[PPT] - Variable Long-Term Trends in 100+ Mineral Prices John T. Cuddington PowerPoint Presentation

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Variable Long-Term Trends in 100+ Mineral Prices

John T. Cuddington

William J. Coulter Professor of Mineral Economics Colorado School of Mines

August 16-17, 2012 Rio de Janeiro, Brazil Conference on “The Economics and Econometrics of Commodity Prices” sponsored by the Getulio Vargas Foundation and VALE

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My Home: Colorado School of Mines

Division of Economics and Business www.econbus.mines.edu

CSM is the oldest university in the CO state system

(1874-)

CSM is a small, elite university focusing on engineering

and applied science

CSM’s Division of Economics and Business Programs
BS - Economics
MS – Engineering and Technology Mgt (ETM)
MS, PhD – Mineral and Energy Economics
I’d show you pictures, but we all can’t live there!

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SLIDE 3

Long-Run Trends in Mineral Prices: Overview

Motivation: policy, theory, empirics
Objective: to explore the use of band-pass

filters for extracting LR trends

Empirical results for some long-span data
Conclusions
Extensions: Super Cycles (20-70 years)

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SLIDE 4

Motivation - Policy

Policy makers – keen interest during periods
f sharply rising resource prices, perceived

‘shortages’ or geo-political threats to availability

Will we run out of various nonrenewable

resources? (Limit to Growth debate)

Will they be exhausted before they become

economically obsolete, or vice versa?

Real prices are a key measure of economic

scarcity; long-span mineral price data is readily available

4

SLIDE 5

Tilton (2003) RFF Book: On Borrowed Time? Assessing the Threat

f Mineral Depletion
“Mining and the consumption of nonrenewable mineral

resources date back to the Bronze Age, indeed even the Stone Age…(p.1)

“What is new is the pace of exploitation. Humankind

has consumed more aluminum, copper, iron and steel, phosphate rock, diamonds, sulfur, coal, oil, natural gas, and even sand and gravel during the past century than all earlier centuries together. (p.1)

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SLIDE 6

Causes of Explosion in Mineral Use

Advances in technology allow [exploration and] extraction…at lower and

lower cost. [Shifts mineral supply curves down]

Advances in technology also permit new and better mineral commodities

serving a range of needs.[Shifts mineral demand curves out/up]

Rapidly rising living standards in many parts of the globe are increasing

demand across the board for goods and services, including many that use mineral commodities intensively in their production [Shifts the derived demand for minerals out/up]

Surge in world population means more and more people with needs to
satisfy. [Shift the derived demand for mineral in or out depending on the

relative mineral intensity of various goods. Source: (Tilton 2003, p.1)

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SLIDE 7

Hotelling Theory of Nonrenewable Resources

Hotelling’s (1931) ‘benchmark’ theory of nonrenewable

resources

Shadow price of resource stock (in the ground) = price

– marginal extraction and production cost

Hotelling model implies the r percent rule: shadow

price should rise at a rate equal to the interest rate

Hotelling also predicted that resource consumption

would decline monotonically over time.

The competitive market outcome was Pareto efficient:

Don’t worry everything will work out fine!

7

SLIDE 8

Extensions of the Hotelling Model: Getting the theory to match the fact!

See Gaudet (2007) and Slade and Thille (2009) for recent

discussions

Declining resource quality (Ore grade, accessibility)
Exploration for additional reserves
Recycling – in effect, adds to reserves
Technological advances that impact demand or supply of

nonrenewables

Theoretical models developed by Pindyck (1978), Heal (1981),

and Slade (1982) predict a U-shaped time pattern for prices with technological advance initially dominating, but ultimately being overpowered by depletion.

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SLIDE 9

Empirical Evidence on Long-Term Price Trends

The ‘game’ is to get the longest data span possible and apply the most

robust univariate time series techniques. For some nonrenewables, data go back to the mid 1800s

Much of the literature focuses on estimating either TS or DS

specifications in order to estimate the constant long-term trend (albeit it with the possible search for occasional breaks).

TS Model
DS Model

9

lnP

t = a + bt +et

D(lnP

t) = b +ut

SLIDE 10

U-Shaped Price Paths

Margaret Slade (1982 JEEM) fit (deterministic) linear and

quadratic trend models for eleven nonrenewables from 1870 through 1978 [Aluminum, Copper, Iron, Lead, Nickel, Silver, Tin, Zinc, Coal, Natural Gas, Petroleum].

Quadratic trend model is flexible enough to allow for up

to one change in direction of the time trend line, including the U-shape behavior

Concerns:
Linear and (presumably) quadratic trend model are subject

to spurious regression issues in the presence of unit roots.

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SLIDE 11

Overall conclusions from review

f empirical work
Conclusions on the significance of the time

trend depend critically on presence/absence

f unit roots and/or breaks
Any trend is small and difficult to estimate

precisely, given the huge year-to-year volatility in the price series.

11

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Continued…

Tilton (2003, p. 54) summarizes his survey of literature on long-term price

trends this way:

“History also strongly suggests that the long-run trends in mineral

prices…are not fixed. Rather they shift from time to time in response to changes in the pace at which new technology is introduced, in the rate of world economic growth, and in the other underlying determinants of mineral supply and demand.

“This not only complicates the task of identifying the long-run trends that

have prevailed in the past, but cautions against using those trends to predict the future. Because the trends have changed in the past, they presumably can do so as well in the future.”

Empirics should allow for variable trends – that is, the gradual evolution in

LT trends without constraining the trends to be constant (or u-shaped) over time.

Band-pass filters provide one way of doing this if our objective is data

description and historical analysis, rather than hypothesis testing.

12

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Our departure point: Variable Long-run Trends

Nonrenewable prices in the long run

will reflect the tug-of-war between exploration, depletion and technological change.

There is no reason to expect that

balance among these forces should remain constant over the longest available data span.

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Band-Pass Filters

“When confronting data, empirical economists must somehow isolate features of

interest and eliminate elements that are a nuisance from the point of view of the theoretical models they are studying. Data filters are sometimes used to do that.” (Cogley, 2008, p. 68)

Explaining how data filters work, Cogley (2008, p.70) notes: “The starting point is the

Cramer representation theorem,… which provides a basis for decomposing xt and its variance by frequency. It is perfectly sensible to speak of long- and short-run variation by identifying the long run with low-frequency components and the short run with high-frequency oscillations.”

“Many economists are more comfortable working in the time domain, and for

purposes it is helpful to express the cyclical component as a two-sided moving average [with infinitely many leads and lags].” (Cogley, 2008, p.71)

Although the ‘ideal’ filters have infinitely many leads and lags, actual filters

necessarily involve lead/lag truncation. There are different methods for doing this (e.g., Baxter-King, Christiano-Fitzgerald)

Actual filters may be symmetric (centered) or asymmetric (uncentered).
Symmetric – no phase shift
Asymmetric - allow the filtered series to be calculated all the way to the ends of the data

set

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Applications

Band-pass (BP) filters allows us to:
Extract cyclical components within a specified range of periods

(or frequencies) from an economic time series.

Decompose any time series into a set of mutually exclusive and

completely exhaustive cyclical components that sum to the series itself.

Note: The highest-frequency (or shortest period) cycle that can be

identified equals 2 times the data frequency

Initial application: Baxter and King define ‘business cycle

fluctuations’ as lying in a ‘period window’ between 6 and 32 months.

Comin-Gertler (2006) Medium-Term Macroeconomic Cycles
Cuddington and coauthors: super cycles in mineral prices

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Our Definition of the ‘Long Run’

16

P

t º P t(2,70)+ P t(70,¥)

P

t(2,70) = 'aggregate'cyclical component

P

t(70,¥) = long- termtrend component

SLIDE 17

Preliminary Look at The Economist Industrials Commodity Index

3.0 3.5 4.0 4.5 5.0 1875 1900 1925 1950 1975 2000

Economist Commodity Price Index US dollar terms, in logs

.4
.2

.0 .2 .4 .6 1875 1900 1925 1950 1975 2000

Economist Commodity Price Index US dollar terms, log-difference

.8
.4

.0 .4 .8 1875 1900 1925 1950 1975 2000

Economist Commodity Price Index US dollar terms, second log-difference

Apparent downward trend after

early 1920s

Annual percentage changes range

from -40% to +40%

Increase in volatility after early

1920s

Average annual growth rate is not

statistically different from zero

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SLIDE 18

30-Year Moving Average: Centered vs. Trailing

18

3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00 70 80 90 00 10 20 30 40 50 60 70 80 90 00 10

Economist Commodity Price Index (US dollar terms, in logs) Centered 30-Year Moving Average Trailing 30-Year Moving Average -- Note severe phase shift!

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Economist Industrial Commodity Index (EICI): Annual Growth Rates

19

.4
.3
.2
.1

.0 .1 .2 .3 .4 .5 70 80 90 00 10 20 30 40 50 60 70 80 90 00 10

Economist Commodity Price Index (US dollar terms, log-difference) Centered 30-Year Moving Average of Annual Growth Rates

SLIDE 20

EICI:

20

.03
.02
.01

.00 .01 .02 70 80 90 00 10 20 30 40 50 60 70 80 90 00 10

Centered 30-year Moving Average Growth Rate

SLIDE 21

ACF-Band-Pass Filter Results

n Long-run

Trend

Long-run Trend in EICI is

negative until mid-1980s, then turns upward

One one change in direction
Not the classic U-shape that

Pindyck-Heal-Slade would predict

Remember: EICI contains

both renewable and nonrenewable resources

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3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00 70 80 90 00 10 20 30 40 50 60 70 80 90 00 10

Economist Commodity Price Index (US dollar terms, in logs) Trend Component = ACF-BP(>70)

1 2 70 80 90 00 10 20 30 40 50 60 70 80 90 00 10

RP_NC_DUM

SLIDE 22

Long-run Trends in LME6:

Aluminum, Copper Nickel, Lead Tin, Zinc

Wide variety of price

paths

Some have more than
ne change in

direction

Can we tell metal

specific stories about the roles of exploration/discovery, depletion, and technological change?

22

7.0 7.5 8.0 8.5 9.0 9.5 10.0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Aluminum (Natural Logs) AL_L_2_70_NC 7.2 7.6 8.0 8.4 8.8 9.2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Copper (Natural Logs) CU_L_2_70_NC 8.4 8.8 9.2 9.6 10.0 10.4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Nickel (Natural Logs) NI_L_2_70_NC 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Lead (Natural Logs) PB_L_2_70_NC 8.4 8.8 9.2 9.6 10.0 10.4 10.8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Tin (Natural Logs) SN_L_2_70_NC 6.4 6.8 7.2 7.6 8.0 8.4 8.8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Zinc (Natural Logs) ZN_L_2_70_NC

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Long-Run Variable Trend Rates for LME6

23

.04
.03
.02
.01

.00 .01 .02 00 10 20 30 40 50 60 70 80 90 00 10 AL_NC_D CU_NC_D NI_NC_D PB_NC_D SN_NC_D ZN_NC_D

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Variable Trend RATES in the USGS 101 Minerals

Hmmm? What am I supposed to learn from this?

24

.12
.08
.04

.00 .04 .08 .12 00 10 20 30 40 50 60 70 80 90 00 10 ABM_NC_D ABN_NC_D ABNSS_NC_D AG_NC_D AL_NC_D ALOX_NC_D ALUM_NC_D AS_NC_D ASB_NC_D AU_NC_D B_NC_D BALL_NC_D BARITE_NC_D BAUXI_NC_D BE_NC_D BENT_NC_D BI_NC_D BR_NC_D CD_NC_D CEM_NC_D CLAY_NC_D CO_NC_D CR_NC_D CS_NC_D CU_NC_D DIAM_NC_D DIATO_NC_D FCLAY_NC_D FELDS_NC_D FEORE_NC_D FEPIG_NC_D FESCR_NC_D FESLA_NC_D FESTE_NC_D FLUOR_NC_D FULE_NC_D GA_NC_D GAR_NC_D GE_NC_D GEM_NC_D GRAPH_NC_D GYP_NC_D HE_NC_D HF_NC_D HG_NC_D I_NC_D IN_NC_D KAO_NC_D KYAN_NC_D LI_NC_D LIME_NC_D MGCOM_NC_D MGMTL_NC_D MICAS_NC_D MICASP_NC_D MN_NC_D MO_NC_D MSCLAY_NC_D MTLAB_NC_D N_NC_D NAS_NC_D NB_NC_D NI_NC_D PB_NC_D PEAT_NC_D PGM_NC_D PHS_NC_D POT_NC_D PRL_NC_D PUM_NC_D QTZ_NC_D RAREARTH_NC_D RE_NC_D S_NC_D SALT_NC_D SB_NC_D SCAB_NC_D SDASH_NC_D SE_NC_D SI_NC_D SN_NC_D SNDGRC_NC_D SNDGRI_NC_D SR_NC_D STEEL_NC_D STNC_NC_D STND_NC_D TA_NC_D TALC_NC_D TE_NC_D TH_NC_D TI_NC_D TISCP_NC_D TL_NC_D TRIP_NC_D V_NC_D VRM_NC_D W_NC_D WLA_NC_D ZN_NC_D ZR_NC_D

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25

5 . 6 6 . 0 6 . 4 6 . 8 7 . 2 7 . 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Abr asi ves ( m anuf act ur ed) ( i n l

gs)

ABM _ L _2 _ 7 0 _ NC 4 . 4 4 . 6 4 . 8 5 . 0 5 . 2 5 . 4 5 . 6 5 . 8 6 . 0 6 . 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Abr asi ves ( nat ur al ) ( i n l

gs)

ABN_ L _ 2_ 7 0 _ NC 4 . 0 4 . 5 5 . 0 5 . 5 6 . 0 6 . 5 7 . 0 7 . 5 8 . 0 8 . 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Abr asi ve Speci al Si l i ca ( i n l

gs)

ABN SS_L_2_70_N C 1 1. 0 1 1. 5 1 2. 0 1 2. 5 1 3. 0 1 3. 5 1 4. 0 1 4. 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Si l ver ( i n l

gs)

AG _L_2_70_N C 7 . 0 7 . 5 8 . 0 8 . 5 9 . 0 9 . 5 1 0. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Al um i num ( i n l

gs)

AL_L_2_70_N C 4 . 5 5 . 0 5 . 5 6 . 0 6 . 5 7 . 0 7 . 5 8 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Al um i num O xi de ( i n l

gs)

ALO X_L_2_70_N C 5 . 0 5 . 2 5 . 4 5 . 6 5 . 8 6 . 0 6 . 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Al um i na ( i n l

gs)

ALU M _L_2_70_N C 5 . 5 6 . 0 6 . 5 7 . 0 7 . 5 8 . 0 8 . 5 9 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ar seni c ( i n l

gs)

AS_L_2_70_N C 5 . 0 5 . 5 6 . 0 6 . 5 7 . 0 7 . 5 8 . 0 8 . 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Asbest os ( i n l

gs)

ASB_L_2_70_N C 1 5. 2 1 5. 6 1 6. 0 1 6. 4 1 6. 8 1 7. 2 1 7. 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 G ol d ( i n l

gs)

AU _L_2_70_N C 5 . 6 6 . 0 6 . 4 6 . 8 7 . 2 7 . 6 8 . 0 8 . 4 8 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Bor on ( i n l

gs)

B_L_2_70_N C 3 . 2 3 . 6 4 . 0 4 . 4 4 . 8 5 . 2 5 . 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cl ay- Bal l cl ay ( i n l

gs)

BALL_L_2_70_N C 3 . 2 3 . 6 4 . 0 4 . 4 4 . 8 5 . 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Bar i t e ( i n l

gs)

BAR I TE_L_2_70_N C 2 . 8 3 . 2 3 . 6 4 . 0 4 . 4 4 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Bauxi t e ( i n l

gs)

BAU XI _L_2_70_N C 1 1. 5 1 2. 0 1 2. 5 1 3. 0 1 3. 5 1 4. 0 1 4. 5 1 5. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ber yl l i um ( i n l

gs)

BE_L_2_70_N C 2 . 0 2 . 5 3 . 0 3 . 5 4 . 0 4 . 5 5 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cl ay- Bent oni t e ( i n l

gs)

BEN T_L_2_70_N C 8 . 5 9 . 0 9 . 5 1 0. 0 1 0. 5 1 1. 0 1 1. 5 1 2. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Bi sm ut h ( i n l

gs)

BI _L_2_70_NC 6 7 8 9 1 0 1 1 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Br om i ne ( i n l

gs)

BR _L_2_70_N C 5 6 7 8 9 1 0 1 1 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cadm i um ( i n l

gs)

C D _L_2_70_N C 4 . 1 4 . 2 4 . 3 4 . 4 4 . 5 4 . 6 4 . 7 4 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cem ent ( i n l

gs)

C EM _L_2_70_N C 2 . 6 2 . 8 3 . 0 3 . 2 3 . 4 3 . 6 3 . 8 4 . 0 4 . 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cl ays ( i n l

gs)

CLAY_L_2_70_NC 8 . 0 8 . 5 9 . 0 9 . 5 1 0. 0 1 0. 5 1 1. 0 1 1. 5 1 2. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cobal t ( i n l

gs)

C O _L_2_70_N C 5 . 0 5 . 5 6 . 0 6 . 5 7 . 0 7 . 5 8 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Chr om i um ( i n l

gs)

C R _L_2_70_N C 1 3 1 4 1 5 1 6 1 7 1 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cesi um ( i n l

gs)

C S_L_2_70_N C 7 . 2 7 . 6 8 . 0 8 . 4 8 . 8 9 . 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Copper ( i n l

gs)

C U _L_2_70_N C 1 2 1 4 1 6 1 8 2 0 2 2 2 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Di am ond ( i ndust r i al ) ( i n l

gs)

D I AM _L_2_70_N C 4 . 4 4 . 6 4 . 8 5 . 0 5 . 2 5 . 4 5 . 6 5 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Di at om i t e ( i n l

gs)

D I ATO _L_2_70_N C 2 . 6 2 . 8 3 . 0 3 . 2 3 . 4 3 . 6 3 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cl ay Fi r e cl ay ( i n l

gs)

FC LAY_L_2_70_N C 3 . 6 3 . 7 3 . 8 3 . 9 4 . 0 4 . 1 4 . 2 4 . 3 4 . 4 4 . 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Fel dspar ( i n l

gs)

FELD S_L_2_70_N C 2 . 8 3 . 0 3 . 2 3 . 4 3 . 6 3 . 8 4 . 0 4 . 2 4 . 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 I r on or e ( i n l

gs)

FEO R E_L_2_70_N C 5 . 2 5 . 6 6 . 0 6 . 4 6 . 8 7 . 2 7 . 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 I r on oxi de pi gm ent s ( i n l

gs)

FEPI G _L_2_70_N C 4 . 0 4 . 4 4 . 8 5 . 2 5 . 6 6 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 I r on and st eel scr ap ( i n l

gs)

FESCR_ L_2 _7 0_NC 1 . 8 2 . 0 2 . 2 2 . 4 2 . 6 2 . 8 3 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 I r on and st eel sl ag ( i n l

gs)

FESLA_L_2_70_N C 5 . 0 5 . 2 5 . 4 5 . 6 5 . 8 6 . 0 6 . 2 6 . 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 I r on and st eel ( i n l

gs)

FESTE_L_2_70_N C 4 . 4 4 . 6 4 . 8 5 . 0 5 . 2 5 . 4 5 . 6 5 . 8 6 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Fl uor spar ( i n l

gs)

FLU O R _L_2_70_N C 4 . 0 4 . 2 4 . 4 4 . 6 4 . 8 5 . 0 5 . 2 5 . 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cl ay- Ful l er s ear t h ( i n l

gs)

FUL E_ L_2 _7 0_NC 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 G al l i um ( i n l

gs)

G A_L_2_70_N C 5 . 2 5 . 6 6 . 0 6 . 4 6 . 8 7 . 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 G ar net ( i ndust r i al ) ( i n l

gs)

G AR_ L_ 2_7 0_ NC 1 2. 5 1 3. 0 1 3. 5 1 4. 0 1 4. 5 1 5. 0 1 5. 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 G er m ani um ( i n l

gs)

G E_L_2_70_N C 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 G em st ones ( i n l

gs)

G EM _L_2_70_N C 5 . 0 5 . 5 6 . 0 6 . 5 7 . 0 7 . 5 8 . 0 8 . 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 G r aphi t e ( nat ur al ) ( i n l

gs)

G R APH _L_2_70_N C 2 . 0 2 . 5 3 . 0 3 . 5 4 . 0 4 . 5 5 . 0 5 . 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 G ypsum ( i n l

gs)

G YP_L_2_70_N C 9 . 0 9 . 2 9 . 4 9 . 6 9 . 8 1 0. 0 1 0. 2 1 0. 4 1 0. 6 1 0. 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Hel i um ( i n l

gs)

H E_L_2_70_N C 1 1. 6 1 2. 0 1 2. 4 1 2. 8 1 3. 2 1 3. 6 1 4. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Haf ni um ( i n l

gs)

HF_L_2_70_N C 8 . 0 8 . 5 9 . 0 9 . 5 1 0. 0 1 0. 5 1 1. 0 1 1. 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 M er cur y ( i n l

gs)

H G _L_2_70_N C 9 . 0 9 . 5 1 0. 0 1 0. 5 1 1. 0 1 1. 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 I odi ne ( i n l

gs)

I _L_2_70_N C 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 I ndi um ( i n l

gs)

I N _L_2_70_N C 4 . 0 4 . 2 4 . 4 4 . 6 4 . 8 5 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cl ay- Kaol i n ( i n l

gs)

KAO _L_2_70_N C 5 . 0 5 . 2 5 . 4 5 . 6 5 . 8 6 . 0 6 . 2 6 . 4 6 . 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Kyani t e ( i n l

gs)

KYAN _L_2_70_N C 7 . 0 7 . 5 8 . 0 8 . 5 9 . 0 9 . 5 1 0. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Li t hi um ( i n l

gs)

LI _L_2_70_NC 4 . 0 4 . 1 4 . 2 4 . 3 4 . 4 4 . 5 4 . 6 4 . 7 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Li m e ( i n l

gs)

LI M E_L_2_70_N C 4 . 4 4 . 8 5 . 2 5 . 6 6 . 0 6 . 4 6 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 M agnesi um com pounds ( i n l

gs)

M G C O M _L_2_70_N C 7 8 9 1 0 1 1 1 2 1 3 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 M agnesi um m et al ( i n l

gs)

M G M T L_L_2_70_N C 6 7 8 9 1 0 1 1 1 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 M i ca ( sheet ) ( i n l

gs)

M I C AS _L_2_70_N C 5 . 4 5 . 6 5 . 8 6 . 0 6 . 2 6 . 4 6 . 6 6 . 8 7 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 M i ca ( scr ap and f l ake) ( i n l

gs)

M I CASP_L_ 2_ 70_ NC 5 . 0 5 . 5 6 . 0 6 . 5 7 . 0 7 . 5 8 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 M anganese ( i n l

gs)

M N _L_2_70_N C 8 . 4 8 . 8 9 . 2 9 . 6 1 0. 0 1 0. 4 1 0. 8 1 1. 2 1 1. 6 1 2. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 M ol ybdenum ( i n l

gs)

M O _L_2_70_N C 1 . 6 2 . 0 2 . 4 2 . 8 3 . 2 3 . 6 4 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Cl ay- M i scel l aneous cl ay and shal e ( i n l

gs)

M SCLAY_ L_2 _7 0_NC 5 . 8 6 . 0 6 . 2 6 . 4 6 . 6 6 . 8 7 . 0 7 . 2 7 . 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 M et al l i c Abr asi ves ( i n l

gs)

M TLAB _L_2_70_N C 4 . 4 4 . 8 5 . 2 5 . 6 6 . 0 6 . 4 6 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ni t r ogen ( i n l

gs)

N _L_2_70_N C 2 . 8 3 . 2 3 . 6 4 . 0 4 . 4 4 . 8 5 . 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Sodi um sul f at e ( i n l

gs)

N AS_L_2_70_N C 8 . 8 9 . 2 9 . 6 1 0. 0 1 0. 4 1 0. 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ni

bi

um ( C

l

um bi um ) ( i n l

gs)

NB_ L_2 _7 0_ NC 8 . 4 8 . 8 9 . 2 9 . 6 1 0. 0 1 0. 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ni ckel ( i n l

gs)

N I _L_2_70_NC 6 . 4 6 . 6 6 . 8 7 . 0 7 . 2 7 . 4 7 . 6 7 . 8 8 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Lead ( i n l

gs)

PB_L_2_70_N C 2 . 8 3 . 2 3 . 6 4 . 0 4 . 4 4 . 8 5 . 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Peat ( i n l

gs)

PEAT_L_2_70_NC 1 4. 8 1 5. 2 1 5. 6 1 6. 0 1 6. 4 1 6. 8 1 7. 2 1 7. 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Pl at i num - gr oup m et al s ( i n l

gs)

PG M _L _2_ 70 _NC 2 . 8 3 . 2 3 . 6 4 . 0 4 . 4 4 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Phosphat e r ock ( i n l

gs)

PHS_ L _ 2_ 7 0 _ NC 4 5 6 7 8 9 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Pot ash ( i n l

gs)

PO T_L_2_70_N C 3 . 4 3 . 5 3 . 6 3 . 7 3 . 8 3 . 9 4 . 0 4 . 1 4 . 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Per l i t e ( i n l

gs)

PR L_L_2_70_N C 2 . 0 2 . 5 3 . 0 3 . 5 4 . 0 4 . 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Pum i ce and pum i ci t e ( i n l

gs)

PUM _ L_ 2_ 70_ NC 7 8 9 1 0 1 1 1 2 1 3 1 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Q uar t z cr yst al ( i ndust r i al ) ( i n l

gs)

Q TZ_L_ 2_ 70 _NC 3 4 5 6 7 8 9 1 0 1 1 1 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Rar e ear t hs ( i n l

gs)

R AR EAR TH _L_2_70_N C 1 2. 5 1 3. 0 1 3. 5 1 4. 0 1 4. 5 1 5. 0 1 5. 5 1 6. 0 1 6. 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Rheni um ( i n l

gs)

R E_L_2_70_N C 1 2 3 4 5 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Sul f ur ( i n l

gs)

S_L_2_70_N C 3 . 1 3 . 2 3 . 3 3 . 4 3 . 5 3 . 6 3 . 7 3 . 8 3 . 9 4 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Sal t ( i n l

gs)

SALT_L_2_70_N C 6 . 8 7 . 2 7 . 6 8 . 0 8 . 4 8 . 8 9 . 2 9 . 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ant i m ony ( i n l

gs)

SB_L_2_70_N C 6 . 0 6 . 2 6 . 4 6 . 6 6 . 8 7 . 0 7 . 2 7 . 4 7 . 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Si l i con Car bi de ( i n l

gs)

SC AB_L_2_70_N C 4 . 0 4 . 4 4 . 8 5 . 2 5 . 6 6 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Soda ash ( sodi um car bonat e) ( i n l

gs)

SDASH_L _ 2 _ 7 0_ NC 8 . 5 9 . 0 9 . 5 1 0. 0 1 0. 5 1 1. 0 1 1. 5 1 2. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Sel eni um ( i n l

gs)

SE_L_2_70_N C 6 . 6 6 . 8 7 . 0 7 . 2 7 . 4 7 . 6 7 . 8 8 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Si l i con ( i n l

gs)

SI _L_2_70_NC 8 . 4 8 . 8 9 . 2 9 . 6 1 0. 0 1 0. 4 1 0. 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ti n ( i n l

gs)

SN _L_2_70_N C 1 . 4 1 . 6 1 . 8 2 . 0 2 . 2 2 . 4 2 . 6 2 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Sand and gr avel ( const r uct i

n) ( i

n l

gs)

SNDG RC_L_ 2_ 70_ NC 2 . 2 2 . 4 2 . 6 2 . 8 3 . 0 3 . 2 3 . 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Sand and gr avel ( i ndust r i al ) ( i n l

gs)

SNDG RI _ L_ 2_7 0_ NC 3 4 5 6 7 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 St r ont i um ( i n l

gs)

SR _L_2_70_N C 4 . 5 4 . 6 4 . 7 4 . 8 4 . 9 5 . 0 5 . 1 5 . 2 5 . 3 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 St eel ( i n l

gs)

STEEL_L_2_70_N C 1 . 4 1 . 6 1 . 8 2 . 0 2 . 2 2 . 4 2 . 6 2 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 St one ( cr ushed) ( i n l

gs)

STNC _L_2_70_N C 4 . 0 4 . 4 4 . 8 5 . 2 5 . 6 6 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 St one ( di m ensi

n) ( i

n l

gs)

STND_ L_2 _7 0_NC 1 0. 8 1 1. 2 1 1. 6 1 2. 0 1 2. 4 1 2. 8 1 3. 2 1 3. 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Tant al um ( i n l

gs)

TA_L_2_70_N C 3 . 8 4 . 0 4 . 2 4 . 4 4 . 6 4 . 8 5 . 0 5 . 2 5 . 4 5 . 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Tal c and pyr ophyl l i t e ( i n l

gs)

TAL C_ L _ 2_ 7 0 _ NC 9 . 6 1 0. 0 1 0. 4 1 0. 8 1 1. 2 1 1. 6 1 2. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Tel l ur i um ( i n l

gs)

TE_L_2_70_N C 1 0. 0 1 0. 4 1 0. 8 1 1. 2 1 1. 6 1 2. 0 1 2. 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Thor i um ( i n l

gs)

TH _L_2_70_N C 8 . 5 9 . 0 9 . 5 1 0. 0 1 0. 5 1 1. 0 1 1. 5 1 2. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ti t ani um m et al ( i n l

gs)

TI _L_ 2_ 70 _NC 7 . 6 8 . 0 8 . 4 8 . 8 9 . 2 9 . 6 1 0. 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ti t ani um scr ap ( i n l

gs)

TI SC P_L_2_70_N C 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Thal l i um ( i n l

gs)

TL_L_2_70_NC 4 . 2 4 . 4 4 . 6 4 . 8 5 . 0 5 . 2 5 . 4 5 . 6 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Tr i pol i ( Nat ur al Abr asi ve) ( i n l

gs)

TRI P_ L_2 _7 0_ NC 8 . 4 8 . 8 9 . 2 9 . 6 1 0. 0 1 0. 4 1 0. 8 1 1. 2 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Vanadi um ( i n l

gs)

V_L_2_70_N C 4 . 0 4 . 4 4 . 8 5 . 2 5 . 6 6 . 0 6 . 4 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Ver m i cul i t e ( i n l

gs)

VR M _L_2_70_N C 8 . 5 9 . 0 9 . 5 1 0. 0 1 0. 5 1 1. 0 1 1. 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Tungst en ( i n l

gs)

W _L_2_70_N C 4 . 8 4 . 9 5 . 0 5 . 1 5 . 2 5 . 3 5 . 4 5 . 5 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 W ol l ast oni t e ( i n l

gs)

W LA_L_2_70_N C 6 . 4 6 . 8 7 . 2 7 . 6 8 . 0 8 . 4 8 . 8 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Zi nc ( i n l

gs)

ZN _L_2_70_N C 5 . 4 5 . 6 5 . 8 6 . 0 6 . 2 6 . 4 6 . 6 6 . 8 7 . 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Zi r coni um m i ner al concent r at es ( i n l

gs)

ZR_ L _ 2_ 7 0 _ NC

SLIDE 26

Conclusions

The extreme volatility of mineral prices (even w annual

frequency data) makes it very difficult to say anything definitive about long-term trends

Our band-pass filter analysis suggests that long-term

trends vary widely over time, often changing direction more than once rather than following the U-shaped pattern suggest by (some) theory

Studying aggregate commodity indexes is a dubious

activity, given variety of underlying price behaviors

26

SLIDE 27

Extensions: Bass-pass Filter Analysis of Super Cycles (20-70 Years)

Cuddington-Jerrett (2008)
n LME6
Jerrett-Cuddington (2008)
n Steel, Pig iron, and

Molybdenum

Zellou-Cuddington (2012)
n crude oil and coal

27

.4
.3
.2
.1

.0 .1 .2 .3 70 80 90 00 10 20 30 40 50 60 70 80 90 00 10

Economist Commodity Price Index: Super-Cycle Component

1 2 70 80 90 00 10 20 30 40 50 60 70 80 90 00 10

Indicator of SC Expansion Difficult to interpret 2000-ongoing? 1961-1977 1934-47 1879-1918??

SLIDE 28

Appendix: USGS Data

The USGS website has annual data for 101 non-energy minerals from 1900 (in many

cases) through 2010. Both nominal unit values and real unit values, using the U.S. CPI as the deflator, are available. This allows for a rather exhaustive coverage of the mineral commodities.

Source: http://minerals.usgs.gov/ds/2005/140/#data
“The U.S. Geological Survey (USGS) provides information to the public and to

policy-makers concerning the current use and flow of minerals and materials in the United States economy. The USGS collects, analyzes, and disseminates minerals information on most nonfuel mineral commodities.

“This USGS digital database is an online compilation of historical U.S. statistics on

mineral and material commodities. The database contains information on approximately 90 mineral commodities, including production, imports, exports, and stocks; reported and apparent consumption; and unit value (the real and nominal price in U.S. dollars of a metric ton of apparent consumption). For many of the commodities, data are reported as far back as 1900. Each commodity file includes a document that describes the units of measure, defines terms, and lists USGS contacts for additional information. [Accessed August 2, 2012]

Insert List and years covered for each (to do) ***

28

SLIDE 29

References (in progress)

Benati, L. 2001. “Band-Pass Filtering, Cointegration, and Business Cycle Analysis,” Working Paper No 142. Bank of England. Cristiano, L. and T. Fitzgerald. 2003. “The Band Pass Filter,” International Economic Review 44, 435-65. Cogley, Timothy. 2008. “Data Filters,” in Steven N. Durlauf and Lawrence E. Blume (eds.) The New Palgrave Dictionary of Economics, 2nd Edition in Eight Volumes, Palgrave MacMillan. Cogley, T. and J. Nason. 1995. “Effects of the Hodrick-Prescott Filter on Trend and Difference Stationary Time Series: Implications for Business Cycle Research,” Journal of Economic Dynamics and Control 19, 253-78. Comin, Diego, and Mark Gertler. “Medium-Term Business Cycles.” American Economic Review 96, no. 3 (June 2006): 523–551. Cuddington, John T., Rodney Ludema and Shamila Jayasuriya. 2007. “Prebisch-Singer Redux,” in Daniel Lederman and William F. Maloney (eds.), Natural Resources and Development: Are They a Curse? Are They Destiny? World Bank/Stanford University Press. Cuddington, John T and Daniel Jerrett. 2008. “Super Cycles in Metals Prices?” IMF Staff Papers 55, 4 (December), 541-565. Gaudet, G. 2007. “Natural Resource Economics Under the Rule of Hotelling,” Canadian Journal of Economics 40: 1033–59. Heap, Alan. 1995. CitiGroup Hotelling, Harold. “The Economics of Exhaustible Resources.” Journal of Political Economy 39, no. 2 (April 1, 1931): 137–175. Murray, C. 2003. “Cyclical Properties of Baxter-King Filtered Time Series,” Review of Economics and Statistics 85, 472-76. Osborn, D. 1995. “Moving Average Detrending and the Analysis of Business Cycles,” Oxford Bulletin of Economics and Statistics 57, 547-58. Slade, Margaret. 1982. “Trends in Natural-Resource Commodity Prices: An Analysis of the Time Domain,” Journal of Environmental Economics and Management 9, 122-137. Slade, Margaret and Henry Thille. 2009. “Whither Hotelling: Tests of the Theory of Exhaustible Resources,” Annual Review of Resource Economics 1, pp. 239-260. Tilton, John E. On Borrowed Time? Assessing the Threat of Mineral Depletion. Washington, D.C.: Resources for the Future, 2003. Zellou, Abdel and John T Cuddington. 2012. “Is There Evidence of Super Cycles in Crude Oil Prices?” SPE Economics and Management (forthcoming).

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SLIDE 30

Thank You! Comments welcome

My e-mail: jcudding@mines.edu Many thanks to the Getulio Vargas Foundation and VALE for sponsoring and hosting this conference

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