Super Cycles in Crude Oil Prices-Zellou-Cuddington(3-22-2012) · 2016-03-01 · economic downturn in 2008-9. ... cyclical components of real crude oil prices, as well as the very

Is There Evidence of Super Cycles in Oil Prices?*

Abdel M. Zellou and John T. Cuddington**

March 22, 2012

Abstract

A number of authors have claimed that the strong upward movement in commodity prices since 2000 represents the early phase of a ‘super cycle’ (SC) driven by the sustained rise in demand associated with industrialization and urbanization in the BRIC countries (Brazil, Russia, India and China). Cuddington and Jerrett (2008) provide statistical evidence on the presence of super cycles in metals prices, defining super cycles as cyclical components between 20 and 70 years (trough to trough) using the Christiano-Fitzgerald band-pass filter.

The purpose of this paper is to address the question: is there evidence of super cycles in crude oil

prices? On one hand, one might expect the strong demand associated with industrialization and urbanization to affect energy prices in a way that is roughly similar to metals, as both are nonrenewable resources. On the other hand, the structure of the crude oil market is quite different from that in other mineral markets, and that structure has changed rather dramatically over time.

Our empirical analysis suggests that there is strong evidence of super cycles in oil prices in the

post-WWII period, and their timing closely matches the SC timing in metals. It appears that the global economy is currently in an expansionary phase of a super cycle that started from a trough in 1996. For the pre-WWII period, on the other hand, the evidence for oil price super cycles is weak. Possible explanations include: (i) oil was economically less important during European and North American industrialization episodes, (ii) pervasive U.S. regulation, (iii) large supply-side shocks caused by new discoveries (e.g. in East Texas and later the Middle East), and (iv) periods of oligopolistic price setting behavior.

* An earlier version of this paper (SPE 147227) was presented at the SPE Annual Technical Conference and Exhibition held in Denver, Colorado, USA, 30 October-2 November 2011. ** PhD candidate and William J. Coulter Professor of Mineral Economics, Colorado School of Mines, Golden, CO.

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I. Introduction

Since the late 1990s, there has been a strong upward surge in a broad range of commodity prices

(from nonrenewables to grains, softs and livestock), temporarily interrupted by the sharp global

economic downturn in 2008-9. A number of authors have claimed that the strong upward movement in

commodity prices represents the expansionary phase of a ‘super cycle’ (SC) caused by industrialization

and urbanization in the BRIC countries (Brazil, Russia, India and China), especially China. These so-

called super cycles have been defined in the literature (Heap 2005; Cuddington and Jerrett 2008) as

having a period between 20 and 50-70 years – including both the expansion and contraction phases of

the cycle. Cuddington and Jerrett (2008) and Jerrett and Cuddington (2008) provide statistical evidence

on the presence of super cycles in metals prices using the asymmetric Christiano-Fitzgerald (ACF) band-

pass filter to decompose a number of metal prices into trend and various cyclical components. (See also

the IMF (2010).)

The primary purpose of this paper is to address the question: is there evidence of super cycles in

crude oil prices? We also provide some interesting statistical evidence on very long-term trends.

Regarding super cycles, one might expect the strong demand associated with industrialization and

urbanization to affect energy prices in a way that is roughly similar to metals, as both are nonrenewable

resources. On the other hand, the structure of the crude oil market is quite different from that in other

mineral markets, and that structure has changed rather dramatically over time. Indeed there was

oligopoly at the end of the nineteenth century during the Rockefeller era, U.S. price controls from the

early 1930s through the 1960s, and the OPEC cartel, which was formed in the early 1960s but became

much more assertive in the 1970s. (See Hamilton (2011) and Yergin (1991) for a detailed discussion of

the history of the oil market. A brief chronology is provided in Appendix A.)

Following the methodology that Cuddington and Jerrett (2008) used to study metal prices, this

paper applies the asymmetric Christiano-Fitzgerald (ACF) band-pass filter to real crude oil prices from

1861 to 2010. The objective is to extract a SC cyclical component with a period between 20 and 70

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years. Cyclical components with periodicity greater than 70 years are then defined as the (very) long-

term trend. Short and intermediate-term cyclical components (<20 years) were also extracted, but are

not the focus of this paper. After extracting the SC component in crude oil prices, we ask: is the SC

component in oil prices similar in timing to those for metal prices – as identified in Heap’s (2005)

historical analysis and Cuddington and Jerrett’s (2008) band-pass filter analysis? We also compare our

oil price super cycles to the oil price ‘epochs’ discussed in Dvir and Rogoff (2009) in Appendix B.

The remainder of the paper is organized as follows. Section 2 provides some additional

background and motivation for our analysis. Section 3 discusses the band-pass filter methodology for

decomposing an economic time series into a number of cyclical components with differernt periods (or

conversely frequencies). Section 4 describes our long-span annual data set for crude oil prices covering

the period from 1861 through 2010 and the application of the ACF band-pass filter to obtain various

cyclical components of real crude oil prices, as well as the very long-term trend. These results are

interpreted by referring important facts about the economic history of the oil market and

industrialization episodes in different regions of the world. Section 5 concludes the paper.

II. Background and Motivation

The economic explanation of the underlying causes of SCs has not yet been formally modeled,

but any complete explanation would clearly focus on the structural transformation that typically

accompanies rising per capita incomes. Simon Kuznets (1973) describes how the initially high share of

agriculture in total employment gradually declines as the manufacturing sector rises with the

industrialization and urbanization that accompanies economic development. As income rises further, the

service sector gains in relative importance, and is ultimately associated with declines in manufacturing

and further declines in agriculture. In the case of the US, for example, the employment share in the

manufacturing sector went from a few percent in 1800 to almost 40% in 1950 before dropping to around

25% at the beginning of this century. During that time period, the employment share in the agricultural

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sector decreased sharply from 90% to single digits while the service sector increased from about 10% to

70%. These shifts in employment shares were presumably mirrored by similar movements in

expenditure shares (on the demand side). Given that the manufacturing sector is presumably the most

mineral and energy intensive of the three broad sectors (agriculture, manufacturing, and services), one

would expect a prolonged surge in mineral and energy demand during the industrialization and

urbanization process in developing nations.

Whether the sustained strength in mineral demand during industrialization leads to a prolonged

period of higher mineral prices will depend on the supply response, which in turn may be importantly

affected by market structure. On the supply side, the SC hypothesis is predicated on the belief that

short-run metal and energy supply curves are very price inelastic due to capacity constraints (which

could be related to constraints in production, refining or transportation, via pipeline or tankers) and the

long gestation periods for capacity-expanding investment projects. In the long run, however, energy

supply curves are much more price elastic. The key factor when modeling market dynamics is the speed

of adjustment. How long does it take to go from the short run (where the supply curve is near vertical)

to the long run (where it is more or less horizontal, according to most mineral and energy economists)?

2.1 Who cares about the presence or absence of super cycles?

Evidence demonstrating the presence of super cycles in energy commodities is valuable for

national and state governments, financial institutions, and oil and gas companies. At the level of

government, countries that rely on the import or export of energy commodities need to take into account

the presence of super cycles in energy commodities in order to define their revenue and spending

policies. At the firm level, the exploration-development-production-distribution-research-and-

development cycle of energy projects often spans several decades, as do super cycles.

Kamel Bennaceur, the chief economist at Schlumberger, one of the world leaders in services in

the oil and gas sector, emphasizes the industry’s long-term planning horizon: “There is high volatility in

the energy sector in terms of prices and investment. Oil and gas companies and service companies work

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on long-term investment and invest significant amounts of resources on technology and developing

people. Energy investments are for the long term: 30 to 50 years. We need long-term commitments from

both the energy suppliers and the demand sector. We need to project ourselves in 20 years.” This

statement from the World Energy Congress 20101 in Montreal focuses mainly on the supply side and the

timeframe is in the SC range.

III. Trend-Cycle Decomposition using the Asymmetric Christiano-Fitzgerald Band Pass Filter

Trends and cycles have been widely studied in various subfields in economics. Seasonal

fluctuations, business cycles (6 to 32 quarters), Kitchin inventory cycles (3-5 years), Juglar fixed

investment cycles (7-11 years), Kuznets cycles applied to real estate and infrastructural investment (15

to 25 years), Bronson asset allocation cycles (around 30 years) and Kondratiev waves or “grand super

cycles” (45 to 60 years) are among those that have received attention.

Cuddington and Jerrett (2008) and Jerrett (2010) provide a good description of the asymmetric

Christiano-Fitzgerald band pass filter methodology, which is used in this paper to extract the super

cycles in oil prices. The use of band pass filters in economics has been promoted by Baxter and King

(1999) and Christiano and Fitzgerald (2003).2 The band-pass or frequency filter extracts cyclical

components of a given time series that lie within a specified ‘window’ or range of frequencies or

(conversely) periods. The user specifies the lower and upper bounds of the periods of the cycles of

interest, e.g. cyclical components with periods within the 20-70 year interval. Thinking of frequency

filters in terms of time rather than frequency domain, Baxter and King explain that band-pass filters are

sophisticated two-sided moving averages. They differ from the standard moving averages in two ways.

First the (ideal) weights of various leads and lags are chosen to filter out cyclical components that do not

1 This conference gathers the leading scientists, regulators and business representatives from the energy sector worldwide. The interview can be found at http://www.energy2point0.com/2010/09/15/kamel-bennaceur-chief-economist-schlumbergerw/ 2 Similar band-pass filter techniques are used in different fields, e.g. hard sciences such as electronics and physics. The first author has encountered it, for example, in spectral imaging and spectral decomposition in geophysics in the oil and gas industry to extract 3D images of reservoirs in the presence of oil, gas or water.

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fall within the chosen window. By choosing symmetric weights on each lead and corresponding lag,

phase shift in the extracted component is prevented. Second, there are asymmetric as well as symmetric

filters. Although asymmetric filters invariably introduce some phase shift into the filtered series, they

have the advantage of allowing computation of the filtered series over the entire data span rather than

being limited to a trimmed data span caused by the number of leads and lags used in calculated the

filtered series. This is obviously advantageous if one is particularly interested in studying cyclical

behavior near the end (or beginning) of the available data span.

Table 1 displays our chosen band-pass windows over the entire range of periods [ ]2,∞ . Note

that these windows are mutually exclusive and exhaustive, implying that the sum of the components will

sum to the actual series. (The seasonal component is not measurable with annual frequency data.)

Cyclical  Component  

   Annual  Data  

Quaterly  Data  

Monthly  Data  

Seasonality   SN   NA   2-­‐6   2-­‐18  Business  Cycle   BC   2-­‐8   6-­‐32   18-­‐96  

Intermediate  Cycle   IC   8-­‐20   32-­‐80   96-­‐240  Super  Cycle   SC   20-­‐70   80-­‐280   240-­‐840  

Trend   T   70-­‐∞   280-­‐∞   840-­‐∞  Actual       2-­‐∞   2-­‐∞   2-­‐∞  

Table 1: Period ‘windows’ for the various cyclical components when using annual, quarterly and monthly frequency data.

An informal way to assess the plausibility of SCs in crude oil prices is to compare them to the

SCs in metals prices. Since the hypothesis is that the cause of these SCs is urbanization and

industrialization, which are energy and metal intensive, one would expect the SCs in metals and oil

prices to have roughly similar timing that matches known development epochs.3

3 In macroeconomic analysis of cycles, there is no attempt to assess whether any cyclical component is ‘statistically significant.’ To accomplish this task (for any cyclical component, be it business cycle, seasonal cycle, or super cycle), one might attempt to devise a method for calculating or simulating confidence bands around cyclical component and then asking whether this component is ever statistically different from zero. Of course, even a regular sin wave cycle with a period of 70 years, would only have three or four peaks and troughs in a dataset covering 149 years, which would result in a test with very low power. (See Gurtler and Cumin for a recent suggestion on testing the significance of cyclical component).

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IV. A Trend-Cycle Decomposition for Real and Nominal Crude Oil Prices, 1861-2010

4.1 Data and Sources for the Nominal and Real Crude Oil Price

Table 2 summarizes our dataset. The oil price series is from the BP Statistical Review. It spans

over 1861-2010 in annual frequency. To get this long span series, BP splices three different oil price

series.4 From 1861 to 1944, the US average oil price is used. From 1945 to 1985, the oil price from the

Arabian light posted at Ras Tanura is used. Finally from 1986 to 2010, the Brent spot price is used. The

Arabian light series begins in 1945, while the Brent series begins in 1986. An Oregon State University

website publishes longest span U.S. Consumer Price (CPI) Index series starting in 1774 on an annual

basis5.

Table 2: List of Data and Sources

Description

Units Frequency Range Source

Oil price $/bbl annual 1861-2010

BP statistics: http://www.bp.com/sectiongenericarticle.do? categoryId=9023773&contentId=7044469

Metals prices annual varies with the metal Alan Heap (Citigroup) Database

PPI annual 1800-2010 Carter et al. (2006) then FRED database (PPIACO)

CPI annual 1774-2010

FRED database and oregonstate.edu/cla/polisci/download- conversion-factors

Figure 1 displays the nominal vs. real price of oil using the two different deflators: Producer

Price Index for All Commodities (PPI) and the Consumer Price Index (CPI). The base year used in this

study to compute the real prices is 2005.

4 Appendix C provides unit root tests in the presence of possible breaks at the splice points and also tests for the statistica significance of breaks. There is no evidence of breaks in the real oil price series at the splice points. 5 A full description of the methodology used to compute the CPI and the different conversion factors are provided in an Excel spreadsheet, downloadable from oregonstate.edu/cla/polisci/download-conversion-factors

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Figure 1: Nominal vs. real price of oil using the two different deflators: Producer Price Index for All Commodities (PPI) and the Consumer Price Index (CPI). The base year is 2005.

4.2 Super Cycles in Nominal and Real Oil Prices

Cuddington and Jerrett (2008) were the first to apply the ACF BPF to commodities, specifically

to metals prices. This paper applies the same statistical technique to oil prices. The ACF filter is used to

extract four mutually exclusive and exhaustive cycles: (i) the business cycle component (2-8 years), (ii)

the intermediate cycle (8-20 years), (iii) the super cycle component (20-70 years) and (iv) the long-term

trend (defined here as cyclical components with periods in excess of 70 years). Our analysis focuses on

the two latter components. If we sum the first three components, we obtain the component (2 70)BP − .

Substracting this sum from the actual series leaves the very long-term trend (70 )BP −∞ as a residual.

This can be expressed with the following equation:

[ (2,8) (8,20) (20,70)] (70, )

(2,70) (70, )Actual BC IC SC TActual BP T

≡ + + + ∞≡ + ∞

The calculated cyclical components are shown in Appendix D for interested readers [Attached.

Not for publication. Available on request from the authors.] A graphical presentation of the

components is more revealing. The bottom panel in Figure 2 displays the super cycle component with

the peaks (P) and the troughs (P). The notation P? and T?, on the other hand, denotes the points that are

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more uncertain due to their low amplitude. It is unclear whether these should be categorized a SC

turning points.

Figure 2: Trend and cycle decomposition of the real price of oil. The top panel displays the real price of oil, the trend component and the cyclical component using the band pass filter over the 2-70 year period (BP(2,70)). That cyclical component, BP(2,70), is the summation of the business, intermediate and supercycle components displaied in the three bottom panels. The bottom panel displays the super cycle component with the peaks (P) and the troughs (P). P? and T? denote the points that one may or may not want to classify as SC truing points due to their low amplitude.

One might wonder if the SCs displayed in Figure 2Error! Reference source not found. depend on

the price deflators used, i.e. Producer Price Index for All Commodities (PPI) vs. the Consumer Price

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Index (CPI). Figure 3 displays the SCs for real price of oil calculated using the alternative deflators.

Note that the SCs for the nominal and real price of oil are very similar (the correlation coefficient is 0.96

when using the CPI and 0.95 when using the PPI as a deflator). Thus, we can conclude that the SCs in

real oil price are not due to movements in the deflator. Moreover, using the PPI or CPI as a deflator

gives very similar results. The difference occurs mainly during the price control period in the 1930s.

Table 3 shows the correlation coefficient matrix between the SCs of the real price of oil, the

nominal price of oil, and the PPI. We can note that the SC for the nominal and real price of oil have a

very high correlation of 0.95, while the correlation coefficient of the nominal price of oil and the price

deflator is 0.79. Appendix C [attached. Not for publication. Available on request from the author]

provides an analysis of the comparison of the WTI and Brent nominal prices, the unit root tests (which

inform the choice of parameters in the ACF filter’s detrending method), and structural break tests at the

splicing dates of the BP price series.

There is strong statistical evidence of three SCs in oil prices: SC1 from 1861 through 1884, SC2

from 1966 through 1996, and SC3 from 1996-present. These are highlighted in Figure 4 below. The

period 1884-1966 is harder to interpret and has been aggregated into one uncertain period for three main

reasons: (i) oil was economically less important during European and North American industrialization

episodes (Figure 5 from Tol (2006)), (ii) the 1884-1966 period includes the price control era, (iii) there

is less of a match in the SCs depending on the deflator used, (iv) there is a lower variance in the price of

oil during that period. The shading corresponds to the super cycles in real oil prices with the

corresponding dates (from trough to trough). The correlation between the SCs in oil and metals prices

before WWII is 0.71 and 0.88 after WWII. It shows a strong correlation between the two after WWII.

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Figure 3: Super cycles in nominal and real price of oil using CPI and PPI (base year 2005) as price deflators. There is little difference between the two series, except during the price control period in the 1930s. The shading corresponds to the expansionary phase of the super cycles in real oil prices using the CPI as deflator. The shading corresponds to the different super cycles in real oil prices as defined in Figure 2.

Figure 4: Super cycles for the real oil price and metals prices. The units on the vertical axis represent percentage deviations from trend. For example, +0.40 indicates 40% above the long-term trend (shown in Figure 2 below). The shading corresponds to the super cycles in real oil prices with the corresponding dates (from trough to trough). There appear to be three obvious SCs in oil prices, the first one between 1861 and 1884, and the last two between 1966-1996 and 1996 to date. The period 1884-1966 is harder to interpret and has been aggregated into one uncertain period.

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Figure 5: Primary energy consumption level (top panel) and share (bottom panel) for the United States between 1850 and 2010. These graphs show that the consumption share for oil was low during the uncertain period. Source: Tol (2006).

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We investigated the correlation between the SC in the nominal and real price of oil and found a

correlation coefficient of 0.95 before World War II and 0.97 for the period after World War II (Table 3).

Correlation   Real   Nominal   Deflator  Real   1   0.97   0.26  

Nominal   0.95   1   0.48  Deflator     0.54   0.77   1  

Table 3: Correlation Coefficient for super cycles in nominal, real oil prices and the deflator (CPI).6 The terms in italics above the diagonal represent the correlation coefficients after WWII. The lower left corner represent the correlation coefficients before WWII.

Figure 6: Real oil prices, along with their trend and super-cycle components. The shading corresponds to the different super cycles in real oil prices identified in Figure 4 above. An upward trend in real oil prices started during World War II. In real terms, the trend in oil prices has increased by roughly 125% over the past 65 years, representing an average annual increase of about 2%. Note that the SC component was only 20% above the trend in 2010, whereas the earlier SC peak in 1981 was almost 100% above the long-term trend. This suggests the current SC is far from over.

4.3 Comparing the Super Cycles in Oil and Metal Prices

The dating of the super cycles in oil prices can now be compared to the ones in metals. Figure 6

6 All the correlation coefficients in this table and in the following correlation coefficient table are statistically significant at the 5% level unless marked otherwise.

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displays the supercycle component and the trend in real oil prices. In this figure the super cycle in oil

price is overlaid with the the principal component of the super cycle for the six LME metals in

(Cuddington and Jerrett 2008). There is a high correlation (.73) between the oil and metals super cycles,

which supposts the SC hypothesis.

The expansionary phase of the SCs can be related to historical episodes of industrialization.

Cuddington and Jerrett (2008) suggest that the SCs in metals prices that they identify are associated with

the industrialization in the US in the late 19th, early 20th century, the reconstruction of Europe after

World War II, the Japanese renaissance in the 1960s and finally the industrialization in the BRIC

countries, mainly driven by China in the 1990s. These SCs in metals match the ones of oil as you can

see in Figure 7. The expansionary phase of the last super cycle started in 1996 with a trough about 70%

below the trend line and the SC component being about 20% above the trend line in 2010. This

information leads us to conclude that we are currently in an expansionary phase of a SC.

Table 4: Period in oil price super cycles over the period 1861-2010. This table displays the expansionary phase (from trough to peak) of the SCs in oil prices, as well as the entire SCs (from trough to trough). For comparison purposes, the SCs for metals are given.

Trough  year  

Expansion  Phase   SC  

SC  for  metals  

~1850  (?)   ~1850-­‐1869  SC  1:  ~1850-­‐

1884      1884   1884-­‐1896          

1906   1906-­‐1919  

Uncertain  Period  

:  1884-­‐1966   ~1900-­‐1937  1932   1932-­‐1944       1937-­‐1965  

1966   1966-­‐1981  SC  2:  1966-­‐

1996   1965-­‐1999  1996   1996-­‐?   SC  3:  1996-­‐?   1999-­‐?  

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Figure 7: Super cycle and trend in real oil prices using the CPI (base year 2005) as price deflator. In the lower panel, the super cycle in oil price is overlaid with the super cycle in metal prices, defined by the principal component of the SCs in the six LME metals in Cuddington and Jerrett (2008). There is a correlation of 0.73 between the oil and the metals super cycles. The right axis is for the trend and the real price of oil, while the left axis is for the SCs. The shading corresponds to the different super cycles in real oil prices as defined in Figure 2.

4.4 The Very Long-Term Trend in Real Oil Prices

Figure 5Error! Reference source not found. shows a pronounced upward trend in real oil prices

since World War II. In real terms, the trend component in oil prices more than doubled with an increase

of 126% over the past 65 years, representing an average annual increase of about 2%. Moreover the SC

component was about 20% above the trend in 2010, knowing that the highest upswing was witnessed in

1981 with a magnitude of almost 100%. The trend component presumably reflects the opposing effects

of increasing scarcity due to depletion and ongoing technological change to alleviate scarcity. An

increase in scarcity in oil causes an increase in the price of oil in real term as described by Hotelling

(1931). An improvement in technology reduces cost and increases the reserves that can be developed

from a given resource. Between 1860 and World War II, we witness a downward trend, which may be

interpreted as oil being more abundant with the discovery of major oil fields and technological change

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with the first logging tool being used in the 1920s for instance. On the other hand, the upward trend

after World War II suggests that increasing scarcity was not completely offset by technological advance.

V. Conclusions and Extensions

Our band-pass filter analysis and reflections on how to interpret the results suggest the following

conclusions:

• There is strong evidence of super cycles in oil prices in the post-WWII period, and their

timing closely matches the SC timing in metals. We have identified two earlier super cycles in

oil price: 1861-1884 (trough to trough) and 1966-96. Moreover, it appears that the global

economy is currently in an expansionary phase of the super cycle from the trough around 1996.

This expansionary phase is presumably driven by urbanization and industrialization in the BRIC

countries.

• For the pre-WWII period, on the other hand, the evidence for oil price super cycles is

weak. Possible explanations include: (i) oil was economically less important during European

and North American industrialization episodes, (ii) pervasive U.S. regulation, (iii) large supply-

side shocks caused by new discoveries (e.g. in East Texas and later the Middle East), and (iv)

periods of oligopolistic price setting behavior.

• The timing of the last two crude oil price super cycles roughly corresponds to the timing

of SCs in metals prices, which in turn matches the timing of the industrialization and

urbanization phase of economic development in US, then Europe and finally Asia.

• Beyond the SC frequency, there is an upward long-term trend in the real price of oil that

started during World War II. It has averaged 2% per year over the past 65 years, but this trend

has been obscured by super cycles as well as shorter-term business cycle fluctuations.

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Presumably, these very long-term trends reflect the opposing effects of increasing scarcity (due

to depletion) and ongoing technological change (to alleviate scarcity through more efficient use

of existing reserves and/or new discoveries).

Our ongoing research on super cycles has empirical and theoretical aspects. On the empirical

front, we are in the process of extending our analysis to coal and natural gas. Preliminary analysis of

coal suggests that there is a strong correlation between the SCs in oil and coal prices, in spite of very

different market structures for the two energy products. On the theoretical front, we are attempting to

develop a simple theoretical model capable of generating super cycle behavior. (See Cuddington and

Zellou (2012)).

In the meantime, we hope our empirical investigation of the possibility of super cycles in crude

oil prices will be of interest to energy-sector analysts, the investment community, and governments in

both energy exporting and importing countries coping with changing trends and cycles in their terms of

trade, and their implications for government revenues.

References

Baxter, Marianne, and Robert G. King. "Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series." Review of Economics and Statistics 81, no. 4 (1999): 575-93.

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Christiano, Lawrence, and Terry Fitzgerald. "The Band Pass Filter." International Economic Review 44, no. 2 (2003): 435-65.

Comin, Diego, and Mark Gertler. "Medium-Term Business Cycles." American Economic Review 96, no. 3 (2006): 523-51.

Cuddington, John T., and Daniel Jerrett. "Super Cycles in Real Metals Prices?". IMF Staff Papers 55, no. 4 (2008): 541-65.

Cuddington, John T., Rodney Ludema, and Shamila A. Jayasuriya. "Prebisch-Singer Redux." In Natural Resources: Neither Curse Not Destiny, edited by Daniel Lederman and William F. Maloney. Stanford University Press, 2007.

Cuddington, John T., and Carlos M. Urzua. "Trends and Cycles in the Net Barter Terms of Trade: A New Approach." Economic Journal 99, no. 396 (1989): 426-42.

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Cuddington, John T., and Abdel M. Zellou. "A Simple Mineral Market Model: Under What Conditions Will It Produce Super Cycles in Prices?" Colorado School of Mines, 2012.

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———. "On the Behaviour of Commodity Prices." The Review of Economic Studies 59, no. 1 (1992): 1-23.

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Tol, Richard S., Stephen W. Pacala , and Robert Socolow. "Understanding Long-Term Energy Use and Carbon Dioxide Emissions in the Usa." SSRN eLibrary (2006).

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APPENDIX A: A Brief History of Oil

Hamilton (2011) and Yergin (1991) provide a very detailed description of the history of the price of oil. The brief chronology provided here relies mainly on these two sources.

-­‐ 1859-1899: Let there be light o 1862-1864: the first oil shock with the rapid drop in oil prices o 1865-1899: evolution of the industry: still drop in price

Hamilton does not see agree with the interpretation of Dvir and Rogoff on the similarities in the behavior of oil prices, in terms of restriction of access to the excess oil supply, between the 19th century and the last quarter of the 20th century. Indeed, Hamilton argues that oil did not have as much economic importance at the end of the 19th century compared to the end of the 20th century. The share of oil in GNP is much smaller in the 19th century compared to the last quarter of the 20th (0.4% of 1900 GNP compared to 4.8% of 2008 GDP).

-­‐ 1900-1945: Power and transportation

o The west coast gasoline famine of 1920 o The great depression and state regulations. These state regulations focused on restricting

production, which allowed a better management of the East Texas Oil Field compared to the early fields in Pennsylvania (see Figures 1 and 5 of the cited paper).

-­‐ 1946-1972: The early postwar era.

o State regulation o 1947-1948: Postwar dislocations: increase in the price of oil due to acceleration in the use

of vehicles. o 1952-1953: supply disruptions and the Korean conflict. o 1956-1957: Suez Crisis o 1969-1970: Modest price increases.

-­‐ 1973-1996: The age of OPEC. o 1973-1974: OPEC Embargo. o 1978-1979: Iranian revolution. o 1980-1981: Iran-Iraq War. o 1981-1986: The great price collapse. o 1990-1991: First Persian Gulf War.

-­‐ 1997-2010: A new industrial age. o 1997-1998: East Asian Crisis. o 1999-2000: Resumed growth. o 2003: Venezuelan unrest and the second Persian Gulf War. o 2007-2008: Growing demand and stagnant supply.

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APPENDIX B: Comparison to Dvir and Rogoff

Dvir and Rogoff (DR) perform a statistical analysis of the change in the persistence and the change in volatility of the price of oil, provide an extended commodity storage model, and give a historical explanation of these transition points related to industrialization and a change in market structure.

DR analyze the same long-span real oil price series considered in this paper. At the outset, they note that “Studies of the time series properties of real oil prices have taken one of the following approaches in the face of these clear non-linearities in the series: either analyzing the series as a whole, or, much more commonly, treating the series as composed of separate series ‘pasted together’, and proceeding to analyze them in isolation.” They pursue the second approach, using a number of econometric techniques to test for possible structural breaks at unknown dates. Their tests allow for changes in persistence and volatility of the oil price series: “In what follows we will treat both the assumption of a pure I(0) process and the assumption of a pure I(1) process as our null hypotheses, and test whether the series exhibits a shift from I(0) to I(1) (or vice versa) against both of these assumptions. […] This allows us to test for structural change without taking an a-priori stand regarding the null hypothesis.”

In contrast, our approach of looking for very-long cycles requires that we consider the price

series as a whole. One cannot detect 20-70 year cycles, in subsample of the data span that are of relatively short duration. The BPF analysis requires the specification of the nature of the underlying trend: mean stationarity, trend stationarity or a unit root process with drift. Based on our unit root test results, we adopted the latter specification. Clearly, this is less general than the DR approach of allowing for shifts in the persistence of the oil price series from I(0) to I(1), but unavoidable if one wants to employ existing BPF methodology.

Dvir and Rogoff detect three ‘epochs’ in oil prices characterized by changes in persistence and

volatility:

• Epoch I from 1861-1877 has high prices and high volatility • Epoch II from 1878-1972 is a period of low prices with low (1878-1933) and then even lower

(1934-72) volatility • Epoch III from 1973-2009 is again a period of high prices and high volatility.

Figure 7 displays the three epochs from DR with the shading overlaid on our oil price SCs. For consistency, we use the CPI as the deflator, as in DR. There is a similarity between the epochs defined by DR and the SCs. The dating of their epochs matches up well with the neutral zero value for the super cycle component. We define our super cycle expansions going from trough to peak, whereas the DR epochs in effect go from our SC midpoint to midpoint. Interestingly, the BPF analysis identifies the start of a new SC emerging from a trough around 1996.

DR emphasize that their epochs reflect a confluence of strong demand growth (associated with industrialization in major regions of the world) and important changes in market structure on the supply side. They stress the pivotal role of supply restrictions in leading to the high volatility and high prices in Epoch I and Epoch III when industrialization was occurring in the US and South East Asia, respectively. During Epoch I, the restriction in supply was initiated by Rockefeller with the quasi monopoly on oil refining and the oligopoly of the railroad companies in the transportation of oil. In Epoch III, the restriction of supply was due to the rising market power of OPEC.

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The amplitude of our first and last two SCs is much more pronounced than in the middle where

the SCs are affected by price regulation. In that respect, we do see similarities if we take into account the amplitude of these SC and superimpose it on the three epochs defined by DR. DR used a different statistical technique and we arrive at similar conclusion in terms of the price behavior of oil in the long-term.

Figure 8: Super cycles on real prices of oil (2005) (CPI deflated) with the epochs defined by Dvir and Rogoff (shaded in this graph).

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***** Not for publication. Available on request from the authors.

APPENDIX C: Econometric Analysis of the BP Price Series

Comparison of the WTI and Brent Nominal Prices

Figure 8 displays the nominal price of oil (on natural logarithm scale). In order to assess if the choice of benchmark (Brent, WTI, Arabian light, etc.) is impacting the analysis, we decided to make a comparison between WTI and Brent over the 1986-2010 period. Figure 9 gives the price of oil in log scale using WTI. Figure 10 shows both prices (Brent and WTI) in logs as well as the difference. There is no significant difference between WTI and the Brent prices. Hence, the results presented in this paper will be using the BP series only, even though we computed the SCs in both cases using Brent and WTI. Figure 9: Nominal price of oil in level on a log scale (Source BP statistical review (BP 2011)).

Figure 10: Nominal price of oil in level on a log scale using the WTI starting in 1986.

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Figure 11: Nominal price of the WTI and Brent in logs over the 1986-2010 period and the difference.

Unit Root Tests

As described earlier, the oil series is composed of three different prices, with the Arabian Light price being used after 1944 and the Brent price starting in 1986 and. We want to investigate whether there is a presence of a structural break at these splicing points (at 1986 and 1944).

First, we checked the presence of a unit root on the log of the real price of oil. The Phillips-

Perron test statistic in Table 6 shows that the log of the real price of oil is integrated of degree one (I(1)). It means that the time series needs to be differentiated once to be stationary. Failing to do so will lead to spurious regressions as described by Cuddington, Ludema, and Jayasuriya (2007) and Cuddington and Urzua (1989). The Philips-Perron unit root test is preferred to the Dickey-Fuller test in the case of time varying volatility, which is the case here. The Philips-Perron unit root test uses robust standard error while the Dickey-Fuller does not. The null hypothesis of a second unit root is rejected. The lag length selection criteria show that two lags are necessary based on the sequential modified likelihood ratio (LR) test statistic and the Akaike information criterion (AIC) (Table 7). Hence, we will run the following univariate model:

1 1 2_ _ _ _ _ _t t t tDLPRoil BP cpi DLPRoil BP cpi DLPRoil BP cpi eα β − −= + + +

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Table 5: Unit root test on the real price of oil. The series is I(1)

Table 6: Lag length selection for the real price of oil

Structural Breaks

When using the univariate equation specified above to test for the presence of structural breaks at the splicing date we find that there are no structural breaks (Table 8, Figure 12 and Table 9). We also performed a Chow breakpoint test at several other dates around these splicing dates, in 1945, 1946, 1984 and 1985, and they all rejected the presence of a structural break.

24

Table 7: Univariate regression of the real price of oil using two lags

Table 8: Chow test to test for structural breaks at the splicing points of the oil price series in 1944 and 1986. There are no structural breaks

Figure 12: Correlogram of the residual corresponding to the univariate regression of the real oil price above (in first difference of the log term)

25

***** Not for publication. Available on request from the authors.

APPENDIX D: Trend and Cycles components for Real Oil Prices Based on the Asymmetric Cristiano-Fitzgerald Band-Pass Filter

All these series are in log terms.

obs  Real  Oil  Price   Trend  

Business  Cycle  

Intermediate  Cycle  

Super  Cycle  

1860   NA   NA   NA   NA   NA  1861   3.20   3.73   -­‐0.58   -­‐0.13   0.19  1862   3.93   3.75   -­‐0.31   0.17   0.32  1863   4.93   3.77   0.29   0.41   0.46  1864   5.73   3.79   0.82   0.53   0.58  1865   5.28   3.80   0.27   0.52   0.70  1866   4.64   3.82   -­‐0.35   0.38   0.79  1867   4.19   3.83   -­‐0.68   0.18   0.85  1868   4.61   3.84   -­‐0.12   -­‐0.01   0.89  1869   4.65   3.85   0.05   -­‐0.15   0.90  1870   4.74   3.86   0.22   -­‐0.22   0.88  1871   4.90   3.87   0.44   -­‐0.24   0.82  1872   4.74   3.87   0.34   -­‐0.22   0.74  1873   4.04   3.88   -­‐0.28   -­‐0.18   0.63  1874   3.62   3.88   -­‐0.62   -­‐0.15   0.50  1875   3.76   3.88   -­‐0.36   -­‐0.11   0.35  1876   4.42   3.88   0.42   -­‐0.07   0.19  1877   4.42   3.88   0.54   -­‐0.03   0.04  1878   3.73   3.87   -­‐0.04   0.01   -­‐0.12  1879   3.40   3.87   -­‐0.25   0.04   -­‐0.26  1880   3.51   3.86   -­‐0.03   0.07   -­‐0.38  1881   3.40   3.85   -­‐0.05   0.08   -­‐0.49  1882   3.26   3.84   -­‐0.11   0.09   -­‐0.56  1883   3.48   3.83   0.15   0.11   -­‐0.61  1884   3.36   3.82   0.04   0.13   -­‐0.63  1885   3.45   3.80   0.11   0.16   -­‐0.62  1886   3.26   3.79   -­‐0.11   0.18   -­‐0.59  1887   3.19   3.77   -­‐0.20   0.16   -­‐0.53  1888   3.47   3.75   0.08   0.10   -­‐0.46  1889   3.51   3.73   0.16   -­‐0.01   -­‐0.38  1890   3.43   3.71   0.14   -­‐0.13   -­‐0.29  1891   3.17   3.69   -­‐0.08   -­‐0.24   -­‐0.20  1892   3.01   3.66   -­‐0.23   -­‐0.30   -­‐0.12  1893   3.13   3.64   -­‐0.16   -­‐0.30   -­‐0.05  

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1894   3.43   3.62   0.04   -­‐0.24   0.01  1895   3.94   3.59   0.43   -­‐0.13   0.05  1896   3.78   3.56   0.15   -­‐0.01   0.07  1897   3.37   3.54   -­‐0.35   0.10   0.07  1898   3.51   3.51   -­‐0.23   0.17   0.06  1899   3.86   3.48   0.14   0.20   0.03  1900   3.75   3.46   0.12   0.19   -­‐0.01  1901   3.50   3.43   -­‐0.02   0.15   -­‐0.05  1902   3.29   3.40   -­‐0.11   0.10   -­‐0.10  1903   3.42   3.37   0.14   0.05   -­‐0.14  1904   3.29   3.34   0.09   0.03   -­‐0.17  1905   2.97   3.31   -­‐0.17   0.02   -­‐0.20  1906   3.13   3.29   0.03   0.03   -­‐0.21  1907   3.09   3.26   0.01   0.02   -­‐0.20  1908   3.03   3.23   -­‐0.02   0.00   -­‐0.18  1909   3.04   3.20   0.03   -­‐0.05   -­‐0.14  1910   2.90   3.18   -­‐0.06   -­‐0.12   -­‐0.09  1911   2.85   3.15   -­‐0.08   -­‐0.19   -­‐0.03  1912   3.04   3.13   0.12   -­‐0.23   0.03  1913   3.23   3.10   0.25   -­‐0.23   0.10  1914   3.09   3.08   0.00   -­‐0.16   0.17  1915   2.84   3.05   -­‐0.40   -­‐0.04   0.23  1916   3.37   3.03   -­‐0.03   0.10   0.28  1917   3.64   3.01   0.11   0.21   0.32  1918   3.69   2.99   0.10   0.26   0.34  1919   3.50   2.97   -­‐0.04   0.23   0.35  1920   3.77   2.95   0.34   0.14   0.34  1921   3.03   2.93   -­‐0.25   0.03   0.32  1922   3.05   2.91   -­‐0.09   -­‐0.06   0.29  1923   2.93   2.89   -­‐0.11   -­‐0.10   0.24  1924   2.98   2.88   -­‐0.03   -­‐0.07   0.20  1925   3.14   2.86   0.14   -­‐0.01   0.14  1926   3.23   2.85   0.22   0.06   0.09  1927   2.85   2.84   -­‐0.13   0.09   0.05  1928   2.76   2.82   -­‐0.13   0.06   0.01  1929   2.86   2.81   0.09   -­‐0.02   -­‐0.02  1930   2.79   2.80   0.16   -­‐0.13   -­‐0.05  1931   2.21   2.79   -­‐0.32   -­‐0.21   -­‐0.06  1932   2.59   2.79   0.09   -­‐0.23   -­‐0.06  1933   2.42   2.78   -­‐0.12   -­‐0.18   -­‐0.06  1934   2.87   2.77   0.22   -­‐0.08   -­‐0.05  1935   2.81   2.77   0.03   0.05   -­‐0.03  1936   2.90   2.76   0.01   0.15   -­‐0.02  1937   2.97   2.76   0.00   0.20   0.00  

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1938   2.89   2.76   -­‐0.09   0.20   0.02  1939   2.81   2.76   -­‐0.13   0.14   0.04  1940   2.82   2.76   -­‐0.05   0.06   0.05  1941   2.92   2.76   0.12   -­‐0.01   0.06  1942   2.92   2.76   0.16   -­‐0.06   0.06  1943   2.81   2.76   0.06   -­‐0.07   0.06  1944   2.75   2.76   -­‐0.01   -­‐0.05   0.06  1945   2.59   2.76   -­‐0.21   -­‐0.02   0.06  1946   2.64   2.77   -­‐0.20   0.02   0.05  1947   3.08   2.77   0.23   0.03   0.05  1948   2.97   2.78   0.12   0.03   0.04  1949   2.77   2.78   -­‐0.04   0.00   0.03  1950   2.75   2.79   -­‐0.03   -­‐0.04   0.03  1951   2.73   2.80   0.00   -­‐0.08   0.02  1952   2.65   2.81   -­‐0.06   -­‐0.11   0.01  1953   2.75   2.81   0.05   -­‐0.10   -­‐0.01  1954   2.74   2.82   0.02   -­‐0.08   -­‐0.03  1955   2.74   2.83   -­‐0.01   -­‐0.03   -­‐0.06  1956   2.74   2.84   -­‐0.02   0.01   -­‐0.09  1957   2.71   2.86   -­‐0.05   0.05   -­‐0.14  1958   2.77   2.87   0.03   0.06   -­‐0.19  1959   2.74   2.88   0.06   0.06   -­‐0.25  1960   2.64   2.89   0.01   0.05   -­‐0.32  1961   2.57   2.91   -­‐0.02   0.06   -­‐0.38  1962   2.56   2.92   -­‐0.01   0.09   -­‐0.44  1963   2.55   2.94   -­‐0.02   0.13   -­‐0.50  1964   2.54   2.95   -­‐0.03   0.16   -­‐0.54  1965   2.52   2.97   -­‐0.03   0.15   -­‐0.56  1966   2.51   2.98   0.00   0.10   -­‐0.57  1967   2.48   3.00   0.04   -­‐0.01   -­‐0.55  1968   2.45   3.02   0.08   -­‐0.14   -­‐0.51  1969   2.40   3.03   0.07   -­‐0.26   -­‐0.44  1970   2.35   3.05   -­‐0.04   -­‐0.33   -­‐0.34  1971   2.51   3.07   -­‐0.03   -­‐0.31   -­‐0.22  1972   2.56   3.09   -­‐0.22   -­‐0.23   -­‐0.08  1973   2.81   3.11   -­‐0.28   -­‐0.09   0.08  1974   4.01   3.13   0.59   0.05   0.24  1975   3.89   3.15   0.17   0.16   0.40  1976   3.89   3.17   -­‐0.06   0.22   0.56  1977   3.92   3.20   -­‐0.22   0.24   0.70  1978   3.86   3.22   -­‐0.41   0.23   0.81  1979   4.59   3.24   0.23   0.22   0.90  1980   4.63   3.27   0.22   0.19   0.95  1981   4.46   3.29   0.05   0.15   0.97  

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1982   4.26   3.31   -­‐0.08   0.08   0.94  1983   4.09   3.34   -­‐0.10   -­‐0.03   0.88  1984   4.03   3.36   0.05   -­‐0.16   0.78  1985   3.94   3.39   0.19   -­‐0.29   0.65  1986   3.26   3.41   -­‐0.29   -­‐0.37   0.50  1987   3.48   3.44   0.08   -­‐0.36   0.33  1988   3.24   3.47   -­‐0.11   -­‐0.27   0.15  1989   3.39   3.49   0.05   -­‐0.11   -­‐0.03  1990   3.61   3.52   0.23   0.06   -­‐0.21  1991   3.38   3.54   -­‐0.01   0.21   -­‐0.36  1992   3.30   3.57   -­‐0.05   0.28   -­‐0.50  1993   3.14   3.60   -­‐0.13   0.28   -­‐0.60  1994   3.04   3.63   -­‐0.12   0.22   -­‐0.68  1995   3.09   3.65   0.02   0.13   -­‐0.72  1996   3.26   3.68   0.25   0.06   -­‐0.73  1997   3.15   3.71   0.14   0.00   -­‐0.70  1998   2.72   3.73   -­‐0.33   -­‐0.03   -­‐0.65  1999   3.05   3.76   -­‐0.08   -­‐0.06   -­‐0.58  2000   3.49   3.79   0.28   -­‐0.09   -­‐0.49  2001   3.30   3.82   0.01   -­‐0.13   -­‐0.39  2002   3.29   3.84   -­‐0.10   -­‐0.16   -­‐0.29  2003   3.42   3.87   -­‐0.11   -­‐0.16   -­‐0.18  2004   3.68   3.89   -­‐0.02   -­‐0.11   -­‐0.09  2005   4.00   3.92   0.11   -­‐0.03   0.00  2006   4.15   3.95   0.07   0.06   0.07  2007   4.22   3.97   -­‐0.02   0.13   0.13  2008   4.51   4.00   0.19   0.15   0.17  2009   4.03   4.02   -­‐0.30   0.11   0.20  2010   4.26   4.04   -­‐0.04   0.04   0.21