How Important Is The Commodity
Supercycle?
Andrés Fernández

International Monetary Fund

Stephanie Schmitt-Grohé

Columbia University
Centre for Economic and Policy Research
National Bureau of Economic Research

Martín Uribe

Columbia University
National Bureau of Economic Research
World commodity prices are known to display long cycles. These
cycles have a periodicity of 20 to 30 years and are called commodityprice supercycles. Figure 1 displays the time paths of eleven commodity
prices deflated by the U.S. consumer price index over the period 1960
to 2018. All commodity prices appear to have long cycles in accordancewith the supercycle hypothesis. In particular, commodity prices display
two peaks post 1960—one in the early 1980s and one in the early2010s. In the academic and financial-industry literature, the upswing
We are gratefull for the comments of Roberto Chang and seminar participants at
Johns Hopkins, Wisconsin, U. Tubingen, Tsinghua, the Macro online seminar series(Hong Kong Baptist, National Taiwan, Yonsei, and Academia Sinica), U. Hamburg,
Bank of Canada, the 2020 CEBRA Workshop for Commodities and Macroeconomics(Central Bank of Chile), the 2021 Central Bank of Chile Annual Conference, the 2021
AEA Meeting, LACEA 2021, CEPII-Paris, and the 2022 JPMCC Intl. Commodities
Symposium. Daniel Guzman and Ken Teoh provided outstanding research assistance.
An earlier version of this paper circulated under the title “Does the Commodity Super
Cycle Matter?” Research for this work was conducted while Andrés Fernández wasaffiliated with the Central Bank of Chile. The information and opinions presented are
entirely those of the authors, and no endorsement by the Central Bank of Chile, the IMF
or their Board Members or IMF Management is expressed or implied. The replication
codes and the time series of the commodity supercycle estimated in our work can be
downloaded from http://www.columbia.edu/~mu2166/fsu2/.
Credibility of Emerging Markets, Foreign Investors’ Risk Perceptions, and Capital
Flows edited by Álvaro Aguirre, Andrés Fernández, and Sebnem Kalemli-Özcan,
Santiago, Chile. © 2023 Central Bank of Chile.

205

206 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribein commodity prices leading to the 1980s peak is typically attributed
to the post-World War II reconstruction of Western Europe and Japanand to the cartelization of the crude oil market. The peak in the early2010s is frequently attributed to the accession of China and othersoutheast Asian countries to world markets.
The existing literature on commodity-price supercycles has mainly
focused on documenting their frequency, amplitude, and turning
points. Less work has been devoted to estimating the importance ofcommodity-price supercycles for economic activity. The contributionof this paper is to identify global disturbances that cause regularcycles and supercycles in world commodity prices and to estimate
the contribution of these global shocks to aggregate fluctuations inemerging and developed countries.
The econometric-oriented related literature typically uses spectral
analysis to identify commodity-price supercycles. Cuddington andJerrett (2008) pioneered the use of the asymmetric band-pass filter ofChristiano and Fitzgerald (2003) to identify supercycles in commodity
prices. They apply this technique to the prices of six metals tradedon the London Metal Exchange. Subsequently, Erten and Ocampo(2013) apply this methodology to the identification of supercycles inreal non-oil commodity prices.
The present paper proposes a different methodology to identify
long cycles in world commodity prices. It identifies the commodity
supercycle as a common permanent component in all commodity prices.
The proposed common permanent component approach to identifying
the commodity-price supercycle has two advantages relative to thespectral analysis approach. First, the spectral approach applies the
band-pass filter to individual commodity-price time series separately.
As a result, it delivers one supercycle per commodity price. However,the data strongly suggests that long cycles in commodity prices
are correlated. The top left panel of figure 1, which plots all elevencommodity prices together, shows that long swings in commodity prices
are synchronized. This correlation has been interpreted as reflecting
the existence of a common driver. The common permanent component
approach we propose delivers this common driver by identifying
the nonstationary world shock responsible for the supercycle in allcommodity prices.

Figure 1. Eleven Real Commodity Prices, 1960:I–2018:IV
Commodities prices

Coal

Crude oil

2

1

2

0

0

0

-2

-1

-2

-4
1960

1980

2000

2020

-2
1960

Natural gas

1980

2000

2020

-4
1960

Beverages
2

2

0

1

1

-1

0

0

1980

2000

2020

-1
1960

Agricultural raw
materials
0.5

1980

2000

2000

2020

Food

1

-2
1960

1980

2020

Fertilizers

-1
1960

1980

2000

2020

Metals and minerals

2

1

0

0
0

-1

-0.5

-1
1960

1980

2000

2020

-2
1960

Gold

1980

2000

2020

-2
1960

Silver

1

1980

2000

2020

Platinum

2

1

0

0
0
-1

-1

-2
1960

1980

2000

2020

-2
1960

1980

2000

2020

-2
1960

1980

2000

2020

Source: Authors’ calculations.
Note: The frequency of the data is quarterly. The eleven commodity prices are deflated by the U.S. consumer priceindex and expressed in logs. All series are normalized to zero in 2010.IV.

208 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
A second desirable property of the common permanent component
approach proposed in this paper for the identification of the commodity
supercycle is that it allows for the joint estimation of transitory andpermanent, domestic and world disturbances affecting aggregate
activity in individual countries. As a result, the common permanent
component approach provides a natural environment for estimating
the contribution of the shocks responsible for the supercycle toexplaining variations in output at the country level.
The paper formulates an empirical model that includes elevencommodity prices, the world interest rate, and the output of 24(quarterly sample) or 41 (annual sample) small open developedand emerging economies. All commodity prices are assumed to becointegrated with a common nonstationary world shock. In addition,commodity prices and the world interest rate are assumed to bebuffeted by stationary world shocks. Output at the individual country
level is driven by the nonstationary and stationary world shocks, anonstationary country-specific shock, and a stationary country-specificshock. Thus, a constellation of stationary and nonstationary world anddomestic shocks compete to explain movements in country-specificoutput. The nonstationary world shock is the one responsible forthe commodity-price supercycle. Thus, ascertaining the role of thesupercycle in accounting for output movements in a given country
amounts to estimating the share of the nonstationary world shock inthe variance decomposition of the country’s output.
The model is cast in terms of deviations of endogenous variablesfrom their respective stochastic trends and exogenous shocks. Since
the exogenous shocks and the stochastic trends are unobservable, thevariables in the model are latent variables. The estimation exploits thefact that the model delivers precise predictions for variables that are
observed. In particular, the observable variables used in the estimationof the model are the growth rates of the eleven commodity prices infigure 1, the level of the world interest rate, and the growth rates of
output of the countries included in the sample. The likelihood of thedata is computed by using the Kalman filter, and the econometricestimation employs Bayesian techniques. The model is estimated onquarterly data covering the period 1960 to 2018. For countries forwhich quarterly output data since 1960 is not available, the model isestimated on annual data.
The paper delivers three main results. First, the common permanent
component plays an important role in explaining movements incommodity prices at frequencies typically associated with the supercycle.

How Important Is The Commodity Supercycle?

209

Specifically, the common permanent component explains on average
across commodities between 67 and 91 percent of the forecast error
variance of commodity prices at horizons between five and thirty years.
Second, world shocks that drive commodity prices and the worldinterest rate are major drivers of aggregate fluctuations in developedand emerging small open economies. Jointly the stationary andnonstationary world shocks explain more than half of the varianceof output growth on average across countries. Third, and more
importantly, the bulk (more than two thirds) of the explanatory powerof world shocks stems from stationary shocks. These results obtain
not only unconditionally but also conditionally on time horizons. Evenat forecasting horizons typically associated with the supercycle (20years or longer), stationary world shocks play a larger role than the
nonstationary world shock in explaining the forecast error variance ofthe level of output in individual countries. Taken together, these results
suggest that the commodity-price supercycle matters for explaining
aggregate activity at the country level, but that its contribution issmaller than that of stationary world shocks.
This paper is related to several strands of literature. The empiricalmodel follows recent studies that identify permanent disturbances andtheir contribution to business cycles (Uribe, 2018). The main results
speak to a body of work on the role of world prices as mediators ofworld shocks for economic outcomes in small open economies. Mendoza(1995) and Kose (2002), by using calibrated real business-cycle models
fed with estimated stochastic processes for the terms of trade, findthat disturbances to this international price account for more than
thirty percent of fluctuations in aggregate activity. More recently,
Miyamoto and Nguyen (2017), and Drechsel and Tenreyro (2018) findsimilar results by using a Bayesian estimation approach. SchmittGrohé and Uribe (2018) apply a more agnostic approach based on
structural vector autoregressions and find that the contribution ofterms-of-trade shocks to explaining aggregate fluctuations in poor andemerging economies is only ten percent. These authors argue for theneed to consider more disaggregated measures of world prices to better
capture the transmission of world shocks to individual economies.
Fernández and others (2017) employ a similar empirical strategy as
Schmitt-Grohé and Uribe (2018) but expand the set of world prices
from one to four, three commodity prices, and the world interest rate,
and find that world shocks mediated by this set of prices explain
one third of output fluctuations on average in a set of 138 countries
over the period 1960 to 2015. This figure more than doubles when210 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
the estimation is conducted on a more recent sample beginning in
the late 1990s, as shown by Shousha (2016), Fernández and others(2018), and Fernández and others (2017). In the papers just cited,shocks to commodity prices, if explicitly modeled, are assumed to bestationary, and as a result, this body of work does not speak directly
to the importance of commodity-price supercycles.
As mentioned earlier, key references on the use of spectral analysis
for the estimation of commodity-price supercycles are Cuddington
and Jerrett (2008) and Erten and Ocampo (2013). These papers andthe early work by Pindyck and Rotemberg (1990) speculate on theexistence of common drivers of commodity prices, which serves as
motivation for the common permanent component approach proposedin the present paper. Alquist and others (2020) apply a factor-based
identification strategy to estimate the role of commodity prices in
explaining the global economic activity. Benguria and others (2018)
identify the commodity-price supercycle by HP filtering and analyze
its transmission by using firm-level administrative data from Brazil.
The paper also contributes to a literature assessing the roleof transitory and permanent shocks in driving business cycles indeveloped and emerging economies.1It finds that, for both developedand emerging countries, transitory shocks play a larger role thanpermanent shocks, even if one conditions on world shocks or on
country-specific shocks.
Finally, the structuralist literature pioneered by Prebisch (1950)
and Singer (1950) argues that secular deterioration in the terms oftrade plays a central role in the development of emerging countries.
The empirical relevance of this hypothesis has been the subject of
debate. One reason is that, as figure 1 suggests, it is not clear that
overall commodity prices display secular deterioration. In the context
of that literature, deterioration of the terms of trade is understood as
primary commodity prices growing on average at a slower pace thanprices of nonprimary goods. Our commodity-price data is deflated by
the U.S. CPI index, and as such secular deterioration should manifest
as a downward trend in raw data. Over the sample period consideredin the figure, some commodity prices display a downward trend (e.g.,
agricultural raw materials) but others display an upward trend (e.g.,energy and precious metals). The present analysis does not aim to
explain the effect of trending real commodity prices on economic
1. See, among others, Aguiar and Gopinath (2007), García-Cicco and others (2010),
Chang and Fernández (2013), and Miyamoto and Nguyen (2017).

How Important Is The Commodity Supercycle?

211

development. However, our analysis does have a point of contact with
the Prebisch-Singer hypothesis in that it allows for innovations in thepermanent component of real commodity prices to have an effect on
output growth at the country level.
The remainder of the paper is organized into seven sections.
Section 1 presents the empirical model. Section 2 introduces theobservables, the priors, and the estimation strategy. Section 3 presents
the definitions and sources of the quarterly data on commodity prices,world interest rates, and output in 24 predominantly developed
economies spanning the period 1960.I to 2018.I. Section 4 analyzes
the estimated commodity-price supercycle. Section 5 presents variancedecompositions, forecast error variance decompositions, and impulse
response analysis to ascertain the importance of the commodity
supercycle for aggregate activity in the small open economies
considered. Section 6 estimates the model on annual data from 1960
to 2018 for 24 emerging and 17 developed countries2. Finally, section
7 concludes.

1. An Empirical Model of the Commodity Supercycle
The empirical model consists of a world block and a countryspecific block. The world block describes the evolution of the vector pt
containing eleven real commodity prices and the gross real interest
rate in quarter t, all expressed in logarithms. The commodity
supercycle is modeled as a nonstationary exogenous variable Xtp withthe property of being cointegrated with the eleven commodity prices.
We can then define a vector of transformed world prices, denoted ,that is stationary as follows:3

2. For further technicals details see Appendix.
3. For expositional purposes, constant terms are omitted. The model with constantterms is presented in the Appendix.

212 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
The identification assumption that all commodity prices have thesame cointegrating vector with Xtp is based on the observation that,in the raw data, commodity prices do not seem to diverge from one
another over time (see figure 1, upper left panel).
The vector is assumed to be buffeted by a nonstationary shock,given by variations in the growth rate of the permanent componentof world prices,, and 12 stationary world shocks
denoted ztp. The vector of world prices evolves according to the following
autoregressive process:(1)

where Bpip for i = 1, 2, 3, 4, CpX p , and Cpz p are matrices of coefficientsof order 12-by-12, 12-by-1, and 12-by-12, respectively. Without lossof generality, assume that Cpz p is lower triangular with ones on
the diagonal. This is not an identification restriction. The elementsof ztp should be interpreted as combinations of stationary worldshocks affecting commodity prices and the interest rate. The present
study does not aim to identify these shocks individually, but rather
to ascertain their joint contribution to explaining movements inworld prices and aggregate activity and to compare it to that of thenonstationary world shock Xtp driving the commodity supercycle.
The domestic block consists of the vector yt containing real output
for 24 small open economies expressed in logarithms and denoted
yti, i = 1,...,24. In each country, output is assumed to be cointegrated
with a linear combination of a country-specific nonstationary shock,denoted Xti for i = 1,...,24 and the nonstationary component of realworld commodity prices, Xtp. The rationale behind the assumptionthat Xtp enters in the cointegrating relationship of output is that, inmodels of small open economies with commodity prices, output inherits
their stochastic properties. We note that this long-run relationshipbetween output and the nonstationary component of world shocks is
not subject to the observation made by Kehoe and Ruhl (2008) that,
depending on how real GDP is measured in the data, terms-of-trade
shocks may not act like technology shocks, for their observation has
to do with the direct effect of terms-of-trade shocks on measured GDP
and not with their indirect effect on quantities. The cointegration
relationship between yti, Xti, and Xtp is estimated, thus allowing theHow Important Is The Commodity Supercycle?

213

data to choose the strength of the long-run link between output andcommodity prices in each country.
Let be a 24-by-1 vector of deviations of output from trend. Then,

For each country i the country-specific shocks consist of the growthrate of the permanent component of output, D X ti, and a stationary
shock, zti. Detrended output is assumed to evolve according to thefollowing autoregressive process:(2)
where DXt = [D X t1 ... D X t24]' and z t = [z t1 ... z t24]' . Byip and Byiy , fori = 1, ..., 4, are 24-by-12 and 24-by-24 matrices of coefficients, respectively,
and CyX p, Cyz p, and CyX are matrices of order 24-by-1, 24-by-12, and 24by-24, respectively. Matrices Byiy and CyX are assumed to be diagonal.
Note that the world shocks D X tp and ztp enter directly in thedomestic block, as opposed to mediated by . This flexibility allows
for the possibility that DX tp and ztp capture global and regional shocks
affecting individual countries both directly and via world prices, suchas exogenous global and regional productivity shocks.
The exogenous shocks, DX tp, DX t, ztp, and zt follow univariate
autoregressive processes.
Specifically, let ut denote the vector of exogenous shocks

214 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
We assume that ut obeys the law of motion
ut = rut–1 + ynt,

(3)

where nt~i.i.d.N (0,I61). The matrices r and y are assumed to bediagonal. This implies that the permanent component of world prices,X tp, is uncorrelated with the stationary world shocks, ztp; that thepermanent and transitory country-specific shocks are uncorrelatedwith each other and with other country-specific shocks; and thatcountry-specific shocks, Xti and zti, are uncorrelated with the worldshocks, Xtp and ztp. The latter assumption is motivated by the fact thatthe countries in the sample are small open economies and, as such,their idiosyncratic shocks do not affect world prices. We assume thatthe correlation of output across countries stems from world shocks.
Accordingly, the matrices Byiy for i = 1,..., 4, as well as the matrix CyX,
are restricted to be diagonal. The assumption that the world shocks
Xtp and ztp (as opposed to the contemporaneous world prices ) enter
directly in the domestic block, equation (2), allows for the possibility
that world shocks affect country-level output, both directly andindirectly, mediated by world prices. A direct effect of world shocks on
country-level output could occur, for example, via productivity shocks
that are correlated across countries.

2. Observables, Priors, and Estimation Strategy
All variables in the system (1), (2), and (3), except for the interest
rate, rt, are latent variables and therefore unobservable. As a result,the system cannot be directly estimated on data. However, we will
exploit the fact that the model has precise predictions for variables
that are observable. Specifically, the data used in the estimation
includes the growth rates of the commodity prices, D pti for i = 1,...,11,
the level of the world interest rate, rt, and the growth rates of output,
D yti for i = 1,...,24. The observable variables are related to the latentvariables through the following identities:
(4)
(5)
and
(6)

How Important Is The Commodity Supercycle?

215

The observable variables are assumed to be measured with error.
Letting ot denote the vector of observed variables, we have that

(7)

where mt is a 36-by-1 vector of measurement errors distributed i.i.d.
N(0, R) and R is a diagonal matrix. We restrict the measurement
errors to explain no more than ten percent of the variance of the data.
The relationship between the observables and the latent variables,
the fact that the model is linear, and that all innovations are Gaussian,make it possible to compute the likelihood of the data, which in turnallows for the estimation of the parameters of the model. To calculate
the likelihood, it is convenient to express the model in state-spaceform. To this end, letThen the state-space representation of the model, equations (1)–(7), is given by
(8)
and
(9)
where the matrices F, P, and H are known functions of the matrices Bpip,
Byip, Byiy for i = 1,...,4, CpX p, Cpz p, CyX p, Cyz p, CyX, r and y. The modelis estimated with Bayesian techniques. Draws from the posterior
distribution are obtained by applying the Metropolis-Hastings
algorithm. We construct an MCMC chain of 2.5 million draws anddiscard the first 1.5 million.
The prior distributions of the estimated parameters are
summarized in table 1. We impose normal prior distributions to allelements of Bpip , Byiy , and Byip, for i = 1,...,4. In accordance with the216 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
Minnesota prior, we assume that, at the mean of the prior parameter
distribution, the elements offollow univariate autoregressive
processes. So, when evaluated at their prior mean, only themain diagonals of Bp1p and By1y take nonzero values, and all otherelements of Bpip and Byiy, and all elements of Byip for i = 1,...,4 are nil.
We impose an autoregressive coefficient of 0.95 in all equations so that
all elements along the main diagonal of Bp1p and By1y take a prior meanof 0.95. We assign a prior standard deviation of 0.5 to these elements,
which implies a coefficient of variation close to one half (0.5/0.95).
Also, along the lines of the Minnesota prior, we impose lower priorstandard deviations on all other estimated elements of the matrices
Bpip , Byiy , and Byip for i = 1,...,4, and set them to 0.25.
All estimated elements of the matrices CpX p, Cpz p, CyX p, Cyz p,
and CyX are assumed to have normal prior distributions with mean
zero and unit standard deviation, with one exception: the diagonal
elements of the diagonal matrix CyX, which govern the responses ofto an innovation in DXti for i = 1,...,24, are assumed
to have a prior mean of –1. This means that a shock that increases
output in country i in the long run by one percentage point, under theprior, has a zero-impact effect. This prior is motivated by a strand of thebusiness-cycle literature suggesting that the impact effect on output
of a permanent productivity shock could have either sign dependingon the strength of the wealth effect on labor supply.4
The diagonal elements of the diagonal matrix y representing thestandard deviations of the innovations in the exogenous shocks are
all assigned Gamma prior distributions with mean and standard
deviations equal to one. We impose non-negative serial correlations on
the exogenous shocks (the diagonal elements of r) and adopt Beta priordistributions for these parameters. We assume relatively small meansof 0.3 for the prior mean of the serial correlations of the nonstationary
shocks (DXtp and DXti for i = 1,...,24) and a relatively high mean of 0.7
for the prior mean of the serial correlations of the stationary shocks(ztp and zt ). The prior distributions of all serial correlations are assumed
to have a standard deviation of 0.2. The variances of all measurement
errors (the diagonal elements of the matrix R) are assumed to have auniform prior distribution with lower bound zero and upper bound often percent of the sample variance of the corresponding observableindicator. Although not explicitly discussed thus far, the estimated

4. See, for example, Galí (1999).

How Important Is The Commodity Supercycle?

217

model includes constants, which appear in the observation equation(9).5 These constants represent the unconditional means of the 36
observables. They are assumed to have normal prior distributions withmeans equal to the sample means of the observables and standard
deviations equal to their sample standard deviations divided by thesquare root of the sample length (231 quarters).
Table 1. Prior Distributions
Parameter

Distribution

Mean

Main diagonal elements of Bp1p and By1y

Normal0.95

0.5

All other estimated elements of Bp1p,
By1y , By1p

Normal00.25

Estimated elements of Bpip , Byiy , Byipi = 2,3,4

Normal00.25

Estimated elements of C pX p and C pz p

Normal01

Diagonal of C yX

Normal–1

1Elements of C yX p and C yz p

Normal01

Diagonal of r(1:25,1:25)

Beta

0.3

0.2

All other diagonal elements of r

Std. Dev.

Beta

0.7

0.2

Diagonal of ψ
α i , i = 1,…,24

Gamma
Normal10

1
1Diagonal elements of R

Uniformvar(o )
[0, 10 t ]var(o t )(10 * 2)var(o t )(10*√12)

Elements of A

Normalmean(o t )var(o t )T

Source: Authors’ calculations.
Notes: T denotes the sample length, which equals 231 quarters. The vector A denotes the mean of the vector ot
and is defined in the Appendix.

5. See the Appendix for details.

218 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
Posterior means and error bands around the impulse responses
shown in later sections are constructed from a random subsample ofthe MCMC chain of length 100 thousand with replacement.

3. The Data
The model is estimated on quarterly data on eleven worldcommodity prices, the world interest rate, and the gross domestic
product of 24 small open economies. The sample period is 1961.I to2018.IV. The eleven commodity prices included in the estimation arebeverages, food, agricultural raw materials, fertilizers, metal andminerals, gold, platinum, silver, coal, crude oil, and natural gas. The
raw data is monthly and expressed in current U.S. dollars. The sourceis the World Bank’s Commodity-Price database (the Pink Sheet)
except for coal prices, which come from Global Financial Data (GFD).
The GFD coal price is identical to the one in the Pink Sheet, exceptthat it begins in 1960.1, whereas it begins only in 1970.1 in the Pink
Sheet. Quarterly real commodity-price indices are constructed by firstdeflating the monthly nominal price indices by the monthly CPI index
of the United States, then taking a simple average of the deflated
values across the corresponding months in each quarter. The data
are normalized by dividing by each series’ 2010.IV observation. The
World Bank publishes data on the prices of 40 individual commodities.
The aggregation into eleven prices responds to the need to economize
on degrees of freedom in the estimation. The individual commodities
whose prices are aggregates are beverages, food, agricultural raw
materials, fertilizers, and metal and minerals. The aggregate priceindices are taken from the Pink Sheet. Thus the eleven commodity
prices included capture information from all 40 commodity prices in
the World Bank’s Commodity-Price database.
The quarterly time series for the world real interest rate,rt, is constructed as, where it denotes thenominal interest rate on three-month U.S. Treasury bills, andP
1 + pt+1 = P , denotes the gross growth rate of the consumer price index,
Pt, as measured by the U.S. CPI index. The expected value of the inverseof gross inflation,
is approximated by the fitted component
t+1tof an OLS regression of

onto a constant,and.

How Important Is The Commodity Supercycle?

219

Output is measured by seasonally adjusted real gross domestic
product from the quarterly national accounts of the OECD.6
For a country to be included in the sample, we require at least 50
years of quarterly observations of real output. The rationale behind
this restriction is that identifying the real effects of the commodity
supercycle requires observing the behavior of output over a relatively
long period. In addition, since commodity prices and the worldinterest rate are assumed to be exogenous to the country, we exclude
large economies. These selection criteria result in the following 24
countries: Australia, Austria, Belgium, Canada, Denmark, Finland,
France, Greece, Iceland, Ireland, Italy, Korea, Luxembourg, Mexico,
Netherlands, New Zealand, Norway, Portugal, South Africa, Spain,
Sweden, Switzerland, Turkey, and the United Kingdom. The panel
includes only four emerging economies, Korea, Mexico, South Africa,
and Turkey. Section 6 estimates the model on annual data, whichallows for the inclusion of a larger number of emerging economies.

4. The Commodity-Price Supercycle
Figure 2 displays the estimated common permanent component,
Xtp, of real commodity prices. It is constructed by Kalman smoothingat the posterior mean of the parameter estimate. The figure also
displays the eleven observed commodity prices. We interpret thevariable Xtp as the commodity supercycle. The figure suggests that
this interpretation is sensible as Xtp appears to capture well thelow-frequency comovement of the individual commodity prices.
Over the period 1960 to 2018, commodity prices display two distinct
supercycles—one peaking in 1980 and the other in 2008. The rapid
growth in Xtp between the early 1970s and 1980 coincides with the
OPEC oil-price crises. As the market power of the oil cartel weakened
in the 1980s and the supply of other countries (e.g., the United States
and those located around the North Sea) rose, the downswing of thesupercycle began. The expansionary phase of the second commodityprice supercycle begins around the time of China’s accession to the
WTO in 2001 and the peak is reached with the onset of the Global
Financial Crisis of 2008. The prediction of two commodity supercycles
6. The OECD series name is VOBARSA. For Greece and Iceland, the data appears
not to have been seasonally adjusted at the source. Therefore, these two series were
adjusted by using the X-13 ARIMA-SEATS software produced, distributed, andmaintained by the U.S. Census Bureau.

220 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
post 1960 and their dating is in line with the estimates reported in
Erten and Ocampo (2013) using an asymmetric bandpass filtering
approach on real non-oil commodity prices that picks out cycles withperiodicity between 20 and 70 years.
The permanent component of commodity prices, Xtp, plays asignificant role in explaining movements in these variables. Table 2
displays the fraction of the variance of changes in commodity prices
accounted for by changes in their permanent component. On average
across prices, D Xtp explains more than one fourth of the variance ofchanges in commodity prices. The variance shares are estimated with
precision, with standard deviations equal to two percentage points on
average. The permanent component plays the largest role in explaining
movements in crude oil prices with a variance share of 60 percent.
Estimating the price block of the model separately from the output
block (not shown) yields similar results for the time path of Xtp. Also,
this estimation approach yields a similar result for the average shareof the variance of the growth rates of world prices explained by DXtp
(21 percent when the price block is estimated separately versus 27
percent when it is estimated jointly with the output block). However,estimating the price block by using only information on prices yields
a smaller role for the permanent component, DXtp, in explaining thevariance of the growth rate of crude oil prices (35 versus 60 percent).
This finding suggests that, even though the price block is independentof the output block, data on country-level output is informative for theestimation of the parameters governing the dynamics of world prices.
Figure 2. The Commodity-Price Supercycle
2

Xtp
Crude oil

1.5
1
0.5
0
-0.5
-1
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Source: Authors’ calculations.
Notes: The permanent component of the eleven real commodity prices, Xtp, is computed by Kalman smoothing using
the posterior mean of the parameter estimates. The thin solid lines are the eleven observed real commodity prices(beverages, food, agricultural raw materials, fertilizers, metal and minerals, gold, platinum, silver, coal, crude oil,and natural gas). All-time series are constructed as cumulative demeaned growth rates.

How Important Is The Commodity Supercycle?

221

Table 2. Percent of Variance of the Growth Rate of RealCommodity Prices Explained by D Xtp
Price of

Mean

Std. Dev.

Coal

26

3

Crude Oil60

2Natural Gas

20

3

Beverages

11

1

Food

21

2

Agr. Raw Materials

20

3

Fertilizers

33

2

Metal and Minerals

30

2

Gold

30

2

Silver28

2

Platinum

19

1

Mean across prices

27

2Median of prices

26

2Real Rate

15

8

Source: Authors’ calculations.
Note: The reported figures are based on 100,000 draws from the posterior distribution of the variance decomposition.

Figure 3 presents the impulse responses of the eleven realcommodity prices and the world interest rate to a unit long-run
increase in the permanent component Xtp along with 95-percent
asymmetric confidence bands, computed by using the methodology
proposed by Sims and Zha (1999). For most commodity prices, a positive
innovation in Xtp has a positive but less than unity impact effect andinduces a slow convergence to the permanently higher level, which byconstruction is equal to one. Exceptions are crude oil, which displays
overshooting on impact and convergence from above, and natural gas,
fertilizers, gold, and silver, which display delayed overshooting.

Figure 3. Impulse Responses of World Prices to a Long-Run
Increase in Xtp of Unity
Coal

Crude oil

Natural gas

1.5

2

1

1.5

1

1

0

0.5

0.5

0.5

0

0

20

40

0

Beverages
1

0.5

0.5

0

0

20

40

0

40

Fertilizers

0

20

40

Agricultural raw
materials

Food

1

0

20

1

0.5

0
0

20

40

0

20

Metals and minerals

40

Gold

1

1.5

1
1
0.5

0.5

0.5

0

0

20

40

0

0
0

Silver

20

40

0

20

PlatinumInterest rate
0.1

1

1.5

40

0.05
1
0.5

0

0.5
0

-0.05

0

20

40
Mean

Source: Authors’ calculations.

0

0

20

40

-0.1

0

95% Sims-Zha asymmetric confidence bands

20

40

How Important Is The Commodity Supercycle?

223

Notably, an increase in the permanent component of commodity
prices has a negative effect on the world interest rate. The estimatednegative conditional comovement between commodity prices andinterest rates is important for commodity exporters with external
debt because it suggests that, when commodity prices increase, thecountry also benefits from favorable conditions in international
financial markets. Similarly, during a downturn in commodity prices,
the costs of external debt rise. This result is in line with the work of
Shousha (2016), who finds that movements in the interest rate arein part driven by variations in commodity prices. The novel aspect of
the result documented here is that the negative comovement between
commodity prices and interest rates is conditional on a permanentchange in commodity prices. The finding that interest rates fall whencommodity prices increase can also be interpreted as representing aparticular manifestation of a phenomenon that Kaminsky and others(2005) refer to as ‘When it Rains, it Pours.’

5. Importance of the Commodity Supercycle for
Economic Activity
Thus far we have documented that the permanent componentof commodity prices explains a sizeable fraction, over one fourth, of
movements in commodity prices. In other words, we have documented
that there is a significant commodity supercycle. We now wish toascertain the role of the commodity supercycle in explaining businesscycle fluctuations in individual countries.
Table 3 displays the variance decomposition of output growth
for the 24 countries in the sample. On average across countries, thepermanent component of commodity prices explains only eight percentof the overall volatility of output growth. By contrast, the transitory
components of commodity prices jointly explain 62 percent of thevariance of output growth. This result suggests that world shocks areimportant in explaining output movements in small open economies.
However the vast majority of the movements stems from stationary
world disturbances. In this sense, the role of the commodity supercycle
is modest in accounting for business cycles. The importance of thecommodity supercycle in explaining output fluctuations does notvary much across countries. The cross-sectional standard deviationof the variance share of output growth accounted for by D Xtp is only
2.4 percentage points. This means that the relatively modest role

224 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
played by the supercycle is not just valid on average but applies tomost countries in the sample.
Table 3 also speaks to a large literature assessing the role ofpermanent versus transitory shocks in accounting for aggregate
fluctuations in emerging and developed countries. 7 It shows that, inthe present sample of 24 countries, the vast majority of fluctuations in
output growth is driven by stationary shocks. Jointly the domestic andworld stationary shocks (zti and ztp) explain 80 percent of the varianceof output growth on average across countries. Noticeably this result isobtained not only for the developed countries in the sample but also
for the emerging ones (Korea, 80 percent; Mexico, 93 percent; South
Africa, 91 percent; and Turkey, 95 percent). The finding that stationary
shocks explain the lion’s share of output fluctuations in emerging
countries is in line with those reported in Garcia-Cicco and others(2010), Chang and Fernández (2013), and Singh (2020).
It is of interest to ascertain the effects of the commodity-pricesupercycle on commodity prices and aggregate activity at different time
horizons. Table 4 presents forecast error variance decompositions of
the level of commodity prices and the level of output at horizons of 5,
10, 20, and 30 years computed at the posterior mean of the parameter
estimate. The top panel of the table shows that the commodity
supercycle plays a sizeable role in explaining the level of commodity
prices at all forecasting horizons considered. The median share of Xtp in
the forecast error across the eleven commodity prices ranges from 67
percent at the five-year horizon to 93 percent at the 30-year horizon.
This suggests that the commodity supercycle affects commodity prices
not just at its own frequency of 20 years or higher but also at shorter
frequencies of five to ten years.
By contrast, the commodity supercycle appears to play a secondaryrole in explaining movements in the level of output at horizons offive and ten years, which are typically associated with business-cycle
fluctuations. The bottom panel of the table shows that the contributionof Xtp in accounting for the forecast error variance is at most 12 percent
at horizons of ten years or less. At horizons of 20 and 30 years, whichfall into the range of frequencies of the commodity supercycle itself,
the contribution of Xtp to explaining the variance of forecast errors of
output increases to 19 percent. By contrast, stationary world shocks,
the elements of the vector ztp, account for the majority of the forecast
error variance of output at all horizons considered. Their median
7. See, for example, Aguiar and Gopinath (2007).

How Important Is The Commodity Supercycle?

225

contribution ranges from 75 percent at the five-year forecastinghorizon to 58 percent at the 30-year horizon. This indicates that theeconomic impact of the commodity supercycle on output, relative tothat of stationary world shocks, is small at business-cycle frequencies(ten years or less) and moderate at its own frequency (20 years ormore).
Table 3. Variance Decomposition of Output Growth
Shock
Country

D Xtp

ztp

D Xti

zti

Australia
Austria
Belgium
CanadaDenmark
FinlandFranceGreeceIceland
Ireland
Italy
Korea, Rep.
Luxembourg
MexicoNetherlands
New Zealand
NorwayPortugal
South Africa
Spain
SwedenSwitzerland
TurkeyUnited Kingdom
Mean
Median

7
10
8
10
7
6
8
7
5
6
10
11
10
7
8
5
4
13
9
12
8
6
4
7
8
8

61
67
84
71
65
68
60
63
47
42
74
60
50
71
58
51
55
63
61
69
54
62
51
74
62
62

1
1
7
1
0
17
1
30
45
51
0
10
23
0
33
36
19
0
0
0
0
0
0
1
12
1

32
22
1
19
28
8
31
0
2
2
17
20
18
22
1
8
22
24
29
19
37
31
44
19
19
20

Source: Authors’ calculations.
Notes: The table presents the share (expressed in percent) of the total variance of output growth explained by shocks
to the permanent component of commodity prices, D Xtp, all 12 transitory commodity-price shocks taken together, ztp,
the country-specific nonstationary shock, DXti, and the country-specific stationary shock, zti. The reported numbers
are averages over 100,000 draws from the posterior distribution of the variance decomposition.

Horizon (in years)
Coal
Crude OilNatural Gas
Beverages
Food
Agr. Raw Materials
Fertilizers
Metal and Minerals
Gold
SilverPlatinumMedianInterest Rate
Australia
Austria
Belgium
CanadaDenmark
FinlandFranceGreeceIceland
Ireland

Shock

5
52
86
73
46
38
54
68
47
71
67
67
67
8
5
19
9
2
6
5
20
1
0
4

10
66
89
83
70
52
74
78
63
83
78
81
78
7
2
27
10
12
13
8
27
2
1
2

Xtp
20
77
93
90
85
76
87
87
79
90
86
90
87
6
2
33
13
33
22
11
36
5
1
1

30
82
95
93
91
85
92
91
87
93
89
93
91
7
7
36
13
41
24
10
41
5
1
2

5
48
14
27
54
62
46
32
53
29
33
33
33
92
82
74
88
92
88
60
70
95
25
26

10
34
11
17
30
48
26
22
37
17
22
19
22
93
89
70
87
85
84
59
69
96
22
18

ztp
20
23
7
10
15
24
13
13
21
10
14
10
13
94
87
65
82
64
76
50
61
92
16
11

30
18
5
7
9
15
8
9
13
7
11
7
9
93
80
63
79
56
74
42
56
91
12
7

5
0
0
0
0
0
0
0
0
0
0
0
0
0
5
1
3
3
0
34
2
3
65
69

10
0
0
0
0
0
0
0
0
0
0
0
0
0
6
1
3
2
0
32
2
2
70
79

Xti
20
0
0
0
0
0
0
0
0
0
0
0
0
0
8
1
5
3
0
39
2
2
78
88

30
0
0
0
0
0
0
0
0
0
0
0
0
0
11
1
8
3
0
48
2
3
84
91

5
0
0
0
0
0
0
0
0
0
0
0
0
0
8
6
0
3
6
1
8
0
9
1

10
0
0
0
0
0
0
0
0
0
0
0
0
0
3
2
0
1
3
0
2
0
8
1

20
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
2
0
1
0
5
0

zt
30
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
2
0
1
0
4
0

Table 4. Forecast Error Variance Decomposition of the Level of Commodity Prices and Output

5
12
1
29
1
19
1
6
24
43
7
8
8
1
17
10
6

10
14
0
35
1
27
1
4
23
42
18
36
15
7
28
15
12

Xtp
20
15
1
36
1
30
3
5
25
38
30
59
29
7
39
20
19

30
14
2
33
3
29
5
4
25
50
33
67
33
9
42
22
19

5
81
19
43
85
75
21
77
69
49
87
74
75
67
77
67
75

10
83
11
36
93
67
18
77
74
55
80
58
75
74
67
64
72

ztp
20
83
6
27
94
60
11
70
74
59
69
38
65
77
56
58
65

30
84
4
22
93
55
8
63
73
47
65
30
61
75
52
54
58

5
0
80
25
0
4
74
14
0
1
0
2
1
0
2
16
3

10
0
88
28
0
5
80
17
0
1
0
2
1
0
3
18
2

Source: Authors’ calculations.
Note: All shares are computed at the posterior mean of the estimated parameters and are expressed in percentage points.

Horizon (in years)
Italy
Korea, Rep.
Luxembourg
MexicoNetherlands
New Zealand
NorwayPortugal
South Africa
Spain
SwedenSwitzerland
TurkeyUnited Kingdom
Mean
Median

Shock

Xti
20
0
93
36
1
10
85
24
0
1
1
1
2
1
4
20
2

30
1
94
44
1
16
86
32
0
1
1
2
3
1
5
22
3

5
7
1
3
13
1
3
3
7
6
6
16
16
31
4
7
6

10
3
0
1
5
0
2
2
3
3
1
5
9
19
2
3
2

20
1
0
1
3
0
1
1
2
1
1
2
4
15
1
2
1

zt
30
1
0
1
3
0
1
1
1
1
1
1
3
15
1
2
1

Table 4. Forecast Error Variance Decomposition of the Level of Commodity Prices and Output(continued)

Figure 4. Impulse Response of Output to a Unit Long-run
Increase in Xtp
Australia

Austria

Belgium

0

0.05

0
-0.1

0

-0.1
-0.2

-0.05

-0.2
-0.3

-0.1
0

20

40

0

Canada

20

0

40

20

Denmark

40

Finland
0.1

0

0

-0.1

0

-0.1

-0.2

-0.1
-0.2

-0.2

-0.3

-0.3
0

20

40

0

France

20

40

0

20

Greece040

Iceland

0.2

0.1

0
-0.2

-0.2

0

-0.4
-0.4

0

20

40

0

Ireland

20

40

-0.05

20

0

0.05

-0.2

0

-0.05

-0.4
40
Mean

40

Korea, Rep.

-0.1
20

0

Italy

0

0

-0.1

0

20

40

0

95% Sims-Zha asymmetric confidence bands

20

40

Figure 4. Impulse Response of Output to a Unit Long-run
Increase in Xtp (continued)
Luxembourg

Mexico0Netherlands
0

0.2

-0.1

-0.1

0
-0.2

-0.2

-0.2

-0.3

-0.3
0

20

0

40

New Zealand

20

0

40

20

Norway40

Portugal

0.1
0

0.04
0.02

-0.2

0

0
-0.4

-0.02

-0.1
0

20

40

0

20

South Africa

0.1

0

20

0

-0.2

-0.1

-0.4

-0.2

0

Switzerland

20

40

Turkey

-0.1

40
Mean

Source: Authors’ calculations.

20

40

United Kingdom
0

0

-0.05
-0.1
-0.15

-0.2
20

0

0.1

-0.1

-0.2

40

Sweden

0

40

0

0

20

Spain

0.2

0

0

40

0

20

40

0

95% Sims-Zha asymmetric confidence bands

20

40

230 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
The fact that the world and domestic stationary shocks, ztp and zti,
jointly explain the majority of the forecast error variance of output
even at horizons of 20 and 30 years, 65 and 60 percent on average,
respectively, indicates that the world and domestic nonstationary
components, Xtp and Xti, are not the dominant drivers of movements
in output.
Figure 4 displays the impulse responses of the level of output,
yt, in each of the 24 countries, to a permanent world shock, Xtp, that
increases commodity prices in the long run by one percent. In mostcountries, the permanent commodity-price increase is contractionary.
One possible explanation for this finding is that the sample includes
mostly developed open economies that are not important primarycommodity producers. As we will see in section 6, in emerging countries
the output response to an increase in the permanent component ofworld prices is in general positive. But even for primary commodity
producers, an increase in Xtp could have an ambiguous effect on output
for at least two reasons. One is that when Xtp goes up, all commodity
prices go up. To the extent that some commodities are imported andused as intermediate inputs in domestic production, an increase in Xtp
would result in an increase in marginal costs, which in turn, may lower
domestic employment. The second reason is that because Xtp represents
a permanent increase in real commodity prices, it might entail a large
positive wealth effect for the commodity-producing country. In turn,this positive wealth effect could lead to a contraction in labor supply
and in this way lower equilibrium employment.

6. Emerging Countries
As mentioned earlier, long quarterly time series for output areavailable mostly for developed countries. As a result, emerging
countries are underrepresented in the sample. To shed light on theimportance of the commodity supercycle in emerging countries,
this section turns to an analysis based on annual data for which
the coverage of this group of countries is more comprehensive. The
empirical model is the one described in section 1 except for the numberof lags and the number of commodity prices included. Because the data
is annual, the model includes only one lag of prices and output, and. To economize on the number of parameters estimated, the elevencommodity prices are aggregated into three indices—energy, non-How Important Is The Commodity Supercycle?

231

energy, and precious metals—, following the Pink Sheet aggregation
scheme. Energy commodities include coal, crude oil, and naturalgas. Non-energy commodities include beverages, food, agricultural
raw materials, fertilizers, and metals and minerals. And precious
metals comprise gold, silver, and platinum. The sample includes 24
emerging countries and 17 developed countries, which are listed intable 6. The output data comes from World Development Indicators.
The selection of countries follows a number of criteria which include
data availability since 1960, a population of more than three million
in 2018, not having transitioned from a planned to a market economy,
and having a common secondary data source for output. As in theanalysis using quarterly data, the model is estimated using Bayesian
techniques. The prior distributions for the model parameters are thesame as those presented in table 1.
Figure 5 plots with thin lines the three real commodity-priceindices and with a thick line their estimated permanent component,
Xtp. As in the case of the estimation on quarterly data, the commodity
supercycle is a smooth stochastic trend of the three prices and displays
two peaks since 1960—one in 1980 and the other in 2012. The peaks
and troughs of the estimated commodity-price supercycle line up withthe ones identified using quarterly data on the more disaggregatedcommodity prices plotted in figure 2.
Figure 5. The Commodity-Price Supercycle in Annual Data
1.2
1
0.8

Xtp
EnergyNon Energy
Precious Metals

0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Source: Authors’ calculations.
Notes: The permanent component of the three aggregate commodity-price indices, Xtp, is computed by Kalmansmoothing using the posterior mean of the parameter estimates. All time series are constructed as cumulative
demeaned growth rates.

232 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
Table 5. Percent of Variance of the Growth Rate of Annual
World Prices Explained by D Xtp
Price of

Mean

Std. Dev.

Energy Commodities

981

Non-Energy Commodities

94

2

Precious Metals

94

1

Mean

95

1

Median94

1

Real Rate

25

18

Source: Authors’ calculations.
Note: The reported figures are based on 100,000 draws from the posterior distribution of the variance decomposition.

Table 5 shows that the permanent component, Xtp, explains morethan 90 percent of the variation in the growth rate of the threecommodity indices. Thus, as in the quarterly estimation, the commodity
supercycle is an important driver of commodity prices. A differenceis that now the share of the variance of the growth rate of prices
explained by the permanent component is much larger than the one
estimated in quarterly data. This is to some extent expected sinceaggregation across time and commodities tends to average away theeffects of commodity-specific and transitory disturbances. The tablealso shows that the commodity supercycle explains 25 percent of
movements in the world interest rate, a share somewhat higher thanthe one obtained in the estimation on quarterly data (15 percent).
Table 6 displays the variance decomposition of output growth in
the 24 emerging and 17 developed countries considered. As in the caseof the analysis using quarterly data on a sample of mostly developed
economies, all world shocks taken together, Xtp and ztp, play a major rolein explaining the variance of output growth. It also continues to be thecase that, of the contribution of world shocks to output fluctuations,the majority is attributed to stationary disturbances, ztp.
This pattern applies to a large extent when one limits attention
to emerging countries. Within this group, on average world shocks

How Important Is The Commodity Supercycle?

233

explain more than 50 percent of the variance of output and, of thisfraction, almost two thirds are attributable to stationary world shocks.8
The fact that stationary world shocks, ztp, explain a much larger
share of the variance of output growth than of the variance of thegrowth rate of prices indicates that these world shocks may be only
partially mediated through commodity prices. An example of a worldshock that could have an output effect both directly and throughworld commodity prices are productivity shocks that are correlated
across countries.
Table 6 also speaks to the literature on the role of stationary
and nonstationary shocks in explaining business cycles in emerging
countries. The posterior mean joint contribution of stationary shocks,
ztp and zti, to the variance of output growth is 57 percent with theremaining 43 percent explained by nonstationary shocks, Xtpand Xti.
This result suggests that the majority of fluctuations in aggregate
activity in the emerging countries considered stems from stationary
domestic and world disturbances.
The preponderance of stationary world shocks in accounting formovements in output in emerging economies also manifests itself
at different forecasting horizons. Table 7 displays the forecast error
variance decomposition of the level of output at horizons 5, 10, 20,
and 30 years. At forecasting horizons of five and ten years, which are
typically associated with business-cycle frequencies, the mean shareof variance explained by the stationary world shocks, ztp, is 40 and
38 percent, respectively, compared to 19 and 24 percent explained by
the nonstationary world shock, Xtp. At longer forecasting horizons of
20 and 30 years, the role of nonstationary world shocks increases, as
expected, but does not clearly dominate that of stationary world shocks.
Specifically, the variance of the forecasting error of output explainedby Xtp has a mean of 30 and 34 percent at horizons 20 and 30 years,
compared to 34 and 31 percent for stationary world shocks.

8. The results are robust to estimating the model by maximum likelihood, whichindicates that the findings are not due to the choice of priors. The maximum likelihood
estimate assigns more importance (about 10 percentage points) to world shocks in
explaining the variance of output growth. For emerging countries, of this, about one
third is accounted for by the supercycle and two thirds by stationary world shocks. For
developed countries, the relative importance of the supercycle is somewhat larger, with
innovations to ∆ Xtp explaining about forty percent of the variance of output growthaccounted for by world shocks.

Table 6. Variance Decomposition of Output Growth —Annual Data
Shock
Country

D Xtp

ztp

Mean Emerging
Mean Developed
Argentina
Bangladesh
Bolivia
BrazilChile
Colombia
Costa Rica
Dominican Republic
EcuadorGuatemalaIndia
Indonesia
Korea, Rep.
Malaysia
MexicoPakistan
PanamaParaguay
Peru
Philippines
South Africa
ThailandTurkeyUruguay
Australia
Austria
Belgium
CanadaDenmark
FinlandFranceGreeceIceland
Italy
Luxembourg
Netherlands
NorwayPortugal
Spain
SwedenUnited Kingdom

18
19
16
8
26
21
10
26
25
8
34
20
10
15
19
24
12
8
14
30
18
20
35
13
4
18
6
25
21
16
21
17
18
22
8
25
28
18
10
19
23
14
30

32
48
8
17
55
33
18
28
42
8
32
78
24
50
55
54
40
35
16
19
19
16
27
60
16
21
27
51
64
43
54
60
77
47
17
58
22
50
39
58
53
63
33

D Xti
24
13
74
73
0
0
0
2
28
0
33
1
63
32
26
0
47
57
0
0
0
60
1
6
79
0
64
1
1
1
2
19
2
0
0
0
50
0
3
22
0
21
34

zti
25
20
1
1
19
45
71
44
4
84
1
1
2
2
1
21
1
1
70
50
63
2
36
20
0
61
4
22
13
40
21
3
4
30
75
17
1
32
49
1
24
2
2

Source: Authors’ calculations.
Notes: The table presents the share (expressed in percent) of the total variance of output growth explained by shocks
to the permanent component of commodity prices, D Xtp, all stationary world price shocks taken together, ztp, thecountry-specific nonstationary shock, DXti, and the countryspecific stationary shock, zti. The reported numbers areaverages over 100,000 draws from the posterior distribution of the variance decomposition.

How Important Is The Commodity Supercycle?

235

Figures 6 and 7 display the impulse response of output in the 17
developed and 24 emerging economies, respectively, to a shock in Xtp
that increases energy, non-energy, and precious metal prices in thelong run by one percent. The figures also include 95-percent confidence
bands. In line with the results obtained in section 5 using quarterly
data (figure 4), in developed economies, a permanent increase in worldcommodity prices is contractionary for most countries. By contrast, formost emerging countries a permanent increase in commodity prices
is expansionary. As pointed out in section 5, a possible explanation forthis difference could be that in emerging countries the production ofprimary commodities represents a larger share of total output thanit does in developed countries.

Horizon (in years)
Argentina
Bangladesh
Bolivia
BrazilChile
Colombia
Costa Rica
Dominican Republic
EcuadorGuatemalaIndia
Indonesia
Korea, Rep.
Malaysia
MexicoPakistan
PanamaParaguay
Peru
Philippines
South Africa
ThailandTurkey

Shock

5
45
15
1
23
5
34
11
20
59
17
9
21
9
16
19
0
13
37
24
19
45
1
4

10
33
12
6
28
5
42
10
33
67
13
10
32
7
18
22
2
26
54
38
19
54
1
3

Xtp
20
20
9
48
31
11
58
38
43
66
10
16
44
5
31
24
16
36
65
38
12
59
6
4

30
14
9
65
32
20
69
60
49
63
11
17
46
9
42
24
28
36
68
35
10
60
14
6

5
10
26
63
39
15
29
68
15
34
82
54
66
77
50
70
71
14
18
13
5
20
66
35

10
6
41
69
51
16
30
61
19
26
86
64
57
54
43
64
67
14
21
12
5
18
63
22

ztp
20
4
38
41
57
15
25
35
19
23
86
64
39
49
36
57
48
13
22
20
10
17
67
12

30
3
33
30
58
14
18
18
17
21
84
61
32
45
32
53
37
14
22
27
14
16
65
8

5
45
55
0
0
0
1
17
0
7
1
32
12
12
0
10
27
0
0
0
74
1
1
61

10
61
45
1
0
1
2
26
0
7
1
24
10
38
1
14
29
1
0
0
75
1
3
75

Xti
20
76
52
1
1
3
2
26
0
11
3
20
16
45
2
19
35
1
0
1
77
2
7
84

30
83
57
1
1
4
2
21
1
16
5
21
21
46
2
23
35
2
0
1
75
4
8
86

5
1
4
36
38
80
37
3
65
1
1
5
2
3
33
1
2
73
45
63
2
35
32
1

10
0
2
25
21
78
26
2
48
1
1
2
1
1
38
0
2
59
25
50
1
27
32
0

Table 7. Forecast Error Variance Decomposition of the Level of Output — Annual Data

20
0
1
10
12
71
15
1
38
0
0
1
1
0
31
0
1
50
13
42
1
22
20
0

zt
30
0
1
5
9
63
10
0
34
0
0
1
0
0
24
0
0
47
10
37
1
20
13
0

5
22
19
0
4
9
15
13
7
2
21
8
5
28
3
1
4
6
4
29
9

10
31
24
0
2
7
15
12
4
1
30
14
5
37
5
6
3
15
3
36
12

Xtp
20
33
30
0
1
9
15
8
3
5
38
26
18
33
6
17
2
21
6
37
14

30
30
34
0
2
13
14
7
3
13
41
35
29
27
6
24
3
22
10
35
17

5
13
40
69
86
86
50
68
86
94
55
27
84
47
72
48
91
69
89
52
69

10
12
38
62
93
90
59
75
87
96
59
41
90
32
81
66
92
72
88
39
72

ztp
20
18
34
46
95
88
64
81
85
92
57
46
80
22
85
69
92
72
77
31
70

30
25
31
36
94
83
65
82
81
84
55
43
70
18
86
65
90
72
66
26
66

5
0
15
24
1
1
1
2
3
1
0
0
0
23
0
3
4
0
5
17
5

10
1
17
34
1
1
1
3
6
2
0
0
0
31
1
4
4
0
8
24
7

Source: Authors’ calculations.
Note: All shares are computed at the posterior mean of the estimated parameters and are expressed in percentage points.

Horizon (in years)
Uruguay
Mean-Emerging
Australia
Austria
Belgium
CanadaDenmark
FinlandFranceGreeceIceland
Italy
Luxembourg
Netherlands
NorwayPortugal
Spain
SwedenUnited Kingdom
Mean-Developed

Shock

Xti
20
1
20
52
1
2
2
5
10
2
0
1
0
45
1
4
5
1
16
32
11

30
2
21
62
2
3
3
6
14
2
0
1
0
55
1
4
7
1
23
39
13

5
64
26
7
9
4
35
16
4
3
24
65
11
1
24
48
1
25
2
2
17

10
56
21
4
4
2
25
9
3
2
11
44
4
1
13
23
1
12
2
1
9

Table 7. Forecast Error Variance Decomposition of the Level of Output — Annual Data(continued)

20
48
16
2
2
1
19
6
2
1
5
28
2
0
8
10
0
7
1
0
5

zt
30
43
13
1
2
1
17
5
2
1
4
21
1
0
7
7
0
6
1
0
4

Figure 6. Impulse Responses of Output to a Unit Long-Run
Increase in Xtp — Developed Economies
Australia

Austria

Belgium

0.05

0.1

0.02
0
0

0.05

-0.05
-0.1

-0.02
0

20

0

40

Canada

20

40

0

0

20

Denmark

40

Finland

0.15
0.06
0.04

-0.05

0.05

0.02
0

-0.1

0
-0.05

0.08

0

0.1

-0.02
-0.04

-0.15
0

20

0

40

France

20

40

0

20

Greece40

Iceland

0

0.2

0.3
-0.5

0.2

0.1

0.1

-1
0
0

20

0

40

0

Italy

20

40

0

Luxembourg
0
-0.05

-0.2

0.2

40

Netherlands

0

0.4

20

-0.1
0

-0.4
0

20

40
Mean

0

20

40

0

95% Sims-Zha asymmetric confidence bands

20

40

Figure 6. Impulse Responses of Output to a Unit Long-Run
Increase in Xtp — Developed Economies (continued)
NorwayPortugal

Spain
0.2

0.1

0

0.2

-0.2

0

-0.4

0.1

-0.6

-0.1
0

-0.8
0

20

40

0

Sweden20

400United Kingdom
0

0.05
0

-0.1

-0.05
-0.1

-0.2

-0.15
0

20

40
Mean

Source: Authors’ calculations.

0

20

40

95% Sims-Zha asymmetric confidence bands

20

40

Figure 7. Impulse Response of Output to a Unit Long-run
Increase in Xtp : Emerging Economies
Argentina

Bangladesh

Bolivia

0.4

0

0.2

-0.2
0.2

-0.4

0.1

-0.6
0

0

0

20

0

40

20

Brazil

40

-0.8
0

Chile

20

40

Colombia

0.3

0.3

0.2

0.2

0.1

0.1

0.4

0.2

0

0

0
0

20

40

Costa Rica

0

20

0

40

Dominican Republic

20

40

Ecuador

0.3
0

0.6
0.2

0.4

-0.2

0.2

0.1
-0.4

0
0

20

40

0
0

20

Guatemala

0

40

India

0.1

0.8
0.6

-0.1

0.05

0.4

-0.2

0

40

Indonesia

0

0.15

20

0.2

-0.05

-0.3
0

20

40
Mean

0
0

20

40

0

95% Sims-Zha asymmetric confidence bands

20

40

Figure 7. Impulse Response of Output to a Unit Long-run
Increase in Xtp : Emerging Economies (continued)
Korea, Rep.

Malaysia

0.4

0.8

0.2

0.6

Mexico
0.4

0.4

0.2

0
0.2
-0.2
0

20

40

0

0

20

Pakistan40

00Panama20

40

Paraguay

0.3
0.8

0.3
0.2

0.6

0.2

0.4

0.1

0.1

0.2

0

0

0

20

0

40

20

Peru

40

0

0

Philippines

20

40

South Africa

0.6
0.4

0.2

0.4

0

0.2

0.2
0
-0.2

0

-0.2
0

20

0

40

Thailand

20

40

0

Turkey0.4

40Uruguay

0.2

0.2

20

0.3
0.2

0.1

0.1
0
0

0
0

20

40
Mean

Source: Authors’ calculations.

0

20

40

0

95% Sims-Zha asymmetric confidence bands

20

40

242 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe

7. Conclusion
This paper aims to fill a gap in the literature on the transmissionof world shocks through commodity prices in open economies. An
existing literature has documented the presence of a commodityprice supercycle. An empirical technique employed in many of thesestudies is based on spectral analysis and identifies one supercycle percommodity price. The resulting supercycles are positively correlated
across commodities suggesting a common driver.
The first contribution of the present paper is to propose analternative definition of the commodity-price supercycle consistingin representing it as the common stochastic trend in all commodity
prices. The so-identified supercycle turns out to share a number ofkey characteristics with the ones obtained using spectral analysis. An
advantage of the common permanent component approach is that itlends itself to a joint estimation of the contributions of domestic andforeign transitory and permanent shocks to aggregate fluctuationsin open economies.
The results of the paper suggest that world shocks are responsible
for more than half of observed variations in aggregate activity indeveloped and emerging economies. However, more than two thirdsof the contribution of world shocks is due to temporary disturbances,leaving less than one third to the permanent world shock that drives
the commodity supercycle. This result is obtained both unconditionallyand conditional on forecasting horizons. Importantly, even at horizonsof 20 and 30 years, which are typically associated with the periodicityof the commodity-price supercycle, the permanent world shock does notclearly dominate temporary world shocks in accounting for variations
in aggregate activity.
Taken together, these findings indicate that the permanent worldshock that drives the commodity-price supercycle does matter but does
not play the central role in shaping short- or medium-run businesscycle fluctuations.

How Important Is The Commodity Supercycle?

243

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How Important Is The Commodity Supercycle?

245

Appendix
In section 1, the presentation of the model omitted constant terms
to facilitate the exposition. This Appendix presents the model including
those omitted constant terms. As we will see, this will introduce a
vector of constants, denoted A, into the observation equation (9). We
will also derive expressions for the matrices F, P, and H of the statespace representation of the model, equations (8) and (9).
Redefine the vectors , , and ut as deviations from their respectivemeans:

andThe evolution of the vector

is
(10)

246 Andrés Fernández, Stephanie Schmitt-Grohé, and Martín Uribe
The evolution of the vector

is

(11)

The evolution of the exogenous shocks, ut, is
(12)
Let

as:

The system of equations (10), (11), and (12) can then be expressed

(13)

where

and

The vector xt evolves over time asHow Important Is The Commodity Supercycle?

247

where
and
Given the redefinition of(6) become, the observation equations (4), (5), andandIn vector form, the observation equations can be expressed as

where

with