Business Cycle Effects on Metal and Oil Prices:
Understanding the Price Retreat of 2008-9
by
John T. Cuddington*
and Daniel Jerrett**
12/02/2011
Abstract
This paper studies the effect of macroeconomic business cycles on six metals traded on the
London Metal Exchange and oil prices. Reduced-form equations for real metal prices that
depend separately on the trend and cyclical components of global real GDP oil prices (as a
proxy for energy inputs in metals production) are derived. The estimated trend and cyclical
income responses suggest that our LME metals (with two exceptions) and oil prices are much
more responsive to cyclical than trend movements in economic activity, as theory would suggest.
Through counterfactual simulations, the following types of questions can be addressed: (i) how
much lower would metal and oil prices have been in the early years of the 21st century in the
absence of the booming global economy? and (ii) “How much higher would metal prices have
been in 2008-2009 in the absence of the sharp slowdown in the global economy?
Key Words: metal price fluctuations and cycles, oil prices, business cycle analysis, trend-cycle
decomposition, Christiano-Fitzgerald band-pass filter.
JEL Codes: Q31 (Nonrenewable Resources and Conservation – Supply and Demand), E32
(Macro: Prices, Fluctuations and Cycles), C53 (Forecasting Models and Simulation Methods),
C32 (Time Series Models), L70 (Industry Studies: Primary Products and Construction – General)
*William J. Coulter Professor of Mineral Economics, Colorado School of Mines, Golden, CO
80401
**Fixed Income Analyst, Putnam Investments, Boston MA 02109
1 I.
Introduction
Mineral economists have long held that metal demand is highly responsive to changes in
the level of economic activity. Given low short-run price elasticities of both supply and demand,
this causes metal prices to be strongly pro-cyclical. That is, metal prices move up quite
dramatically during macroeconomic expansions, and fall sharply during contractions. See
Radetzki (2006, 2008) and Tilton (1992). The worldwide economic slowdown, with outright
recessions in major industrial nations, is one possible explanation of the unprecedented metals
price retreat in the second half of 2008 and early 2009, following an equally unprecedented six to
seven year run up in real prices. Other possible contributing factors are (i) increased investment
activity in commodities, as an emerging asset class, with hedge funds retrenching sharply with
the onset of the recession and (ii) overshooting behavior of metals prices in response to sharp
changes in monetary policy (as first described in Frankel, 1986).
Although the view that metal prices are strongly pro-cyclical is widely held, and is
supported by the detailed “case study” investigations like Radetzki (2006), our search of the
literature found few formal econometric studies addressing the topic.1 Perhaps the link between
metal prices and business cycles is too self-evident to be a topic worthy of formal testing. There
are several papers that characterize metal price cycles using a dating procedure like that used by
the NBER when dating U.S. business cycles.2 Noteworthy examples include Davutyan and
1
In spite of its title, Fama and French’s (1988) paper “Business Cycles and the Behavior of Metal Prices” has very
little discussion of business cycles. Their analysis focuses on the impact of metal market tightness and its effect on
the interest-adjusted basis. They presume that markets are tight near the end of business cycle expansions, but there
is no analysis of this claim.
2
The NBER Business Cycles Dating Committee uses a judgmental approaches that relies on an analysis of various
leading, coincident, and lagging indicators using monthly frequency data in their efforts to determine the official
turning points in the U.S. economy by month (not quarter) when they occur. See
http://www.nber.org/cycles/dec2008.html for details.
2 Roberts (1994), Labys et al (1999), Labys et al (2000), and Cashin and McDermott (2002).
Some of these authors employ the mechanical algorithm developed by Bry and Boschan (1971)
for the NBER’s early research to identify local peaks and troughs in general economic activity.
Although these papers provide interesting analyses of peaks and troughs in metal prices, they do
not ask whether the metal-price cycles that they identify match up with the timing of
macroeconomic fluctuations. Only the paper by Labys, Achouch and Terraza (1999) examines
the correlation between metals prices and macroeconomic variables such as industrial
production, interest rates, and stock prices.
The objective of this paper is to study the link between macroeconomic business cycles
and metals prices. Using a simple structural model, we derive reduced-form equations for real
metals prices that depend separately on the trend and cyclical components of global real GDP, as
well as other determinants of mineral supply and demand. Estimation of this equation allows us
to address the question: how sensitive are metals prices to cyclical versus trend movements in
economic activity? The reduced-form equation is then used to simulate the path of real metal
prices in a counterfactual scenario where the business-cycle component of real GDP is set equal
to zero. Comparing this scenario to the historical price series enables us to answer the related
questions: (i) how much lower would metal and oil prices have been in the early years of the 21st
century in the absence of the booming global economy? and (ii) “How much higher would metal
prices have been in 2008-2009 in the absence of the sharp slowdown in the global economy?
3 II.
A Simple Structural Model of Metal Markets
The importance of studying business-cycle movements in metals is well summarized by
John Tilton (1992), who has a detailed conceptual discussion of how the business cycle affects
metals demand:
The demand for many mineral commodities is particularly responsive to income changes caused
by business cycle fluctuations. Metals and other materials, in particular, are consumed primarily
in the capital equipment, construction, transportation, and consumer durable sectors of the
economy, which use them to produce automobiles, refrigerators, homes and office buildings,
new machinery, and other such items. These sectors boom when the economy is doing well, and
suffer severely when it falters. Since small fluctuations in the business cycle cause major
changes in their output and in turn the demand for materials, the income elasticity is normally
greater than one when the business cycle is responsible for changes in income. When income
changes are the result of secular growth trends, the traditional and still very common
presumption is that mineral demand grows or declines in direct proportion with income. The
income elasticity of demand in such situation is thus one. (p.51)
In addition to the income elasticity of metal demand being larger in the presence of
business-cycle shocks, mineral economists generally believe that the metals supply curves are
quite price inelastic in the short run due to capacity constraints. (See, e.g., Tilton 1992 and
Radetzki 2008). A steep supply curve along with large demand curve movements due to
business-cycle shocks will result in large price movements in the short run.
Our empirical analysis below is, to our knowledge, the first to implement Tilton’s (1992,
p.51) idea that “The effect of income on metals demand can be separated into two parts: a
cyclical component caused by short-term fluctuations in the business cycle, and a secular
component caused by long-term growth trends.”
Consider a simple structural model of a metal market where the quantity of metal
demanded at time t ( qmt ) depends separately on the trend and cyclical components of real
4 economic activity such as global real GDP ( ytT , ytC ) , global population ( popt ) in addition to the
real metal price ( pmt ) :
qmt = β10 − β11 pmt + β12 ytT + β13 ytC + β14 popt + ε Dt
(1)
qmt = β10 − β11 pmt + β12 ( ytT + ytC ) + γ ytC + β14 popt + ε Dt
(1’)
or equivalently:
Tilton’s discussion above implies that γ ≡ β13 − β12 > 0 . The inverse metal supply function is
assumed to depend on energy input costs ( pet ) , and a time trend (trend) to capture the net effects
of technological change and ongoing resource depletion:
pmt = β 20 + β 21qmt + β 22 pet + β 23t trend + ε St
(2)
Our primary focus is on metal price behavior and we do not have access to quantity data
on metal supply, demand or stocks (inventories). Therefore, the following reduced-form price
equation is estimated:
pmt = β 0 + β1 ytC + β 2 ytT + β3 pet + β 4 popt + β 5trend
+ β 6 pmt −1 + β 7 pmt − 2 + ε t
(3)
The structural model implies the following signs on the reduced-form coefficients:
β1 , β 2 , β3 > 0 . The Tilton conjecture that the cyclical income elasticity of mineral demand
exceeds the trend income elasticity implies the following testable hypothesis: β1 > β 2 > 0 in
equation (3). In the estimated reduced-form equations reported below, one or two lags of the real
metal price were included to eliminate serial correlation in the residuals and allow for expected
dynamic effects on both the supply and demand sides of the metals markets.
5 III.
Data and Estimation
Reduced-form real metal price equations were estimated for the following six metals
traded on the LME using annual data from 1950 through 2009: aluminum, copper, nickel, lead,
tin and zinc. An analogous equation was also estimated for real oil prices (but with oil prices, of
course, eliminated as a regressor on the right-hand side). Measures of global real GDP and
global population were obtained from Maddison (2009) for the period 1950-2008 and extended
through 2009 by the authors by using growth rates from IMF data3. The real price of oil4 is used
as a proxy for the price of energy inputs; it was provided by Alan Heap of CitiGroup. All metal
and oil prices are in U.S. dollars and were deflated by the U.S. consumer price index to obtain
real prices. All variables, except trend, appear in natural log form in the regressions below.
The Christiano-Fitzgerald asymmetric (2003) band pass filter with a window of 2-8 years
is used to extract the business cycle component of real GDP and real oil prices. The ‘trend’ in
these series is then defined as the actual series minus the business cycle component. The trend
therefore includes possible longer-term cycles such as ‘super cycles’ (with periods of 20-70
years) studied by Cuddington-Jerrett (2008) and Jerrett-Cuddington (2008).5 See CuddingtonJerrett (2008) for a detailed discussion of band-pass filters.
The estimated reduced-form real price equations for the six LME metals and crude oil are
reported in Table 1.6 The adjusted R2 statistics are uniformly high (.72-.95) and the Ljung-Box
3
http://www.imf.org/external/ns/cs.aspx?id=28
The oil price series is Arabian Light posted at Ras Tanura from 1945 to 1985 and the Brent spot price thereafter.
5
The time span of our dataset is too short to permit the extraction of the super cycle component from the trend.
6
The equations are estimated OLS equation by equation. We also experimented with SUR estimation, which is
identical to OLS when the same regressors appear in all equations. In our application, only the lagged dependent
variable varies across equations. The results are very similar.
4
6 Q(5) statistics7 indicate that two lags of the dependent variable is sufficient to render the
residuals white noise. Note that the estimated coefficients on both the cyclical and trend
Table 1: OLS Regression Results for Reduced-Form Real Mineral Price Equations
Dep. Var. Real Price of: Aluminum Copper C 38.98 82.09 [1.91]⁰ [2.76]** Log(GDPcyclical) 4.11 [1.71]⁰ Log(GDPtrend) Log(Oil Price) Log(Pop) Time Trend 1.67 [3.96]** 0.09 [2.03]* ‐2.82 [‐2.55]* ‐0.03 [‐1.82]⁰ 0.52 [3.23]** ‐0.30 [‐1.79]⁰ 0.87 48.82** Dep. Var.(‐1) Dep. Vary.(‐2) 5.77 [3.39]** Crude Oil 47.18 [2.02]* Nickel 107.94 [2.99]** 2.46 [0.79] 11.59 [2.88]** 2.39 3.65 1.64 [3.83]** [4.90]** [2.20]* 0.01 0.07 [0.21] [1.71]⁰ ‐5.39 ‐7.55 ‐3.34 [‐3.13]** [‐3.81]** [‐2.08]* 0.00 ‐0.01 [‐0.13] [‐0.32] 0.85 0.67 0.87 [7.75]** [6.84]** [8.30]** ‐0.29 ‐0.37 ‐0.03 [‐2.91]** [‐2.66]* [‐0.21] 0.87 0.72 0.88 48.01** 18.63** 60.72** Lead 70.21 [2.10]* Tin ‐19.97 [‐0.74] Zinc 7.50 [0.21] 5.93 [1.75]⁰ 7.32 [2.98]** 0.38 [0.15] 1.48 [3.12]** 2.61 [5.69]** 0.18 [3.11]** ‐0.81 [‐0.63] ‐0.09 [‐3.49]** 0.63 [6.09]** ‐0.12 [‐0.90] 0.95 134.95** 1.50 [3.00]** 0.11 [2.06]* ‐1.30 [‐0.68] ‐0.04 [‐1.96]⁰ 0.76 [8.10]** ‐0.41 [‐2.91]** 0.74 20.03** 0.03 [0.49] ‐4.21 [‐2.34]* 0.01 [0.51] 0.72 [6.30]** ‐0.18 [‐1.16] 0.82 32.40** R‐squared F‐statistic Ljung‐Box Q(5) 3.78 1.21 6.91 3.79 3.15 2.30 2.14 Student t statistics in parentheses. ** indicates significant at the 99% level, * at the 95% level and ⁰ at the 90% level. 7
The null hypothesis is no serial correlation of order 1-5.
7 components of global real GDP are positive, as theory would predict, and for the most part the t
statistics (in parentheses) indicate they are statistically different from zero. The two exceptions
are nickel and zinc, where the coefficients on cyclical GDP are statistically insignificant and, in
fact, smaller in magnitude than the coefficient on trend GDP (contrary to the Tilton hypothesis).
Table 2 reports t tests for the null hypothesis that the coefficient to cyclical GDP minus the
coefficient on trend GDP is less than or equal zero; the (one-sided) alternative is that the
difference is positive, as predicted by the Tilton hypothesis. That is,
H 0 : β1 − β 2 ≤ 0; H A : β1 − β 2 > 0.
Table 2: Estimated Difference between Cyclical and Trend GDP Response on Real
Prices; Hypothesis Test H 0 : β1 − β 2 ≤ 0; H A : β1 − β 2 > 0.
Aluminum Copper Nickel Crude Oil Lead Tin Zinc β1‐β2 2.44 3.38 ‐1.18 9.66 4.45 4.71 ‐1.12 t stat for H0: β1‐β2 ≤0 [1.00] [1.78]* [‐0.36] [2.62]** [1.41]⁰ [1.81]* [‐0.43] The differential response of real prices to cyclical rather than trend GDP movements is positive
in all cases except nickel and zinc. Four of the remaining five minerals have positive responses
that are statistically significant (at the 10% level, at least), as the Tilton hypothesis predicts.8
All in all, we conclude that our estimated reduced-form price equations provide some,
empirical support for the proposition that cyclical fluctuations in global GDP should have a more
pronounced effect on real mineral prices than trend movements in GDP. We are surprised by the
8
We experimented with the use of stepwise regression to narrow down the set of regressors in the hopes of
improving the precision of our estimates. This however, made little difference in the simulation analysis that
follows. These stepwise regression results are available on request.
8 absence of cyclical income effects for nickel and zinc, given that both metals are used in
stainless steel production, a cyclical industry. (Nickel is a direct input, while zinc is a
galvanizing agent.)
Perhaps the use of a global measure of real economic activity in our
analysis is too crude; greater country or regional disaggregation may be needed. When we added
the cyclical and trend components of U.S. GDP to the specification in place of or in addition to
global GDP, however, the results were less satisfactory than those reported.
IV.
Model Simulations
The seven mineral price equations (six metals plus crude oil) are now combined in a
simulation model to produce dynamic multi-step ahead forecasts of the mineral prices from 1952
through 2009, conditional on the historical paths of world GDP and population. Note that the
forecasts for oil prices feed into the six metal equations. Thus, the cyclical component of GDP
affects real metal prices both directly and via its effect on oil/energy input costs. The dynamic
forecast results in Panels 1 and 2 of Figure 1 show that, for the most part, the actual data over the
1952-2009 period lie within the two standard forecast error band. This is quite remarkable for a
58-year multi-step ahead forecast – albeit in-sample and conditional on world GDP and
population. With the exceptions of tin and oil, the model under-forecasted mineral prices during
the boom of the middle years of the first decade of the 21st century. That is, actual prices were
higher than what would have been expected given the underlying ‘fundamentals’ in our model.
An open question that whether this reflects the omission of additional fundamentals in our
model, or whether it reflects ‘animal spirits,” i.e. speculative factors.
9 Figure 1
Multi-Step Ahead Forecasts with 2 Std Error Band
vs. Actuals: Panel 1
Copper
Aluminum
6.0
6.4
5.6
6.0
5.2
5.6
4.8
5.2
4.4
4.8
4.0
1950
1960
1970
1980
1990
4.4
1950
2000
1960
Nickel
5.5
2.5
5.0
2.0
4.5
1.5
4.0
1.0
3.5
1960
1970
1980
1990
3.0
1950
2000
10 1980
1990
2000
Lead
3.0
0.5
1950
1970
1960
1970
1980
1990
2000
Multi-Step Ahead Forecasts with 2 Std Error Band
vs. Actuals: Panel 2
Zinc
Tin
5.6
8.5
8.0
5.2
7.5
4.8
7.0
6.5
4.4
6.0
4.0
5.5
5.0
1950
1960
1970
1980
1990
2000
3.6
1950
1960
1970
1980
1990
2000
Oil
6
5
4
3
2
1
1950
1960
1970
1980
1990
2000
Next, we consider a counterfactual scenario where the cyclical component of real global
GDP is set equal to zero over the entire forecast sample 1952-2009. The resulting dynamic multistep ahead forecasts for the seven minerals are then compared to the baseline forecasts above.
By examining the difference between the two scenarios, which is due solely to the cyclical
component of GDP and the auto-correlation built into the estimated coefficients on the lagged
dependent variables in each equation, we can address the question: In the absence of any given
business-cycle expansion/contraction where would metals prices have been? The forecasted time
paths of the seven minerals with and without the cyclical component of global GDP are shown in
11 the upper portion of each graph in Figure 2. The lower panel (with the scaling on the left axis)
shows the difference between the two scenarios – that is, the business cycle components of the
mineral prices.
Figure 2
Model Simulations With and Without
Global GDP Cyclical Component: Panel 1
Aluminum
Global GDP - Cyclical Component
.03
5.6
.02
5.2
.01
.2
4.8
.1
4.4
.00
-.01
.0
-.02
-.1
-.03
50
55
60
65
70
75
80
85
90
95
00
50
05
55
60
65
Copper
70
75
80
85
90
95
00
05
Nickel
6.0
2.4
5.6
2.0
.12
.2
5.2
.1
.08
1.6
.04
4.8
.00
1.2
.0
-.04
-.1
-.08
50
55
60
65
70
75
80
85
90
95
00
05
50
55
60
65
70
75
80
85
90
95
Forecast with Business Cycle Component
Forecast Without Business Cycle Component
Business Cycle Component of Mineral Price
12 00
05
Model Simulations With and Without
Global GDP Cyclical Component: Panel 2
Tin
Lead
8.0
5.2
7.5
4.8
7.0
.2
.1
4.4
.3
6.5
4.0
.2
6.0
.1
5.5
3.6
.0
.0
-.1
-.2
-.1
50
55
60
65
70
75
80
85
90
95
00
50
05
55
60
65
70
75
80
85
90
95
00
05
Oil
Zinc
5.0
5.0
4.5
4.8
4.0
.08
4.6
.3
3.5
4.4
.2
3.0
.1
2.5
.04
4.2
.0
.00
-.1
-.2
-.04
50
55
60
65
70
75
80
85
90
95
00
05
50
55
60
65
70
75
80
85
90
95
00
05
Figure 2: Model Simulations With and Without Global GDP Cyclical Component for Six
LME Metals and Crude Oil: The top lines in each graph provide model forecasts of mineral
prices with and without the business cycle component of global GDP. “Without” means that the
business-cycle component of global GDP is set to zero. The bottom line is the difference
between the top lines at any point in time and is percentage deviation of the mineral price from
its trend.
The business cycle components for each mineral in Figure 2 are collected in Figure 3
along with the cyclical component of global GDP. Note the strong positive correlation among the
minerals’ cyclical components. Their amplitudes are much larger than the cyclical component of
13 global GDP, reflecting the large estimated coefficients of the yc in the mineral price equations in
Table 1. There is also a strong positive correlation between the various mineral cycles and the
macroeconomic business cycle, supporting the common view that mineral prices have strong comovements and are strongly pro-cyclical. This figure shows us how much higher (lower) each
mineral price was during various business cycle booms and busts. For the interested reader, the
numerical values underlying the Figure are shown in Appendix A. Table 3 below contains an
excerpt with the 1973-1974 and 2007-2008 boom episodes, which stand out in Figure 3.
Figure 3
Dynamic Effects of Cyclical Component of
World GDP on Real Metal and Oil Prices
Percentage Deviations from Trend (in decimals)
.28
World GDP Cyclical Component
Copper
Tin
.24
.20
Crude Oil
Nickel
Zinc
Aluminum
Lead
.16
.12
.08
.04
.00
-.04
-.08
-.12
-.16
50
52
54
56
58
60
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
94
96
98
00
02
04
06
08
As Table 3 shows, the cyclical component of global GDP was 2.1 percent in 1973; i.e. global
real GDP was 2.1 percent above its long-run trend. The global expansion resulted in crude oil
14 prices being 14.3 and 19.2 percent above the level they would have been at in the absence of the
business cycle expansion in 1973 and 1974, respectively. Copper prices, say, were 9.1 and 12.9
percent higher over the same two years. The effects on the other metals were also large, with the
exception of nickel and zinc.
During the global cyclical upswing in 2007-2008, before the abrupt slowdown in 2009,
crude oil prices surged to 27.6 percent above trend. The metals, too, experienced double digit
increases relative to trend, then moved into negative territory with the global slowdown. Again
zinc is an exception.
Table 3: Cyclical Components During the 1973-1975 and 2007-2009 Episodes
GlobalGDP obs Cyclical Crude Oil ... Alum.
Copper
Nickel
Lead
Tin Zinc
... ... ...
...
...
...
... ...
1973 0.021 0.143 0.089
0.091
0.052
0.098
0.132 0.013
1974 0.006 0.192 0.100
0.129
0.073
0.122
0.173 0.043
1975 ‐0.016 ‐0.023 ‐0.044
‐0.011
‐0.012
‐0.026
‐0.029 0.018
... ... ...
...
...
...
... ...
2007 0.014 0.068 0.064
0.051
0.035
0.057
0.072 0.000
2008 0.019 0.276 0.145
0.172
0.100
0.172
0.244 0.049
2009 ‐0.024 ‐0.039 ‐0.045
‐0.007
‐0.007
‐0.029
‐0.036 0.024
... 15 V.
Conclusion
Metal and oil prices are notoriously volatile. It is commonly observed that they have
strong co-movement at business cycle frequency. Our empirical work provides formal
econometric support for these claims. We find that (with some puzzling exceptions involving
nickel and zinc) that the real prices of LME metals and crude oil are much more responsive to
cyclical than to trend movements in global real GDP. The business cycle upswing in the early
part of the 21st century and the subsequent global slowdown explain a significant portion, but
certainly not all of the sharp price movements that captivated the financial markets during this
period.
The results of our counter-factual simulation help to explain the cause of mineral price
movements during two separate commodity boom periods. During 1973-74, the cyclical
components of crude oil and copper help elevate prices above the long-term trend by 14-19
percent and 9-12 percent, respectively. Similar price movements were experienced in the most
recent boom with oil and copper prices rising 27% and 17% above long-term trends by early
2008.
For those of us who believe that a long-term super cycle for nonrenewable resource
prices is in progress, the results in this paper on business cycle movements suggest that it would
be premature to exclaim “So Long Super Cycle?!” after witnessing the metals price retreat in
2008-9. It is quite possible that business cycle movements in minerals prices could obscure
long-term super cycle behavior for a period of time. Indeed, that is what we believe happened
during late 2008 and early 2009. Interestingly, metal and oil prices have rebounded sharply since
their troughs in 2008-9, which is consistent with our interpretation of events.
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18 Appendix A: Cyclical Effects Shown in Figure 3 obs Global GDP Cyclical Crude Oil Alum.
Copper
Nickel
Lead
Tin
Zinc 1950 ‐0.00 NA NA
NA
NA
NA
NA
NA 1951 0.01 NA NA
NA
NA
NA
NA
NA 1952 0.00 0.04 0.02
0.02
0.01
0.02
0.03
0.01 1953 0.00 0.06 0.03
0.03
0.02
0.03
0.05
0.01 1954 ‐0.01 ‐0.10 ‐0.06
‐0.06
‐0.03
‐0.06
‐0.09
‐0.01 1955 0.00 ‐0.07 ‐0.04
‐0.05
‐0.03
‐0.04
‐0.06
‐0.02 1956 0.00 ‐0.01 0.02
0.00
0.00
0.01
0.00
‐0.01 1957 0.00 0.02 0.03
0.03
0.02
0.03
0.03
0.00 1958 ‐0.00 ‐0.04 ‐0.01
‐0.01
‐0.00
‐0.01
‐0.02
0.00 1959 0.00 ‐0.03 ‐0.02
‐0.01
‐0.01
‐0.01
‐0.02
‐0.00 1960 0.01 0.09 0.04
0.05
0.02
0.05
0.08
0.01 1961 ‐0.00 0.05 0.02
0.03
0.02
0.03
0.04
0.01 1962 ‐0.00 ‐0.01 ‐0.02
‐0.01
‐0.01
‐0.02
‐0.02
0.00 1963 ‐0.01 ‐0.15 ‐0.08
‐0.09
‐0.05
‐0.10
‐0.14
‐0.02 1964 0.01 ‐0.06 ‐0.02
‐0.04
‐0.02
‐0.03
‐0.05
‐0.02 1965 0.01 0.02 0.04
0.03
0.02
0.03
0.04
‐0.00 1966 0.01 0.12 0.07
0.09
0.05
0.08
0.11
0.02 1967 ‐0.01 0.03 0.00
0.03
0.01
0.02
0.03
0.02 1968 ‐0.00 ‐0.01 ‐0.04
‐0.02
‐0.02
‐0.02
‐0.02
0.00 1969 ‐0.00 ‐0.02 ‐0.02
‐0.03
‐0.02
‐0.02
‐0.03
‐0.01 1970 ‐0.00 ‐0.02 ‐0.01
‐0.02
‐0.01
‐0.02
‐0.02
‐0.01 1971 ‐0.01 ‐0.09 ‐0.03
‐0.05
‐0.02
‐0.05
‐0.07
‐0.02 1972 ‐0.00 ‐0.12 ‐0.04
‐0.05
‐0.03
‐0.06
‐0.09
‐0.02 19 1973 0.02 0.14 0.09
0.09
0.05
0.10
0.13
0.01 1974 0.01 0.19 0.10
0.13
0.07
0.12
0.17
0.04 1975 ‐0.02 ‐0.02 ‐0.04
‐0.01
‐0.01
‐0.03
‐0.03
0.02 1976 ‐0.01 ‐0.09 ‐0.09
‐0.08
‐0.06
‐0.08
‐0.10
‐0.02 1977 ‐0.00 ‐0.12 ‐0.06
‐0.09
‐0.05
‐0.08
‐0.11
‐0.03 1978 0.00 ‐0.05 0.01
‐0.02
‐0.00
‐0.01
‐0.03
‐0.02 1979 0.01 0.06 0.07
0.06
0.04
0.06
0.07
0.01 1980 0.00 0.09 0.05
0.07
0.04
0.07
0.09
0.03 1981 0.00 0.08 0.02
0.05
0.02
0.04
0.06
0.03 1982 ‐0.01 ‐0.05 ‐0.05
‐0.04
‐0.03
‐0.04
‐0.05
‐0.00 1983 ‐0.01 ‐0.12 ‐0.07
‐0.09
‐0.05
‐0.08
‐0.11
‐0.03 1984 0.01 ‐0.01 0.01
‐0.02
‐0.00
‐0.00
‐0.01
‐0.02 1985 0.01 0.05 0.05
0.04
0.03
0.04
0.06
0.01 1986 ‐0.00 0.03 0.02
0.03
0.02
0.03
0.03
0.01 1987 ‐0.00 ‐0.03 ‐0.03
‐0.01
‐0.01
‐0.02
‐0.03
0.00 1988 0.00 ‐0.02 ‐0.02
‐0.02
‐0.02
‐0.01
‐0.02
‐0.00 1989 0.00 0.02 0.01
0.01
0.00
0.01
0.01
‐0.00 1990 0.00 0.03 0.02
0.02
0.01
0.02
0.03
0.00 1991 ‐0.00 ‐0.01 ‐0.01
‐0.00
0.00
‐0.01
‐0.01
0.00 1992 ‐0.00 ‐0.01 ‐0.01
‐0.01
‐0.01
‐0.01
‐0.02
‐0.00 1993 ‐0.00 ‐0.05 ‐0.02
‐0.03
‐0.02
‐0.03
‐0.04
‐0.01 1994 0.00 ‐0.03 ‐0.01
‐0.01
‐0.01
‐0.01
‐0.02
‐0.01 1995 0.01 0.04 0.03
0.03
0.02
0.03
0.04
0.00 1996 0.00 0.05 0.03
0.04
0.02
0.03
0.05
0.01 1997 0.00 0.08 0.02
0.04
0.02
0.04
0.06
0.02 1998 ‐0.01 ‐0.07 ‐0.05
‐0.05
‐0.03
‐0.05
‐0.07
‐0.00 1999 ‐0.01 ‐0.14 ‐0.07
‐0.09
‐0.05
‐0.09
‐0.13
‐0.03 20 2000 0.01 0.01 0.03
0.00
0.00
0.01
0.02
‐0.01 2001 0.01 0.09 0.07
0.07
0.05
0.07
0.09
0.01 2002 ‐0.00 0.07 0.03
0.05
0.03
0.05
0.06
0.02 2003 ‐0.00 0.02 ‐0.02
0.01
‐0.00
0.00
0.01
0.01 2004 ‐0.00 ‐0.03 ‐0.04
‐0.03
‐0.02
‐0.03
‐0.04
‐0.00 2005 ‐0.01 ‐0.12 ‐0.06
‐0.08
‐0.04
‐0.07
‐0.10
‐0.02 2006 ‐0.00 ‐0.11 ‐0.03
‐0.06
‐0.03
‐0.06
‐0.09
‐0.03 2007 0.01 0.07 0.06
0.05
0.04
0.06
0.07
0.00 2008 0.02 0.28 0.15
0.17
0.10
0.17
0.24
0.05 2009 ‐0.02 ‐0.04 ‐0.05
‐0.01
‐0.01
‐0.03
‐0.04
0.02 21