

show quite similar results regarding the magnitude of the coe�cients and their signi�cance

levels. The extent of the coe�cients of oil prices variables are contained between 0.0012 and

0.0020. In the linear case including the spread of interest rates for example, when oil prices

increase by 10% in the previous quarter, the contemporaneous in�ation rises by 0.02%. The

cumulated e�ect can reach almost 0.045%. Headline in�ation is positively a�ected by the oil

prices change rate and more often by positive variation. Indeed, three of the four lags of the

positive variation in oil prices are clearly signi�cant while only the third lag of the negative

variation is weakly signi�cant. The asymmetry is not complete however since the coe�cient

associated to the negative variation compensate the cumulative e�ects of positive variation.

Regressions including the NOPI variable validate however the nonlinear pattern. The biggest

crude oil price increases, seen as oil shocks, push up the in�ation rate6.

The robustness of those results is tested in two ways. We use alternative indicators of

activity by constructing an unemployment gap variable with the Hodrick and Prescott �lter.

It calculates deviations of unemployment from its trend. We also compare the GDP de�ator to

the growth rate of the Private �nal Consumption Expenditure (PCE) de�ator. The estimation

results of the regressions are reported in Appendixes B and C. We note that results are close to

the baseline case when we replace the activity indicator: coe�cients appear clearly signi�cant

and asymmetry is still only displayed with NOPI. On the contrary, regressions including the

alternative price indicator (PCE de�ator) are not in line with the baseline results. The negative

sign of the coe�cients (-0.0018) associated to oil price (for ∆oil and NOPI) contradicts the

6A symmetric indicator for biggest decreases has no signi�cant impact on prices.

11

theoretical intuitions. Moreover, the asymmetry demonstrated is not the expected one between

crude oil price and overall in�ation. Surprisingly, a positive shock on oil price would not generate

in�ation while a decrease would be in�ationary. We can also note that the speci�cation of the

Phillips curve matters because we obtain the "good" sign for the linear oil price variable when

we add the component of in�ation expectations.

Table 3: Augmented Phillips curve - 1970:I - 2007:IIIa

∆oil ∆oil+/∆oil− nopi ∆oil ∆oil+/∆oil− nopic 0.033 0.1804 -0.0127 -0.0903 0.0251 -0.1691

(0.1421) (0.1643) (0.1439) (0.2112) (0.2573) (0.2186)πt−1 0.3053 0.2971 0.3153 0.3101 0.3035 0.3176

(0.0882)*** (0.0901)*** (0.0884)*** (0.082)*** (0.0839)*** (0.0816)***πt−2 0.2631 0.2597 0.2664 0.2464 0.2439 0.2471

(0.0792)*** (0.0801)*** (0.0783)*** (0.0741)*** (0.0753)*** (0.0737)***πt−3 0.0274 0.0356 0.0186 0.027 0.0325 0.0198

(0.0714) (0.0723) (0.071) (0.0668) (0.0681) (0.0664)πt−4 0.3653 0.3664 0.3608 0.3857 0.3868 0.3853

(0.0861)*** (0.0855)*** (0.0878)*** (0.0819)*** (0.083)*** (0.0835)***st−1 -0.559 -0.5582 -0.5717

(0.2246)** (0.226)** (0.2280)**st−2 0.6552 0.6358 0.6918

(0.262)** (0.2681)** (0.2661)**yt−1 0.901 0.828 0.9582 0.7579 0.7396 0.7932

(0.2731)*** (0.2799)*** (0.2771)*** (0.2321)*** (0.241)*** (0.2368)***yt−2 -0.2848 -0.2515 -0.3263 -0.3348 -0.309 -0.3641

(0.3004) (0.3125) (0.3080) (0.2953) (0.3085) (0.2996)yt−3 -0.34 -0.3212 -0.3397 -0.1054 -0.1104 '-0.0944

(0.2377) (0.2423) (0.2455) (0.2614) (0.2651) (0.2653)oilt−1 0.0014 0.0011 0.0019 0.0019

(0.0007)*** (0.0009) (0.0008)** (0.0010)*oilt−2 0.0014 0.0012 0.0014 0.0013

(0.0005)*** (0.0005)** (0.0005)*** (0.0006)**oilt−3 0.0022 0.0019 0.0017 0.0014

(0.0007)*** (0.0006)** (0.0006)*** (0.0005)***

oil+t−1 0.0013 0.0019

(0.0007)* (0.0009)**

oil+t−2 0.0012 0.0013

(0.0005)** (0.0006)**

oil+t−3 0.0018 0.0013

(0.0006)*** (0.0005)**

oil−t−1 0.0025 0.0021

(0.0024) (0.0027)

oil−t−2 0.0026 0.0020

(0.0023) (0.0023)

oil−t−3 0.0055 0.0045

(0.0026)* (0.0026)*

R2 0.895 0.894 0.892 0.901 0.912 0.900AIC 3.100 3.128 3.125 3.048 3.10 3.065DWb 1.691 1.700 1.701 1.75 1.765 1,7650

LB Q statc 0.329 0.312 0.312 0.450 0.382 0.398ARCH LM-Testc 0.933 0.959 0.863 0.986 0.943 0.996

a We report the results reached using Least Squares with heteroskedasticity-robust standard errors. Standarderrors are in brackets. ***, ** and * denote signi�cance at the 1%, 5%, and 10% level respectively.

b Durbin-Watson Statistic.c p-values of serial correlation tests, respectively the Q-statistic of Ljung-Box and the ARCH Lagrange Multi-plier test.

12

We then test for the stability of the relationship which could be in�uenced by several

structural factors as weaker use of oil in the economy.

3.3 An unstable relationship

First of all, we compute a structural break test developed by Bai & Perron (1998) to identify

potential breakpoints in the relationship. The Bai-Perron test determines endogenously the

number of breakpoints and their corresponding dates when they are initially unknown. It

is implemented by an algorithm that searches all possible sets of breaks and calculates a �t

measure for each one. According to this measure, there exist an optimal number of structural

breaks in a given time series. The number of breaks selected minimizes the Bayesian Information

Criterion (BIC). We successively apply the test for one, two and three break points. In the

last case, we recover three dates corresponding to three important shifts in oil prices: 1974

with the �rst oil shock, 1983 during the counter-oil shock and 1990 including the oil shock

associated to the Gulf war. According to the statistical criterion, selecting one break seems

more appropriate. This date is the same for all speci�cations considered and arises in the third

quarter of 1981, after the second oil shock and during the beginning of the long-lasting decline

in oil prices. It is in line with Hooker (2002) who �nds, for the U.S, a structural break in the

1980-81 range. During this period the economies tried to modify their behaviour relative to the

non renewable resources and diminish their dependence. We have also checked the robustness

of the date with the Andrews & Ploberger (1994) structural stability test which provides a

single break point. The results are in line with the Bai-Perron estimates for one break since

we also obtain 1981:3 for each speci�cation of oil prices and in both regressions. We obtain

therefore two sub-samples whose estimates and estimate the augmented Phillips curve in both

sub-samples. Results are reported in tables (7) and (8). Firstly, we observe that the oil price

has a di�erent impact depending on the period considered. We note that in the �rst period, oil

prices are systematically signi�cant and the asymmetry is clearly demonstrated since (∆oil+)

are signi�cant while (∆oil−) are not. Coe�cients associated to (∆oil+) display bigger e�ects

than those on the whole sample and the e�ects of oil prices on in�ation are also immediate.

The cumulative coe�cients reached around 0.0045 against 0.0078 and 0.0060 in the �rst period.

Observations related second sub-sample di�er widely. Indeed, the two �rst lags of oil prices

do not enter signi�cantly. A delay occurs before oil prices in�uence in�ation. Even if the

asymmetry is still present, in�ation responds to a less extent to (∆oil+): around 0.0040.

13

Table 4: Bai-Perron Test without interest rates spreada

1 break BIC 2 breaks BIC 3 breaks BIC

∆oil 1981:3 0.47 1974:4, 1982:1 0.62 1974:4, 1983:4, 1990:2 0.76

∆oil+/∆oil− 1981:3 0.65 1974:3, 1982:1 0.82 1974:2, 1983:4, 1990:2 0.96

nopi 1981:3 0.57 1973:4, 1981:3 0.64 1974:1, 1983:4, 1990:2 0.8

a We report the BIC criterion.

Table 5: Bai-Perron Test including interest rates spreada


∆oil 1981:3 0.53 1975:2, 1982:1 0.67 1975:2, 1982:2, 1992:4 0.87

∆oil+/∆oil− 1981:3 0.77 1974:2 1981:4 0.93 1974:2, 1983:4, 1990:2 1.17

nopi 1981:3 0.52 1975:4, 1982:1 0.73 1978:3, 1983:4, 1990:2 0.89

a We report the BIC criterion.

Table 6: Andrews-Ploberger Test

spread p-Value no spread p-Value∆oil 1981:3 0.0532 1981:3 0.0380

∆oil+/∆oil− 1981:3 0.0406 1981:3 0.0393nopi 1981:3 0.0443 1981:3 0.0334

We also test the robustness of structural breaks with the alternative indicators presented

above. The dates are close to the dates of the case with the output gap and the GDP de�ator.

With the alternative activity indicator, the break occurs sooner in the �rst quarter of 1980. Once

again results with the unemployment gap indicator are in line with baseline regressions and the

e�ects of oil prices decline markedly. We however observe that the magnitude coe�cients are

higher than in the baseline case with the output gap. The cumulative e�ects reached around

0.01 in linear and NOPI cases for the �rst period while it is weaker (0.002) when the interest

rate spread is introduced. Asymmetry seems more obvious in the second speci�cation. The

alternative variable for the level of prices remains imperfect to verify the robustness because

14

tit still provides very di�erent conclusions from the benchmark case. In the �rst sample, oil

prices seem to have no incidence on in�ation while they play a more signi�cant role during the

second period. We recover a counter-intuitive relationship between the two variables when oil

prices' coe�cients exhibit negative signs (∆oil+ and NOPI ). No asymmetry is demonstrated.

Table 7: Augmented Phillips curve - 1970:I - 1981:IIa

∆oil ∆oil+/∆oil− nopi ∆oil ∆oil+/∆oil− nopic 0.6174 0.7881 0.6495 -1.0466 -0.7317 -0.9071

(1.3089) (1.3195) (0.298) (1.7814) (1.7936) (1.7490)πt−1 0.2340 0.2299 0.2323 0.2509 0.2467 0.2477

(0.0920)** (0.0955)** (0.0919)** (0.0753)*** (0.0810)*** (0.0766)***πt−2 0.1765 0.1798 0.1746 0.1723 0.1754 0.1685

(0.1134) (0.1049)* (0.1112) (0.0993)** (0.0925)* (0.0985)*πt−3 -0.0633 -0.0803 -0.0712 -0.0131 -0.0398 -0.0244

(0.0866) (0.0927) (0.0858) (0.0822) (0.0856) (0.0828)πt−4 0.5654 0.5384 0.5661 0.6142 0.5856 0.6138

(0.0879)*** (0.0844)*** (0.0876)*** (0.0813)*** (0.0831)*** (0.0803)***st−1 -0.4596 -0.4001 -0.4799

(0.4563) (0.5570) (0.4612)**st−2 1.0487 0.9260 1.0323

(0.4513)** (0.4965)* (0.4471)**yt−1 1.6690 1.7763 1.7033 1.2750 1.4425 1.3077

(0.3575)*** (0.4039)*** (0.3460)*** (0.3987)*** (0.3763)*** (0.3911)***yt−2 -0.5233 -0.5443 -0.5314 -0.2703 -0.3211 -0.2948

(0.4647) (0.4545) (0.4572) (0.5367) (0.5345) (0.5301)yt−3 -0.6596 -0.7430 -0.6946 -0.0677 -0.2445 -0.1222

(0.3897) (0.4416) (0.3913)* (0.5158) (0.5682) (0.5139)oilt−1 0.0019 0.0019 0.0033 0.0033

(0.0006)*** (0.0005)*** (0.00078)*** (0.0008)***oilt−2 0.0014 0.0015 0.0017 0.0016

(0.0006)*** (0.0005)*** (0.0007)** (0.0006)*oilt−3 0.0021 0.0022 0

.0015 0.0015

(0.0007)*** (0.0006)*** (0.0008)* (0.0007)**

oil+t−1 0.0017 0.0031

(0.0005)*** (0.0011)***

oil+t−2 0.0032 0.0029

(0.0032)** (0.0013)**

oil+t−3 0.0029 0.0020

(0.0010)*** (0.0012)

oil−t−1 0.0194 0.0136

(0.0155) (0.0184)

oil−t−2 -0.0063 -0.0068

(0.0126) (0.0126)

oil−t−3 -0.0169 -0.0149

(0.0101) (0.0099)

R2 0.706 0.712 0.709 0.816 0.732 0.738AIC 3.466 3.486 3.915 3.384 3.435 3.378DW 1.573 1.601 1.621 1.781 1.855 1.832

LB Q stat 0.601 0.470 0.649 0.876 0.660 0.873BG LM test 0.526 0.350 0.586 0.816 0.613 0.822

ARCH LM-Test 0.928 0.999 0.933 0.323 0.619 0.340

a See notes in Table (3).

The stability is then evaluated with rolling estimations. The next �gures report the evolution

of the cumulative coe�cients associated with oil prices. We realize these estimations for the

four oil prices indicator. A window of sixty observations is set constant and the step is equal to

15

one. The �rst regression starts in 1970:I until 1985:4 and ends in 2007:III and starts in 1992:III.

All coe�cients display the similar feature and a declining in�uence in the second part of the

seventies. Oil prices totally lost their role on in�ation dynamics from 1981 and 1982 what is

consistent with the break dates found above with previous break tests.

Table 8: Augmented Phillips curve - 1981:III - 2007:IIIa

∆oil ∆oil+/∆oil− nopi ∆oil ∆oil+/∆oil− nopic 0.1529 0.2400 0.1577 0.2777 0.3774 0.2487

(0.1664) (0.2038) (0.3572) (0.2184) (0.2702) (0.2259)πt−1 0.3610 0.3497 0.3407 0.3622 0.3506 0.3435

(0.1007)*** (0.0987)*** (0.1026)** (0.1000)*** (0.0993)*** (0.1024)***πt−2 0.2920 0.3131 0.3230 0.2894 0.3098 0.3196

(0.1012)*** (0.1032)*** (0.1033)*** (0.1018)*** (0.1037)*** (0.1049)***πt−3 (0.1012)*** 0.1998 0.1802 0.1791 0.1942 0.1751

(0.1181) (0.0977)** (0.1188) (0.1174) (0.0984)* (0.1183)πt−4 0.0787 0.0552 0.0653 0.0711 0.0479 0.0599

(0.1237) (0.0917) (0.1262) (0.1252) (0.0927) (0.1278)st−1 -0.1948 -0.2009 -0.1943

(0.2184) (0.2272) (0.3757)st−2 0.1021 0.1048 0.1240

(0.2268) (0.2246) (0.2668)yt−1 0.3693 0.3578 0.4402 0.3332 0.3198 0.4094

(0.1774)** (0.21629) (0.1842)** (0.1942)* (0.22191) (0.1996)**yt−2 -0.2505 -0.2484 -0.3587 -0.2761 -0.2718 -0.3844

(0.2755) (0.3112) (0.2792) (0.29603) (0.3163) (0.2969)yt−3 0.0069 0.0234 0.0522 -0.0108 0.0039 0.0445

(0.2245) (0.216) (0.2244) (0.2486) (0.2245) (0.2487)oilt−1 -0.0003 -0.0025 -0.0001 -0.0023

(0.0010) (0.0013)** (0.0011) (0.0013)*oilt−2 0.0010 0.0009 0.0010 0.0010

(0.0007) (0.0007) (0.0007) (0.0007)oilt−3 0.0032 0.0038 0.0031 0.0037

(0.0008)*** (0.0009)*** (0.0009)*** (0.0009)***

oil+t−1 -0.0022 -0.0021

(0.0017) (0.0017)

oil+t−2 0.0008 0.0009

(0.0018) (0.0018)

oil+t−3 0.0042 0.0041

(0.0042)** (0.0017)**

oil−t−1 0.0034 0.0037

(0.00285) (0.0029)

oil−t−2 0.0011 0.0012

(0.0029) (0.0029)

oil−t−3 0.0019 0.0020

(0.0029) (0.0029)

R2 0.854 0.854 0.855 0.853 0.852 0.854AIC 3.676 2.704 2.667 2.703 2.730 2.696DW 2.120 2.085 2.061 2.109 2.072 2.050


ARCH LM-Test 0.029 0.199 0.016 0.021 0.185 0.010

a See notes in Table (3).

Our results suggest that the in�uence of oil prices on overall in�ation has declined over

time and that the break date is relevant. The link between crude oil prices and in�ation is

clear in the �rst period while in the second in�ation reacts less to oil price variations. The

16

interpretations we could make is that the two successive oil shocks of 70's were strong while

no major oil shock with the same intensity occurs during the second period. Nevertheless, the

recent upsurge in oil price between 2003 and 2007 has a similar magnitude than the second

oil shock in 1979. We then explore explanations that would account for the evolution of the

relationship.

Figure 4: Rolling estimates including a spread

∆oil NOPI

∆oil+ ∆oil−

17

Figure 5: Rolling estimates without spread

∆oil NOPI

∆oil+ ∆oil−

4 Potential explanations of the instability

Table (9) reports descriptive statistics on macroeconomic variables for 4 oil shock episodes. We

select the same episodes than Blanchard & Gali (2007). Samples di�er a little because we take

the Brent spot prices instead of West Texas Intermediate (WTI) spot price. In each episode,

cumulative changes of oil prices exceed 100%. The �rst one (1973:2-1974:1) is associated with

the �rst oil shock following the cutdown in oil supply after the Kippour war. The second

oil shock (1978:4-1980:4) occurs during the Iranian Revolution. We then consider a third oil

shock at the end of the last decade (1999:1-2000:3) which arises not only after the Asian crisis

but also after an increase of OPEC's quota from 10%. The last period we look at is a longer

period which begins in 2002 and lasts until the end of sample (2007:3). This shock re�ects the

disequilibrium on the oil market due to the inadequacy between a steady increase in demand

(mainly driven by emerging markets) and an insu�cient supply. In each period we calculate

the average of quarterly growth rates of in�ation (GDP de�ator), of the real GDP following

the shock, the compensations in the private sector and the unit labor costs. We also show the

average of the unemployment and output gap on the period.

18

Table 9: Descriptive statistics - 1970:I - 2007:III

Average of the quarterly growth rate in% 1973:2-1974:1 1978:4-1980:4 1999:1-2000:3 2002:1-2007:3Real oil prices in dollars 50.37 9.65 17.26 5

Total change 236.5 104.4 163.7 191.1

Real oil prices in euros 52.06 9.9 21.87 2.85Total change 251.57 112.81 227.58 85.63

GDP de�ator 2.73 2.4 0.34 0.51

Compensation per employee (Private sector) 3.56 2.76 0.6 0.48

Unit labor cost 2.98 2.46 0.37 0.33

Real GDP (one year after the shock) -0.42 0.31 0.5 0.48a

LevelsOutput gap 2.95 2.24 1.03 -0.83

Unemployment 2.04 4.67 8.46 8.05

a The calculation is based on GDP forecasts of OECD.

Macroeconomic e�ects of oil price shocks di�er thus depending to the period when they

occur. The two �rst episodes display a higher level of in�ation and wages' evolution but a

lower growth of GDP than in the two others. Those �gures corroborate our results that show

a weaker link between oil prices and in�ation but also between oil prices and macroeconomy

widely described in the litterature.

What could explain the decreasing trend in the oil prices-in�ation link? Many papers

attempt to understand and evaluate the lower macroeconomic impacts of crude oil prices after

1980. Blanchard & Gali (2007) list four reasons: the lack of adverse shocks, the smaller share

of oil in production, more �exible labor markets and improvements in monetary policy. Hooker

(2002) also evoks the declining oil intensity and monetary policy and adds the deregulation

of energy and energy-related industries. De Gregorio et al. (2007a) point to the role of the

exchange rate in the pass-through of oil prices in the in�ation, and especially the appreciation

of domestic currency for non-U.S economies which smoothes the rising price of the barrel

libelled in dollars. Authors like Nordhaus (2007) analyse the evolution between past oil shocks

episodes and the current period as a di�erence in the nature of oil shocks. While shocks in the

seventies are seen as a disruption in oil supply, the recent upsurge in oil prices is rather due to

an economic expansion and especially a strong demand from emerging countries.

In this section, we will focus on two types of explanations. First, we explore reasons relative

19

to structural shifts linked to oil. It deals with the evolution of oil usage in the economy and the

economic implications of a supply shock versus a demand-driven shock. Second, we take into

account the monetary side and the role of a new monetary policy regime which began after the

second oil shock in 1980. Monetary explanation appears as a determinant justi�cation of the

declining pass-through between oil prices and in�ation rates. Many views agree to assign an

important role to monetary policy in the e�ects of oil shocks on output and in�ation. Bernanke

et al. (1997) and Darrat et al. (1996) �nd that, by controlling for Fed funds rates in a VAR

framework, the Federal reserve was responsible of the output contraction. Barsky & Kilian

(2001) conclude that a monetary expansion, with low real interest rates, was at the source of

the two big oil shocks of 70's as in 1999. If monetary conditions have a central place in the

transmission channel, it could thus promote the diminished pass-through. And obviously it

does. Recent monetary policymakers become more watchful to in�ation deviations according

to the Taylor's rule literature. Central Banks are from now more reactive to the triggering of

a possible wage-price spiral. They also anchor in�ation expectations into low levels (around

2%) by building a strong credibility. In the EMU, the Bundesbank model prevailed in the

construction of the ECB, what plays a large role in the declining trend of in�ation in the area.

Nevertheless, Hooker (2002) �nds that monetary policy has less responded to oil price shocks

than in the past. The results suggest that the causality could run in the opposite direction.

Monetary authorities would not need to react agressively to rising oil prices because oil prices

in�uence in�ation in a lesser extent7. One traduction of the new monetary regime could be the

disappearence of the wage-price spiral.

4.1 A steady decline in oil intensity

The �rst structural explanation deals with the decline in oil intensity of the European economies

belonging to the euro zone. The oil intensity constitutes the quantity of the oil consumption

per unit of production. The decreasing energy intensity is a key explanation of the modi�ed

oil prices and in�ation relationship. The two graphs below indicate a declining trend.

7Taylor (2000) argues that the new monetary environment explains itself the declining pass-through

20

Figure 6: Oil intensity in Euro area in time

On the left, we plot a weighted average8 of oil intensity for several area members9. On the

right, we plot the oil intensity in 1981 versus oil intensity in 2005 for each economy. The scale

is BTU10. The two graphs display a lower oil utilization in euro area which can explain that

members could face more easily to an oil price increase. The weighted average in euro area

indicates that, between 1980 and 2005, the intensity decreased by 20% from 8987 barrels to

7186. This evolution hides however heterogeneity between members since oil dependency in

Finland fell from 30% while in Portugal, it rose from 36%. In the second graph, we see that more

countries are below above the diagonal. This area implies that countries made improvements

in the energy usage.

Like Hooker (2002), we test empirically this hypothesis by introducing an interaction term

(int*oil), with intensity and oil prices growth rate in two regressions with output gap and

unemployment gap. We interpolate to a quarterly frequency the variable of intensity only

available annually. This term enters signi�cantly on its second lag and validates our assumption.

The sign indicates that for a co-movement of both variables in the same direction, the term

a�ects logically in�ation in a positive way.

8See appendix A for further details.9Finland, France, Greece, Italy, Netherlands, Portugal and Spain.10British Thermal Uunit: quantity of heat required to raise the temperature of 1 pound of liquid water by 1

degree Fahrenheit at the temperature at which water has its greatest density.

21

Table 10: Augmented Phillips curve - Estimates withan interaction term (1990:2-2007:3)a

y = output gap y = unemployment gap π = PCEc 0.2796 0.4071 0.5008

(0.2144) (0.2020)** (0.1952)πt−1 0.2506 0.2335 0.2849

(0.0938)*** (0.0978)** (0.0828)***πt−2 0.1892 0.1994 0.2859

(0.0929)** (0.0949)** (0.1215)**πt−3 0.2097 0.1903 0.2676

(0.1360) (0.1334) (0.0915)***πt−4 0.2120 0.2258

(0.0974)** (0.1029)**st−1 0.0039 -0.0933 -0.1789

(0.0952) (0.0950) (0.2618)st−2 -0.3272

(0.3918)st−3 1.0632

(0.3733)***st−4 -0.7090

(0.2069)***yt−1 0.1795 -1.5034 0.2157

(0.1184) (0.7443)** (0.1462)yt−2 1.4661 -0.2166

(0.7433)*** (0.1813)Int ∗ ∆oilt−1 -1.65E-07 -1.78E-07 5.48E-07

(1.27E-07) (1.40E-07) (1.43E-07)***Int ∗ ∆oilt−2 1.49E-07 1.33E-07 -1.24E-07

(9.77E-08) (1.15E-07) (1.72E-07)Int ∗ ∆oilt−3 3.71E-07 3.53E-07 1.02E-07

(1.21E-07)*** (1.35E-07)** (1.84E-07)Int ∗ ∆oilt−4 2.29E-07 2.53E-07 2.41E-07

(1.81E-07) (1.91E-07) (1.50E-07)

R2 0.839 0.840 0.842AIC 2.628 2.627 2.693DWb 2.072 2.098 2.120

LB Q statc 0.971 0.930 0.892BG LM testc 0.664 0.509 0.517

ARCH LM-Testb 0.017 0.005 0.055

aWe report the results reached using least square method with heteroskedasticrobust standard errors. Standard errors are in brackets; ***, ** and * denotesigni�cance at the 1%, 5%, and 10% respectively.b See notes in Table (3).

Obviously, the oil intensity decline mitigates the consequences of an oil price shock on

in�ation. The less the capital goods are oil intensive, the lower production costs and prices

of �nal products will be a�ected. The less consumers buy gasoline, the lower their purchasing

power diminishes. Moreover, a service-oriented economy (mostly the case in Europe) is less

oil dependent and a shock only impacts a minor component of the production. We observe

in De Gregorio et al. (2007b) that in�ation in developing countries is more sensitive to an oil

price shock due to an old-fashioned productive structure. At last, second round e�ects will

progressively disappear in the economies where the oil pass-through falls.

22

4.2 A new kind of oil shock: a demand driven shock

The second hypothesis refers to a new de�nition of an oil shock for the recent large and long-

lasting oil prices upsurge. Many papers have investigated this issue by distinguishing the two big

oil shocks of the seventies11 from the latest oil shock. The distinction is made in the following

manner: Unlike to the oil shocks of the seventies, the recent upsurge in oil price would be

a "demand-driven shock", while the previous oil shocks were supply shocks generated by an

exogenous cut in oil production, especially by OPEC countries. Kilian (2006) goes beyond and

proposes three di�erent shocks: an oil supply disruption, shocks to the global demand for all

industrial commodities and a speci�c crude oil demand shock. He concludes that the regressions

of macroeconomic aggregates on oil prices are unstable and depend on the underlying cause of

the oil price increase.

Figure 7: Evolution of world oil supply and world oil demand

The last historical oil prices shocks12 (1974, 1979-80) were caused by abrupt oil supply

disruptions13 on the oil market. The left graph shows these collapses between vertical lines for

world oil and OPEC supplies. On the right, we note that the world oil demand follows closely

the direction of the disruption. These shocks were purely exogenous and assimilated as supply

shock, whereas the actual one can be view as a demand shock14.

Indeed, since 2001 no major oil disruption supply has occurred and the world oil demand

11We could also add the oil shock associated to the Gulf war.12These shocks are often linked to political events: Kippour war (1974), Iranian revolution (1979) and Iran-

Iraq war (1980), see Hamilton (2003)13The average gross supply shortfalls respectively reached 2.6, 3.5 and 3.3 millions barrels per day14The exogeneity was however widely discussed by Barsky & Kilian (2001) who explain that those oil price

shocks were generated by precise macroeconomic conditions and especially by a large monetary expansion andlow interest rates.

23

(widely in�uenced by the demand of emerging countries) has steadily risen. The high oil prices

level actually re�ects a disequilibrium between oil supply and oil demand. The growth rhythm

of supply is insu�cient regarding the evolution of the demand. OPEC production quotas or

speculative schemes impact only marginally the evolution of prices but rise their volatility.

According to Nordhaus (2007), the oil shock of 2002-2006 is long-lasting, more gradual and

appears less as a surprise than in the past. Indeed, the �rst oil shock occured while the biggest

oil increase in the twenty previous years reached 10%. The actual and smooth oil prices rise is

thus not a surprise and allows agents to adapt their purchases and substitute oil or oil-intensive

capital with other energy sources and more oil-e�cient capital goods15. The rhythm of the oil

prices increase does not modify roughly the in�ation forecasts. Firms, workers and households

perceive the current oil price movements as volatile and temporary variations. They perfectly

know that the oil shock is not the beginning of a new regime of high in�ation16, so that in�ation

expectations integer these shifts and remain fairly stable. Therefore claims for higher wages,

for instance, would not be strong. On the other hand, an abrupt fall in oil supply generating

higher oil prices signi�cantly bites into economic agents welfare. Moreover, the perception of

in�ation is widely troubled and conducts to query higher wages. Finally, behaviors adjusted

to the historical events and oil prices evolution. The current smoothed shock associated to a

weaker short-run elasticity of demand to oil price entails to a less extent the pro�ts of �rms

and utility of consumers. Inversely, during episodes of the 70's, the strong short-run elasticity

combined with rising oil price provoke large losses in the welfare of each agents who were not

contemporaneously able to change their consumption habits or investments.

If we can only put forward some intuitions, we do not provide empirical evidences and our

arguments are disputable. We can however have a look to the literature. Hamilton (2003) �nds

that military con�icts, embargo and political events caused the oil supply disruption in Arab

OPEC oil producers in 1973, 1978, 1980 and 1990 17. Without eliminating Barsky & Kilian

(2001) work and the role played by macroeconomic conditions, he notes that countries which

suddenly cut their production were speci�cally hit by those events. He demonstrates with linear

IV regressions that oil prices were helpful to predict real GDP when shortfalls in the production

15Moreover, the long-term expectations of oil prices are based on an upward trend since oil is recognized asa nonrenewable energy.

16Notably because monetary policy has achieved a great credibility.17He also mentions the Suez crisis in 1956

24

were used as instruments for crude oil prices. This linear speci�cation approaches the forecasting

performance of nonlinear models only when we take into account those supply disruptions. It

suggests that oil exogenous shocks restore a signi�cant negative relationship between oil prices

and activity, especially before the mid-eighties. We can thus conclude that the disappearing link

between crude oil prices and macroeconomic variables could be due to the lack of this kind of

shock. Kilian (2008a) discusses Hamilton's conclusions with estimations on countries belonging

to the G-7. He suggests that even if oil supply disruptions cause a temporary contraction

in output in the second year, they need not generate sustained consumer price in�ation nor

stag�ationary situation. He shows in a counterfactual exercise that those exogenous shocks

would not have modi�ed the path of in�ation in these countries. This paper can not prove

that 1973-74 and 2002-2003 periods of a slight reduction in oil supply had signi�cants e�ects

on real GDP growth. Recently, Kilian actualized his work incuding a new measure of shortfall

of the OPEC production. Kilian (2008b) builds this indicator for the U.S. case. The strategy

is to generate the counterfactual level of production for each OPEC's country assuming that

the exogenous event would not have occured. He also concludes that this kind of shocks has a

minor e�ect on output on USA.

The e�ects of the recent shift in the nature of oil shock on macroeconomic aggregates are

con�icting. This explanation is thus not totally convincing since results are sensitive to the

method used to test it. Nevertheless, Hamilton lets an open question which could provide a

bridge between both views: if these historical shocks are decisive, we can also consider that wars

and con�icts cause recessions because of psychological reasons or uncertainty on the future.

4.3 The end of second round e�ects

Another explanation linked to the monetary side deals with the second round e�ects which are

particularly feared in the euro area. Governor Trichet often mentions this point in his speeches

when crude oil prices are upward. An extract from the Monthly Bulletin(November 2004) of

the ECB con�rms its motivation: "The oil price increase has already had a signi�cant direct

impact on euro area in�ation. Against this background, monetary policy has to ensure that

this direct e�ect does not fuel in�ationary expectations and has to remain vigilant against the

emergence of second-round e�ects" (p.63). Blanchard & Gali (2007) largely investigate this

issue in a New-Keynesian model in which oil prices are included as an input in the production

25

function and as a consumption good. They also allow for wage rigidity and evaluate their

impact in the pass-through of oil prices on macroeconomic variables. They conclude that the

greater �exibility in labor market and wage adjustments plays a signi�cant role in the declining

pass-through.

The statistics in Table (9) show that the compensations of the private sector have increased

more slowly during the two last shocks relative to the two �rst episodes. We evaluate this

explanation with a Vector Autoregressive (VAR) methodology for both samples which can be

written under the following structural representation:

B0Yt = α +p∑i=1

BiXt + εt (3)

withB0 the contemporaneous coe�cients matrix, α the vector of constants, p the order of the

VAR selected with statistic criteria (AIC or SBC) and εt the vector of structural disturbances.

By multiplying both sides of the equation with B−10 , we obtain the reduced form of the VAR:

Yt = c+ AiXt + et (4)

with Ai = B−10 Bi and et = B−1

0 εt which constitute innovations in the model.

It is an unrestricted VAR where the (n x 1) vector Yt includes the set of endogenous variables.

Xt is a (np x 1) vector grouping all lagged terms of Yt while A is the rectangular matrix

containing coe�cients. We identify the VAR model by means of a Cholesky decomposition.

The order of variables follows: real oil prices (OPR), private compensations (COMP) andGDP

de�ator (DEFG). This order implies that real oil prices can not be contemporaneously impacted

by compensations and in�ation while the GDP de�ator is immediately in�uenced by oil prices

and compensations. We could consider oil prices as an exogenous variables but many papers

introduce them in an endogenous way like Jimenez-Rodriguez & Sanchez (2005), Cologni &

Manera (2008) and Cunado & Perez de Gracia (2003). This question was widely discussed in

Barsky & Kilian (2001) in the case of the U.S and they conclude that oil prices are in�uenced

by the evolution of demand. The size of the euro area close to the U.S economy conducts us

to integrate real oil prices in an endogeneous way. The suitable lag length is set to 4 according

26

to the likelihood ratio test.

Before studying impacts of oil shocks on labour incomes and in�ation, we perform unit root

tests for the three variables. We successively use Dickey-fuller test and Phillips-Perron tests.

Results are reported in appendix E. Each test indicates that variables in levels are I(1) and

that their growth rates (DOPR, DCOMP, DEFG) are stationnary.

Figure 8: Impulse response functions in the �rst sub-sample: 1970:I - 1981:III

We then look at cumulative impulse response functions and especially the impact of oil price

shocks on private compensations and then the e�ects of compensations on in�ation. Figures

(8) and (9) display the accumulated response functions and their con�dence bands calculated

with Monte Carlo procedure. We perform a one standard deviation shock on residuals. We

observe a slight di�erence between the two samples regarding the accumulated responses of

private compensations to oil prices in domestic currency. If they steadily increase between 1970

and the beginnig of the eighties, the impacts during the second sub-sample are close to zero

and non-signi�cant. The reaction of in�ation to a shock on incomes remains however positive.

The graphic interpretation con�rms the weakening of the second-round e�ects.

27

Figure 9: Impulse response functions in the second sub-sample: 1981:IV - 2007:III

Those results could also validate the distinction made by households on the nature of the

recent surge in oil prices. They understand that the evolution of oil prices re�ects shifts in

fundamentals on the oil market rather than short-term considerations like speculation. They

forecast the long term features of the price of a non-renewable energy which will evolve on

an upward trend. In turn, economic agents modify their consumption habits, purchases and

investments towards energy-e�cient goods.

5 Conclusion

An asymmetric reaction of policymakers to oil prices can be considered as optimal if the link

between those crude oil prices and in�ation is also asymmetric. This paper investigates the

relationship between oil prices and overall in�ation in Euro area since 1970. Two issues were ex-

plored: the stability of the relationship and its asymmetric pattern. We estimate an augmented

Phillips curve with oil prices under an asymmetric speci�cation in order to assess nonlinear ef-

fects. The stability of this relationship is also examined since major shifts occured in the use

of oil in European economies.

28

The estimation results show that crude oil prices a�ect signi�cantly overall in�ation. We

�nd that the transmission between both variables is nonlinear, and more particularly when the

greater oil price movements are retained. The asymmetry appears de�nitely when we estimate

sub-samples. The structural break tests display a break around the end of 1981, which is

consistent with previous studies (see Hooker (2002)). This date coincides with the end of the

second oil shock and shifts in the behaviour of consumers and producers that entail the counter-

oil shock. The magnitude of oil e�ects on the level of price also declines over time. Our results

are more robust with alternative variables of activity than with others price variables. We

provide some potential explanations for this unstability. The declining oil intensity constitutes

a major reason for the lower in�uence of oil price in in�ation dynamics. The intuition that

pure endogenous oil shock would generate lower in�ation pressures than oil supply disruptions

remains disputable. Finally, it appears that monetary policy has played a large role in the

evolution of in�ation in the last years. A VAR model estimation demonstrates that the spiral

between oil prices and income seems weakened.

This work has to be improved in two ways. First, monetary reasons that explain the dis-

appearing link between oil prices and overall in�ation could be deepened. The implications for

monetary policy of the asymmetry could be assessed in a more global perspective, for instance

in a structural model. Further research could alsodeal with the reasons of the nonlinearity and

especially the transmission of crude oil prices to re�ned product prices.

29

A Data

We work on quarterly data between 1970:I and 2007:III. All the data except oil prices and ex-

change rates are extracted from the OECD Economic Outlook database. From 1970:1 to 1991:4,

the database includes Western Germany. From 1992:1, the Euro area data are constituted by

Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands,

Portugal and Spain.

• Our baseline in�ation indicator is the quarterly growth rate of the seasonally-adjusted

GDP de�ator at market prices.

• We compute a spread of interest rate resulting by the di�erence between a short-term in-

terest rate (3 months) and a long-term interest rate corresponding to return of government

bonds (10 years).

• The output gap is calculated as the deviation of real GDP from a trend extracted using

the Hodrick-Prescott �lter on the initial series of real GDP at market prices, with the

standard quarterly value of 1600 for the smoothing parameter.

• Robustness of our estimates is checked with two alternative indicators: the monthly

unemployment rate which is detrended with the Hodrick-Prescott procedure. We also use

the de�ator of the Private Final Consumption expenditure in order to test another price

variable. This variable includes imputed expenditure, incurred by residents on individual

consumption goods and services.

• Oil prices are based on the Brent spot price, which is the most appropriate for Euro-

pean countries. Data are extracted from the Energy Information Administration (EIA)

database, and crude oil prices in U.S. dollars are expressed in domestic currency using

the bilateral euro-dollar (or ECU-dollar) exchange rate.

� Our baseline indicator (∆oil) is therefore the quarterly percentage change in crude

oil prices in domestic currency.

� We also separate the positive (∆oil+) from the negative (∆oil−) variations of oil

prices to test for asymmetric e�ects on in�ation, as suggested by Mork (1989).

� We �nally calculate the Net Oil Price Increases (NOPI) indicator from Hamilton

(1996). When the oil price level of the current quarter exceeds the value of the

30

previous year's maximum level, the NOPI is equal to the percentage change between

the two "peaks". For all other values of oil price variations (negative as positive),

the NOPI indicator is equal to zero.

• For all used variables we calculate the quarterly annualised growth rate.

31

B Robustness checks of estimates with alternative prices

and activity indicators

Table 11: Augmented Phillips curve - 1970:I - 2007:III with an unem-ployment gap indicatora

∆oil ∆oil+/∆oil− nopi ∆oil ∆oil+/∆oil− nopic 0.0531 0.2932 0.0010 -0.0676 0.0892 -0.1668

(0.1901) (0.1709)* (0.1950) (0.2156) (0.233) (0.2222)πt−1 0.3483 0.3394 0.3606 0.3047 0.3012 0.3120

(0.0723)*** (0.1267)*** (0.0707)*** (0.0671)*** (0.1068)*** (0.0676)***πt−2 0.2923 0.2880 0.2998 0.2766 0.2708 0.2796

(0.0723)*** (0.1132)*** (0.0783)*** (0.0674)*** (0.085)*** (0.0681)***πt−3 0.0027 0.0150 -0.0079 0.0243 0.0305 0.0162

(0.0723) (0.0853) (0.0734) (0.0676) (0.0774) (0.0682)πt−4 0.3115 0.3081 0.3035 0.3600 0.3592 0.3586

(0.0683)*** (0.0920)*** (0.0694)*** (0.0642)*** (0.0772)*** (0.0649)***st−1 -0.7193 -0.7211 -0.7448

(0.2119)*** (0.2775)** (0.2133)***st−2 0.4814 0.4897 0.5044

(0.3269) (0.4346) (0.3296)st−3 0.4814 0.2794 0.3335

(0.3077) (0.2347) (0.3335)ut−1 -2.5189 -2.4901 -2.6731 -1.5712 -1.5647 -1.6087

(0.8585)*** (0.7523)*** (0.8719)*** (0.8176)* (0.6396)** (0.8292)**ut−2 2.3630 2.1707 2.5400 1.4332 1.3553 1.5030

(0.8592) (0.7543)*** (0.3080) (0.8306)* (0.6869)* (0.8408)*oilt−1 0.0018 0.0014 0.0026 0.0026

(0.0009)* (0.0011) (0.0009)*** (0.0010)***oilt−2 0.0014 0.0012 0.0018 0.0018

(0.00095) (0.0011) (0.00095*** (0.0010)*oilt−3 0.0025 0.0020 0.0020 0.0016

(0.0009)*** (0.0011)* (0.0009)** (0.0010)

oil+t−1 0.0014 0.0025

(0.0010) (0.0010)**

oil+t−2 0.0013 0.0018

(0.0005)** (0.0006)***

oil+t−3 0.0019 0.0014

(0.0006)*** (0.0005)***

oil−t−1 0.0049 0.0037

(0.0026)* (0.0027)

oil−t−2 0.0025 0.0017

(0.0026) (0.0023)

oil−t−3 0.0072 0.0056

(0.0024)*** (0.0025)**R2adj 0.8750 0.8760 0.8710 0.8930 0.8920 0.8910AIC 3.2650 3.2820 3.2990 3.1310 3.1600 3.1530DWb 1.4860 1.5240 1.4780 1.6180 1.6450 1.6250

LB Q statc 0.1320 0.2250 0.1090 0.3150 0.4210 0.3020BG LM testc 0.0002 0.0015 0.0001 0.0091 0.0249 0.0074

ARCH LM-Testc 0.2857 0.4246 0.2276 0.9923 0.9839 0.9400


b Durbin-Watson Statistic.c p-values of serial correlation tests, respectively the Q-statistic of Ljung-Box and the ARCH Lagrange Multipliertest.

32

Table 12: Augmented Phillips curve - 1970:I - 2007:III with the Private�nal Consumption Expenditure de�atora

∆oil ∆oil+/∆oil− nopi ∆oil ∆oil+/∆oil− nopic 0.1595 0.2321 0.1699 -0.0656 -0.0130 -0.0787

(0.1411) (0.1545) (0.1439) (0.2181) (0.2334) (0.2119)πt−1 0.6167 0.6122 0.6244 0.5716 0.5768 0.5842

(0.0969)*** (0.0995)*** (0.0926)*** (0.0957)*** (0.1037)*** (0.098)***πt−2 0.2770 0.3021 0.2376 0.2713 0.2948 0.2309

(0.108)** (0.1092)*** (0.1074)** (0.0994)*** (0.1056) (0.1055)**πt−3 0.1895 0.1774 0.2207 0.2037 0.1874 0.2315

(0.087)** (0.0934)* (0.0845)*** (0.0984) (0.0939)** (0.0873)***πt−4 -0.1188 -0.1294 -0.1196 -0.0722 -0.0864 -0.0731

(0.0802) (0.0798) (0.0803) (0.0874) (0.0836) (0.0865)st−1 0.0480 0.0440 0.0693

(0.2218) (0.2365) (0.2445)st−2 -0.2823 -0.2656 -0.3344

(0.3284) (0.3554) (0.3615)st−3 0.7782 0.7431 0.7906

(0.3282)** (0.7431)** (0.3463)**st−4 -0.3620 -0.3499 -0.3387

(0.2011)* (0.2424) (0.2403)yt−1 0.6369 0.5889 0.6508 0.5770 0.5327 0.5846

(0.1882)*** (0.1874)*** (0.1933)*** (0.2004)*** (0.1921)** (0.2014)***yt−2 -0.5193 -0.4605 -0.5305 -0.3227 -0.2824 -0.3441

(0.1840)*** (0.1875)** (0.1842) (0.2018) (0.2069) (0.2035)*oilt−1 0.0013 0.0008 0.0020 0.0016

(0.0012) (0.0011) (0.001)* (0.0012)oilt−2 -0.0018 -0.0013 -0.0015 -0.0009

(0.0007)*** (0.0006)* (0.001) (0.0006)

oil+t−1 0.0002 0.0010

(0.0008) (0.0009)

oil+t−2 -0.0009 -0.0006

(0.0007) (0.0044)

oil−t−1 0.0087 0.0085

(0.0026)*** (0.0025)***

oil−t−2 -0.0064 -0.0064

(0.0025)** (0.0026)**

R2 0.906 0.909 0.903 0.909 0.912 0.906AIC 3.101 3.076 3.132 3.087 3.067 3.122DWb 2.037 2.016 2.023 2.022 1.999 2.010

LB Q statc 0.988 0.981 0.975 0.977 0.983 0.933BG LM testc 0.747 0.511 0.777 0.795 0.786 0.746

ARCH LM-Testc 0.541 0.531 0.697 0.238 0.237 0.360


b Durbin-Watson Statistic.c p-values of serial correlation tests, respectively the Q-statistic of Ljung-Box and the ARCH Lagrange Multi-plier test.

33

C Robustness checks of stability tests with alternative prices

and activity indicators

C.1 Bai-Perron tests

Table 13: Stability tests with alternative indicators


Unemployment gapno spread

∆oil 1981:3 0.74 1974:4, 1982:1 0.86 1974:4, 1983:4, 1990:2 1.07

∆oil+/∆oil− 1975:2 0.76 1975:2, 1981:4 0.79 1975:2, 1981:4, 1990:2 0.99

nopi 1981:1 0.76 1974:1, 1982:4 0.90 1974:4, 1980:4, 1990:2 1.08

Unemployment gapspread

∆oil 1981:4 0.56 1976:4, 1982:1 0.67 1976:2, 1982:1, 1990:2 0.9

∆oil+/∆oil− 1976:2 0.76 1976:2 1982:1 0.88 1976:2, 1982:1, 1990:2 1.1

nopi 1981:3 0.57 1976:4, 1982:1 0.68 1977:2, 1982:4, 1990:2 0.85

PCE de�atorno spread


∆oil 1980:1 0.45 1974:4, 1983:4 0.61 1974:4, 1982:4, 1989:4 0.78

∆oil+/∆oil− 1980:1 0.5 1974:2, 1981:4 0.69 1974:2, 1984:1, 1990:2 0.85

nopi 1980:1 0.48 1983:4, 1989:1 0.65 1974:4, 1983:4, 1989:1 0.78

PCE de�atorspread


∆oil 1975:2 0.53 1975:3, 1981:1 0.69 1975:2, 1982:1, 1990:2 0.93

∆oil+/∆oil− 1980:1 0.59 1975:3 1980:1 0.81 1975:3, 1980:1, 1994:1 1.05

nopi 1980:1 0.58 1975:3, 1981:1 0.79 1975:3, 1980:3, 1985:3 1.04

34

C.2 Andrews-Ploberger Tests

Table 14: Andrews-Ploberger Test with unem-ployment gap

spread p-value no spread p-value∆oil 1981:4 0.013 1981:3 0.124

∆oil+/∆oil− 1981:4 0.008 1982:1 0.02nopi 1981:3 0.02 1981:1 0.127

Table 15: Andrews-Ploberger Test with private�nal consumption expenditure de�ator

spread p-value no spread p-value∆oil 1980:1 0.151 1980:1 0.036

∆oil+/∆oil− 1980:1 0.081 1980:1 0.036nopi 1980:1 0.2 1980:1 0.02

35

D Sub-sample estimates of alternative prices and activity

indicators

D.1 Unemployment gap case

Table 16: Estimates of the �rst sub-sample with unemployment gapa

∆oil ∆oil+/∆oil− nopi ∆oil ∆oil+/∆oil− nopi19701:1-1981:2 19701:1-1981:4 19701:1-1980:1 19701:1-1981:3 19701:1-1981:3 19701:1-1981:2

c 2.7364 2.1504 2.265274 0.5857 0.8864 0.8243(1.5761)* (1.7327) (1.6799) (1.7271) (1.8707) (1.9177)

πt−1 0.1735 0.2235 0.1926 0.1008 0.1000 0.1006(0.2041) (0.2328) (0.2189) (0.1064) (0.1166) (0.1108)

πt−2 0.2163 0.2608 0.2897 0.1969 0.1832 (0.1108)(0.2072) (0.2208) (0.2205) (0.1048)* (0.10941) (0.1119)

πt−3 -0.0795 -0.0125 -0.1004 0.0224 0.0101 -0.0016(0.1427) (0.1544) (0.1416) (0.0915) (0.0962) (0.1045)

πt−4 0.3551 0.3545 0.3223 0.5157 0.4958 0.5204(0.1269)*** (0.1155)*** (0.1297)** (0.1016)*** (0.0994)*** (0.1056)***

st−1 -0.9132 -0.9211 -1.0311(0.4002)** (0.4885)* (0.485)***

st−2 0.5209 0.4157 0.6165(0.5449) (0.6268) (0.5987)

st−3 0.9112 1.0048 0.9079(0.3646)** (0.3683)** (0.3942)**

ut−1 -5.5578 -4.1741 -6.5062 -2.0913 -1.8575 -1.6812(2.9072)* (2.2433)* (2.9302)** (1.6662) (1.8220) (0.8292)

ut−2 5.1972 2.5338 6.9732 0.2744 0.2894 -0.0095(2.98037)* (2.3441) (2.9145)** (1.6509) (1.6931) (2.1408)

oilt−1 0.0032 0.0031 0.0054 0.0056(0.0007)*** (0.0008)*** (0.0008)*** (0.0008)***

oilt−2 0.0019 0.0014 0.0039 0.0038(0.0008)** (0.0007)** (0.0010)*** (0.0010)***

oilt−3 0.0034 0.0035 0.0026 0.0022(0.0008)*** (0.0009)*** (0.0012)** (0.0022)*

oil+t−1 0.0013 0.0054

(0.0009) (0.0012)***

oil+t−2 0.0038 0.0055

(0.0017)*** (0.0016)***

oil+t−3 0.0045 0.0025

(0.0017)*** (0.0017)

oil−t−1 0.0484 0.0176

(0.0015)*** (0.0192)

oil−t−2 0.0185 -0.0087

(0.0162) (0.0136)

oil−t−3 0.0132 -0.0047

(0.0137) (0.0112)

R2 0.4255 0.4324 0.4288 0.7140 0.7009 0.7081AIC 4.1191 4.1291 4.16 3.4440 3.5225 3.4866DW 1.1557 1.0399 1.2205 2.0149 2.0010 2.0597


ARCH LM-Test 0.0363 0.0127 0.0195 0.2456 0.5766 0.1741

a See notes in Table (3)

36

Table 17: Estimates of the second sub-sample with unemployment gapa


c 0.1842 0.4242 0.1012 0.3176 0.4758 0.2883(0.1679) (0.2077)** (0.1842) (0.2291) (0.2689)* (0.2210)

πt−1 0.3298 0.2358 0.3429 0.3233 0.3082 0.3486(0.099)**** (0.0974)** (0.1027)*** (0.1015)** (0.1032)*** (0.1033)***

πt−2 0.2728 0.2213 0.2388 0.2737 0.2900 0.3001(0.1001)*** (0.0979)** (0.1175)** (0.1015)*** (0.1026)*** (0.1102)***

πt−3 0.2144 0.2150 0.1984 0.2090 0.2241 0.1496(0.1007)** (0.0957)** (0.1211) (0.1025)** (0.0948) (0.1197)

πt−4 0.0857 0.1911 0.1555 0.0829 0.0663 0.0988(0.091) (0.0949)** (0.1211) (0.0921) (0.0948) (0.1348)

st−1 -0.0657 -0.0925 -0.1926(0.2548) (0.2585) (0.2453)

st−2 -0.1037 0.0137 0.1106(0.3804) (0.3856) (0.4006)

st−3 0.0602 -0.0366 -0.0238(0.2364) (0.2423) (0.2662)

ut−1 -1.2717 -1.7305 -1.3878 -1.3163 -1.4403 -1.2268(0.7378)* (0.7131)** (0.6725)** (0.7644)* (0.7757)* (0.7349)*

ut−2 1.1288 1.4690 1.1098 1.291289 1.3193 1.1802(0.7303) (0.7131)** (0.6256)* (0.7692)* (0.7692)* (0.6945)*

oilt−1 -0.0005 -0.0027 -0.0003 -0.0024(0.0012) (0.0011)** (0.0012) (0.0014)**

oilt−2 0.0005 0.0008 0.0008 0.0008(0.0017) (0.0011) (0.0012) (0.0010)

oilt−3 0.0028 0.0028 0.0029 0.0033(0.00129)** (0.0010)*** (0.0012)** (0.0010)**

oil+t−1 -0.0032 -0.0027

(0.0016) (0.0018)

oil+t−2 0.0001 0.0007

(0.0017) (0.0018)

oil+t−3 0.0030 0.0035

(0.0017)* (0.0018)*

oil−t−1 0.0028 0.0044

(0.0027) (0.0029)

oil−t−2 0.0007 0.0004

(0.0028) (0.0029)

oil−t−3 0.0019 0.0027

(0.0027) (0.0028)

R2 0.8445 0.8430 0.8641 0.8414 0.8423 0.8513AIC 2.6540 2.5591 2.7184 3.0298 2.7176 2.7117DW 2.0420 2.0408 1.8796 2.0420 2.0010 2.0024


ARCH LM-Test 0.0006 0.0314 0.0098 0.0004 0.0249 0.0044


37

D.2 PCE de�ator case

Table 18: Estimates of the �rst sub-sample with PCE de�atora


c 2.0640 2.2017 2.0279 0.8600 0.8897 0.8106(0.9970)** (1.0020)** (1.0033)* (1.4769) (1.4468) (1.4605)

πt−1 0.8018 0.6719 0.8077 1.0714 0.9411 1.0650(0.2450)**** (0.2335)*** (0.2473)*** (0.3046)**** (0.2865)*** (0.3044)***

πt−2 0.0594 0.1680 0.0466 0.0997 0.2070 0.1000(0.2192) (0.2233) (0.2161) (0.1947) (0.2052) (0.1920)

πt−3 -0.0989 -0.0900 -0.0763 -0.1900 -0.1635 -0.1591(0.2134) (0.2424) (0.2018) (0.2073) (0.2151) (0.1937)

πt−4 -0.0360 0.0631 0.0298 -0.0638 -0.0395 -0.0708(0.1815) (0.1977) (0.1804) (0.2222) (0.6745) (0.2242)

st−1 1.3442 1.3998 1.3828(0.5968)** (0.5442)** (0.5840)**

st−2 -1.3553 -1.2213 -1.3396(0.7068)** (0.6478)* (0.6741)*

st−3 0.1943 0.1368 0.1856(0.6668) (0.6745) (0.6589)

st−4 0.0233 -0.0417 -0.0285(0.4563) (0.5042) (0.4504)

yt−1 0.7877 0.7639 0.7781 1.3975 1.4420 1.4451(0.4703) (0.4727) (0.4722) (0.5254)** (0.5496)** (0.5119)***

yt−2 -0.3570 -0.2327 -0.3356 -0.7217 -0.5657 -0.6909(0.4376) (0.4501) (0.4404) (0.6451) (0.6117) (0.6239)

oilt−1 -0.0010 -0.0012 -0.0040 -0.0044(0.0011) (0.0012) (0.0021) (0.0022)**

oilt−2 -0.0022 -0.0022 -0.0036 -0.0037(0.0013) (0.0013) (0.0017) (0.0016)

oil+t−1 -0.0012 -0.0043

(0.0012) (0.0022)

oil+t−2 0.0001 -0.0021

(0.0018) (0.0018)

oil−t−1 0.0335 0.0259

(0.0261) (0.0236)

oil−t−2 -0.0088 -0.0058

(0.0163) (0.0161)

R2 0.7370 0.7336 0.7389 0.7617 0.7520 0.7671AIC 3.5675 3.6147 3.5602 3.5306 3.5805 3.5078DW 2.0228 1.9985 1.9861 2.0024 1.9777 1.9861


ARCH LM-Test 0.1653 0.0403 0.1394 0.1747 0.1723 0.2056


38

Table 19: Estimates of the second sub-sample with PCE de�atora


c 0.1379 0.0803 0.0695 0.2020 0.2127 0.0834(0.1438) (0.1729) (0.1716) (0.1966) (0.2261) (0.2337)

πt−1 0.3475 0.3437 0.3797 0.3552 0.3469 0.3897(0.0849)**** (0.0889)*** (0.0980)*** (0.0816)*** (0.0822)*** (0.1033)***

πt−2 0.2778 0.3033 0.2437 0.3090 0.3288 0.2699(0.1178)** (0.1195)** (0.0986)** (0.1112)*** (0.1108)*** (0.0951)***

πt−3 0.3399 0.3360 0.3640 0.2989 0.2988 0.3264(0.0877)*** (0.0927)*** (0.0982)*** (0.0845)*** (0.0890)*** (0.0955)***

πt−4 -0.0393 -0.0546 -0.0589 -0.0385 -0.0495 -0.0547(0.0980) (0.0978) (0.0925) (0.0888) (0.0842) (0.0902)

st−1 -0.2303 -0.2507 -0.1968(0.2286) (0.2249) (0.2440)

st−2 -0.3473 -0.2600 -0.3799(0.3026) (0.2980) (0.3780)

st−3 1.2509 1.1834 1.1997(0.3079)*** (0.3125)*** (0.3770)***

st−4 -0.7414 -0.7396 -0.6582(0.2164)*** (0.2160)*** (0.2362)***

yt−1 0.3595 0.2846 0.3857 0.3112 0.2523 0.3337(0.1894)* (0.1740) (0.2089)* (0.1672)* (0.5496) (0.2054)***

yt−2 -0.2163 -0.1502 -0.2566 -0.1728 -0.1075 -0.2100(0.1952) (0.1873) (0.2098) (0.1769) (0.1720) (0.2101)

oilt−1 0.0038 0.0031 0.0045 -0.0041(0.0001)*** (0.0017)* (0.0012) (0.0017)**

oilt−2 -0.0009 0.0010 -0.0011 0.0005(0.0013) (0.0017) (0.0011) (0.0017)

oil+t−1 -0.0013 0.0018

(0.0008)* (0.0009)**

oil+t−2 0.002 0.0012

(0.0012)* (0.0012)

oil−t−1 0.0086 0.0097

(0.0026)*** (0.0024)***

oil−t−2 -0.0072 -0.0064

(0.0030)** (0.0032)*

R2 0.8951 0.9000 0.8886 0.9067 0.9111 0.8981AIC 3.0310 3.0473 3.0906 2.7257 2.6932 2.8139DW 1.9647 2.0155 2.0155 2.0433 2.0604 2.0327


ARCH LM-Test 0.1370 0.0525 0.0497 0.1306 0.1687 0.2664


39

E Unit root tests

Table 20: Three unit root testsa

None Constant Constant and TrendADF PP ADF PP ADF PP

GDP de�ator 1.42 5.73 -1.16 -0.72 -1.25 -0.82∆GDP de�ator -1.21 -1.37 -1.76 -2.81* -3.86** -6.57***

Private compensations 1.11 5.21 -1.69 -1.26 0.52 -0.12∆Private compensations -1.55 -2.83*** -1.30 -5.64*** -4.55*** -12.03***

Oil prices -0.58 -0.48 -2.18 -2.09 -2.19 -2.15∆Oil prices -12.73*** -12.76*** -12.88*** -12.95*** -12.86*** 0.09***

a ***, ** and * denote signi�cance at the 1%, 5%, and 10% level respectively.

Table 21: Critical values of the unit root tests

None Constant Constant and Trend1% 5% 10% 1% 5% 10% 1% 5% 10%

ADF -2.58 -1.94 -1.62 -3.47 -2.89 -2.58 -4.02 -3.44 -3.14

PP -2.58 -1.94 -1.62 -3.47 -2.89 -2.58 -4.02 -3.44 -3.14

40

References

Andrews, Donald & Ploberger, Werner [1994]. �Optimal Tests when a nuisance parameter is

Present under the Alternative�. Econometrica, 62, pp. 1383�1414.

Bai, J. & Perron, Pierre [1998]. �Estimating and Testing linear models with multiple structural

changes�. Econometrica, 66 (1), pp. 47�78.

Barsky, Robert B. & Kilian, Lutz [2001]. �Do we really know that oil caused the great stag�a-

tion? A monetary alternative�. working paper 8389, NBER.

Bernanke, Ben S., Gertler, Mark & Watson, Mark [1997]. �Systematic Monetary Policy and

the E�ects of Oil Price Shocks�. Brookings Papers of Economic Activity, 1, pp. 91�157.

Blanchard, Olivier J. & Gali, Jordi [2007]. �The macroeconomic E�ects of Oil Price Shocks:

Why are the 2000's so di�erent from the 1970's?� Technical report, NBER, working Paper

13368. Mimeo, MIT and CREI.

Borio, Claudio & Filardo, Andrew [2007]. �Globalisation and in�ation: New cross-country

evidence on the global determinants of domestic in�ation�. working paper 227, Bank of

International Settlements (BIS).

Brook, Anne-Marie, Price, Robert, Douglas, Sutherland, Westerlund, Niels & André,

Christophe [2004]. �Oil Price developments: Drivers, Economic Consequences and Policy

Responses�. Working paper 35, OECD.

Chen, Shiu-Sheng [2009]. �Oil Pass-Through Into In�ation�. Energy Economics, 31, pp. 126�

133.

Christophe, Kamps & Pierdzioch, Christian [2002]. �Monetary Policy Rules and Oil Price

Shocks�. (1090).

Cologni, Alessandro & Manera, Matteo [2008]. �Oil Prices, In�ation and Interest Rates in a

Structural Cointegrated VAR Model for the G-7 Countries�. Energy Economics, 30(3), pp.

856�888.

Cunado, Juncal & Perez de Gracia, Fernando [2003]. �Do oil price shocks matter? Evidence

for some European countries�. Energy Economics, 25, pp. 137�154.

41

Darrat, Ali F., Gilley, Otis W. & Meyer, Don J. [1996]. �U.S Oil Consumption, Oil Prices and

the Macroeconomy�. Empirical Economics, 21, pp. 317�334.

De Gregorio, José, Landerretche, Oscar & Neilson, Christopher [2007a]. �Another Pass-Through

Bites the Dust? Oil Prices and In�ation�. Economia, 7, pp. 155�208.

De Gregorio, Jose, Landerretche, Oscar & Neilson, Christopher [2007b]. �Another pass-trough

bites the dust? Oil prices and in�ation�. Working papers 417, Central Bank of Chile.

Gerlach, Stefan [1997]. �The Information Content of the Term structure: Evidence for Ger-

many�. Empirical Economics, 22, pp. 161�179.

Goodhart, Charles & Hofman, Boris [2005]. �The Phillips Curve, the IS Curve and Monetary

Transmission: Evidence for the US and the Euro Area�. CESIFO Economic Studies, 51, pp.

757�775.

Granger, Clive W [1969]. �Investigating causal relations by Econometrics Models and Cross-

spectral Methods�. Econometrica, 37 (3), pp. 424�438.

Hamilton, James D. [1996]. �This is What Happened to the Oil Price-Macroeconomy Relation-

ship�. Journal of Monetary Economics, 38, pp. 215�220.

Hamilton, James D. [2003]. �What is an oil shock?� Journal of Econometrics, 113, pp. 363�398.

Hooker, Mark A. [2002]. �Are Oil Shocks In�ationary? Asymmetric and Nonlinear Speci�cations

versus Changes in Regime�. Journal of Money, Credit and Banking., 34, pp. 540�561.

Hunt, Benjamin, Isard, Peter & Laxton, Douglas [2001]. �The Macroeconomic E�ects of Higher

Oil Prices�. working paper 01/14, IMF.

Huntington, Hillard G. [2005]. �The Economic Consequences of Higher Crude Oil Prices�.

Energy Modelling Forum (Stanford).

IMF [2000]. �The Impact of Higher Oil Prices on the Global Economy:�. Technical report, IMF.

Jimenez-Rodriguez, Rebeca & Sanchez, Marcelo [2005]. �Oil Price Shocks and Real GDP

Growth: Empirical Evidence for Some OECD Countries�. Applied Economics, 37(2), pp.

201�228.

42

Kilian, Lutz [2006]. �Not All Oil Price Shocks Are Alike: Disentangling Oil And Demand

Shocks in the Crude Oil Market�. Technical Report 5994, CEPR.

Kilian, Lutz [2008a]. �A Comparison of the E�ects of Exogenous Oil Supply Shocks on Output

in the G7 Countries�. Journal of the European Economic Association, 6(1), pp. 78�121.

Kilian, Lutz [2008b]. �Exogenous Oil Supply Shocks: How Big Are They and How Much Do

They Matter for the U.S. Economy?� The Review of Economics and Statistics, 90(2), pp.

216�240.

Leblanc, Michael & Chinn, Menzie [2004]. �Do High Oil Prices Presage In�ation? The Evidence

from G-5 Countries�. Business Economics, 39, pp. 38�48.

Mehra, Yash P. [2004]. �The Output Gap, Expected In�ation and In�ation Dynamics: Another

Look�. Topics in Macroeconomics, 4(1), pp. 1194�1194.

Mishkin, Frederic [1990]. �What does the term structure tell us about future in�ation?� Journal

of Monetary Economics, 25, pp. 77�95.

Mork, Knut A. [1989]. �Oil and the Macroeconomy When Prices Go Up and Down: An

Extension of Hamilton's Results�. Journal of Political Economy, 91, pp. 740�744.

Mork, Knut A, Olsen, Oystein & Mysen, Hans T [1994]. �Macroeconomic Responses to Oil

Price Increases and Decreases in Seven OECD Countries�. Energy Journal, 15, pp. 15�37.

Mory, Javier F. [1993]. �Oil Prices and Economic Activity: Is the Relationship Symmetric?�

The Energy Journal, 14(4), pp. 151�161.

Nordhaus, William [2007]. �Who's Afraid of a Big Bad Oil Shock?� In Brookings Panel on

Economic Activity.

Roberts, John M. [1995]. �New Keynesian Economies and the Phillips Curve�. Journal of

Money Credit and Banking, 27(4), pp. 975�984.

Svensson, Lars E.O [2005]. �Oil prices and ECB Monetary Policy�.

Taylor, John B. [2000]. �Low In�ation, Pass-Trough, and the Pricing Power of �rms�. European

Economic Review, 44, pp. 1389�1408.

43

Van Den Noord, Paul & Christophe, André [2007]. �Why has Core In�ation Remained so Muted

in the Face of Oil Shock?� Working Paper 551, OECD Economics Department.

44

the asymmetric transmission of oil prices to in ation ... · abstract otunderstand the response of...

Documents