Variable Long-Term Trends

in 100+ Mineral Prices

John T. CuddingtonWilliam J. Coulter Professor of Mineral Economics

Colorado School of Mines

August 16-17, 2012 Rio de Janeiro, Brazil Conference

“The Economics and Econometrics of Commodity Prices”sponsored by the Getulio Vargas Foundation and VALE

2

My Home:Colorado School of Mines

Division of Economics and Businesswww.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 Programso BS - Economicso MS – Engineering and Technology Mgt (ETM)o MS, PhD – Mineral and Energy Economics

• I’d show you pictures, but we all can’t live there!

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)

4

Motivation - Policy• Policymakers–-keen interest during periods

of 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

5

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

Threat of 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)

6

Causes of Explosion in Mineral Use

o Advances in technology allow [exploration and] extraction…at lower and lower cost. [Shifts mineral supply curves out/down]

o Advances in technology also permit new and better mineral commodities serving a range of needs.[Shifts mineral demand curves out/up]

o 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]

o 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)

7

Hotelling Theory of Nonrenewable

Resources• Hotelling’s (1931) ‘benchmark’ theory of

nonrenewable resources o Shadow price of resource stock (in the ground) =

Price – Marginal Extraction and Production Costo Hotelling model implies the r percent rule:

shadow price should rise at a rate equal to the interest rate

o Hotelling also predicted that resource consumption would decline monotonically over time.

o The competitive market outcome was Pareto efficient: Don’t worry, everything will work out fine!

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.

9

Empirical Evidence on Long-

Term Price Trendso 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

o Much of the literature focuses on estimating either TS or DS specifications in order to estimate the constant long-term trend (albeit with the possible search for occasional structural breaks).

o TS Model

o DS Model

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: o Linear and (presumably) quadratic trend model are

subject to spurious regression issues in the presence of unit roots.

11

Overall conclusions from review of empirical work

• Conclusions on the significance of the time trend depend critically on presence/absence of unit roots and/or structural breaks

• Any trend is small and difficult to estimate precisely, given the huge year-to-year volatility in the price series.

12

Continued…• “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.” (Tilton, 2003, p.54)

• 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.

•  

13

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.

14

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.”

15

Band-pass Filters (cont.)

• “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).o Symmetric – no phase shift o Asymmetric - allow the filtered series to be calculated

all the way to the ends of the data set

16

Applications• Band-pass (BP) filters allows us to:

o Extract cyclical components within a specified range of periods (or frequencies) from an economic time series.

o 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 (2008, 2008, 2012): super cycles in mineral prices

17

Our Definition of the ‘Long Run’

18

Preliminary Look at The Economist

IndustrialsCommodity Index3.0

3.5

4.0

4.5

5.0

1875 1900 1925 1950 1975 2000

Economist Commodity Price IndexUS dollar terms, in logs

-.4

-.2

.0

.2

.4

.6

1875 1900 1925 1950 1975 2000

Economist Commodity Price IndexUS dollar terms, log-difference

-.8

-.4

.0

.4

.8

1875 1900 1925 1950 1975 2000

Economist Commodity Price IndexUS dollar terms, second log-difference

• Index includes LME6 & non-food

agriculturals (wool, timber, etc.)

• 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

19

30-Year Moving Average:

Centered vs. Trailing

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 AverageTrailing 30-Year Moving Average -- Note severe phase shift!

20

Economist Industrial Commodity Index (EICI):

Annual Growth Rates

-.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

21

EICI:

-.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

22

ACF-Band-Pass Filter

Results on Long-run

Trend

• Long-run Trend in EICI is

negative until mid-1980s,

then turns upward

• One change in direction

• Not exactly the classic U-

shape that Pindyck-Heal-

Slade would predict

• Remember: EICI contains

both renewable and

nonrenewable resources

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)

0

1

2

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

RP_NC_DUM

23

Long-run Trends in

LME6:Aluminum, Copper

Nickel, LeadTin, Zinc

• Wide variety of

price paths

• Some have more

than one change in

direction

• Can we tell metal

specific stories

about the roles of

exploration/discove

ry, depletion, and

technological

change?

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

24

Long-Run Variable Trend Rates

for LME6

25

Variable Trend RATES in the

USGS 101 Minerals

Hmmm?

What am I supposed to

learn from this?

(Don’t put too much info

on a slide!?)

-.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_DAL_NC_D ALOX_NC_D ALUM_NC_D AS_NC_DASB_NC_D AU_NC_D B_NC_D BALL_NC_DBARITE_NC_D BAUXI_NC_D BE_NC_D BENT_NC_DBI_NC_D BR_NC_D CD_NC_D CEM_NC_DCLAY_NC_D CO_NC_D CR_NC_D CS_NC_DCU_NC_D DIAM_NC_D DIATO_NC_D FCLAY_NC_DFELDS_NC_D FEORE_NC_D FEPIG_NC_D FESCR_NC_DFESLA_NC_D FESTE_NC_D FLUOR_NC_D FULE_NC_DGA_NC_D GAR_NC_D GE_NC_D GEM_NC_DGRAPH_NC_D GYP_NC_D HE_NC_D HF_NC_DHG_NC_D I_NC_D IN_NC_D KAO_NC_DKYAN_NC_D LI_NC_D LIME_NC_D MGCOM_NC_DMGMTL_NC_D MICAS_NC_D MICASP_NC_D MN_NC_DMO_NC_D MSCLAY_NC_D MTLAB_NC_D N_NC_DNAS_NC_D NB_NC_D NI_NC_D PB_NC_DPEAT_NC_D PGM_NC_D PHS_NC_D POT_NC_DPRL_NC_D PUM_NC_D QTZ_NC_D RAREARTH_NC_DRE_NC_D S_NC_D SALT_NC_D SB_NC_DSCAB_NC_D SDASH_NC_D SE_NC_D SI_NC_DSN_NC_D SNDGRC_NC_D SNDGRI_NC_D SR_NC_DSTEEL_NC_D STNC_NC_D STND_NC_D TA_NC_DTALC_NC_D TE_NC_D TH_NC_D TI_NC_DTISCP_NC_D TL_NC_D TRIP_NC_D V_NC_DVRM_NC_D W_NC_D WLA_NC_D ZN_NC_DZR_NC_D

26

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 asiv es ( m anuf act ur ed) ( in logs )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 asives ( nat ur al) ( in logs )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 as ive Spec ial Silic a ( in logs)ABNSS_L_2_70_NC

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

Silv er ( in logs) AG _L_2_70_NC

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

Alum inum ( in logs) AL_L_2_70_NC

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

Alum inum O xide ( in logs)ALO X_L_2_70_NC

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

Alum ina ( in logs) ALUM _L_2_70_NC

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 senic ( in logs) AS_L_2_70_NC

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 ( in logs) ASB_L_2_70_NC

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 old ( in logs) AU_L_2_70_NC

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 ( in logs) B_L_2_70_NC

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

  Clay -   Ball c lay ( in logs)BALL_L_2_70_NC

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 it e ( in logs)BARI TE_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

Bauxit e ( in logs)BAUXI _L_2_70_NC

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 y llium ( in logs) BE_L_2_70_NC

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

Clay-   Bent onit e ( in logs )BENT_L_2_70_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

Bism ut h ( in logs) 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 ine ( in logs) BR_L_2_70_NC

5

6

7

8

9

1 0

1 1

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Cadm ium ( in logs) CD_L_2_70_NC

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 ( in logs) CEM _L_2_70_NC

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

Clay s ( in logs) 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

Cobalt ( in logs) CO _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

Chr om ium ( in logs) CR_L_2_70_NC

1 3

1 4

1 5

1 6

1 7

1 8

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Ces ium ( in logs) CS_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 ( in logs) CU_L_2_70_NC

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

Diam ond ( indust r ial) ( in logs)DI AM _L_2_70_NC

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

Diat om it e ( in logs)DI ATO _L_2_70_NC

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

Clay Fir e c lay ( in logs)FCLAY_L_2_70_NC

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

Felds par ( in logs)FELDS_L_2_70_NC

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 ( in logs)FEO RE_L_2_70_NC

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 oxide pigm ent s ( in logs)FEPI G _L_2_70_NC

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 s t eel s cr ap ( in logs)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 s t eel s lag ( in logs )FESLA_L_2_70_NC

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 s t eel ( in logs)FESTE_L_2_70_NC

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

Fluor spar ( in logs)FLUO R_L_2_70_NC

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

Clay-   Fuller s ear t h ( in logs )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 allium ( in logs) G A_L_2_70_NC

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 ( indust r ial) ( in logs )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 anium ( in logs ) G E_L_2_70_NC

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 ( in logs )G EM _L_2_70_NC

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 aphit e ( nat ur al) ( in logs)G RAPH_L_2_70_NC

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 ( in logs) G YP_L_2_70_NC

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

Helium ( in logs) HE_L_2_70_NC

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 nium ( in logs) HF_L_2_70_NC

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 c ur y ( in logs) HG _L_2_70_NC

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 odine ( in logs) I _L_2_70_NC

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 ndium ( in logs) I N_L_2_70_NC

4 . 0

4 . 2

4 . 4

4 . 6

4 . 8

5 . 0

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Clay - Kaolin ( in logs)KAO _L_2_70_NC

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

Ky anit e ( in logs) KYAN_L_2_70_NC

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

Lit hium ( in logs) 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

Lim e ( in logs) LI M E_L_2_70_NC

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 agnesium com pounds ( in logs )M G CO M _L_2_70_NC

7

8

9

1 0

1 1

1 2

1 3

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

M agnesium m et al ( in logs)M G M TL_L_2_70_NC

6

7

8

9

1 0

1 1

1 2

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

M ic a ( sheet ) ( in logs)M I CAS_L_2_70_NC

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 ic a ( scr ap and f lak e) ( in logs)M I CASP_ L _ 2 _ 7 0 _ 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 ( in logs) M N_L_2_70_NC

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 olybdenum ( in logs)M O _L_2_70_NC

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

Clay- M isc ellaneous c lay and s hale ( in logs)M SCL AY_ 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 allic Abr as iv es ( in logs)M TLAB_L_2_70_NC

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

Nit r ogen ( in logs) N_L_2_70_NC

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

Sodium s ulf at e ( in logs)NAS_L_2_70_NC

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

Niobium ( Colum bium ) ( in logs )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

Nic k el ( in 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 ( in logs) PB_L_2_70_NC

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 ( in logs) 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

Plat inum - gr oup m et als ( in logs)PG M _ L _ 2 _ 7 0 _ 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

Phos phat e r ock ( in logs)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 ( in logs) PO T_L_2_70_NC

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 lit e ( in logs) PRL_L_2_70_NC

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 ic e and pum icit e ( in logs)PUM _ L _ 2 _ 7 0 _ 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 c r yst al ( indus t r ial) ( in logs )Q TZ_ L _ 2 _ 7 0 _ 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 ( in logs)RAREARTH_L_2_70_NC

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

Rhenium ( in logs) RE_L_2_70_NC

0

1

2

3

4

5

6

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Sulf ur ( in logs) S_L_2_70_NC

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

Salt ( in logs) SALT_L_2_70_NC

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 im ony ( in logs) SB_L_2_70_NC

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

Silicon Car bide ( in logs)SCAB_L_2_70_NC

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 ( s odium car bonat e) ( in logs )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

Selenium ( in logs) SE_L_2_70_NC

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

Silic on ( in logs) 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

Tin ( in logs) SN_L_2_70_NC

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 uc t ion) ( in logs )SNDG RC_ L _ 2 _ 7 0 _ 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 av el ( indus t r ial) ( in logs)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 ium ( in logs) SR_L_2_70_NC

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 ( in logs) STEEL_L_2_70_NC

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) ( in logs)STNC_L_2_70_NC

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 ( dim ension) ( in logs)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 alum ( in logs) TA_L_2_70_NC

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

Talc and pyr ophyllit e ( in logs)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

Tellur ium ( in logs) TE_L_2_70_NC

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 ium ( in logs) TH_L_2_70_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

Tit anium m et al ( in logs)T I _ L _ 2 _ 7 0 _ 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

Tit anium s cr ap ( in logs)TI SCP_L_2_70_NC

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

Thallium ( in logs) 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 ipoli ( Nat ur al Abr asive) ( in logs)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

Vanadium ( in logs) V_L_2_70_NC

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 iculit e ( in logs)VRM _L_2_70_NC

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 ( in logs) W _L_2_70_NC

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 ollas t onit e ( in logs)W LA_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 ( in logs) ZN_L_2_70_NC

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

Zir c onium m iner al concent r at es ( in logs )Z R_ L _ 2 _ 7 0 _ NC

27

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 suggested by (some) theory

• Studying aggregate commodity indexes is a dubious activity, given variety of underlying price behaviors, if one is interested in long-run trends

28

Extensions: Bass-pass

Filter Analysis of Super Cycles

(20-70 Years)

• Cuddington-Jerrett

(2008) on LME6

• Jerrett-Cuddington

(2008) on Steel, Pig

iron, and Molybdenum

• Zellou-Cuddington

(2012) on crude oil and

coal

-.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

0

1

2

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

Indicator of SC Expansion

Difficult tointerpret

2000-ongoing?

1961-1977

1934-47

1879-1918??

29

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) *** 

30

ReferencesBenati, 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).

 

31

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