Oil Price Shocks, Firm Uncertainty and Investment
by
Kiseok Leea, Wensheng Kangb, Ronald A. Rattic*
a
Department of Economics, Kyung Hee University, Seoul, Korea,
Department of Economics, Kent State University, New Philadelphia, Ohio, USA
c
School of Economics and Finance, University of Western Sydney, NSW, Australia
b
*
Corresponding author: [email protected]
School of Business and Economics
University of Western Sydney
NSW, Australia
Telephone: +61 2 9685 9346
Fax: +61 2 9685 9105
1
Oil Price Shocks, Firm Uncertainty and Investment
ABSTRACT
The intuition in this paper is that an oil price shock has a greater effect on delaying a
firm’s investment the greater the uncertainty faced by that firm. A rise in the composite
variable of oil price shock and firm uncertainty significantly delays investment. While
growth in real oil price is not significant, the time-varying individual firm’s stock price
volatility identified with oil price shock dates is highly significant. Among 12 measures,
the uncertainty measure obtained by multiplying an indicator function, that takes 1 if
Hamilton (1996) type oil price shock is greater than 0 and 0 otherwise, to a firm’s stock
price volatility provides the largest absolute t-values in investment equations. A rise in
firm uncertainty decreases both short- and long-run firm investment, indicating that oil
price shocks affect the U.S. economy because they identify important geopolitical or
economic events that have significant implications for the future U.S. economy.
Key words: Oil price shocks, firm uncertainty, stock price volatility, investment
JEL Classification: E22, G31, Q43
2
Oil Price Shocks, Firm Uncertainty and Investment
1. Introduction
The literature on the relationship between oil price shocks and economic and
financial activity has grown quite large in recent years and has covered a number of
issues. Early papers by Hamilton (1988), Mork (1989), Lee, Ni, and Ratti (1995), Hooker
(1996), among others, report a negative association between oil price shocks and
economic activity. These authors claim that OPEC driven supply disruptions and oil price
hikes due to international military conflicts provide strong evidence for the exogeneity of
oil price shocks. Papers by Kilian (2008a, 2008b, 2009) and Kilian and Park (2009) argue
that the size of the impact of oil price shock on economic activity differs by the nature of
oil shock, i.e., demand driven, driven by speculative demand, or driven by supply
disruptions. A recent study by Fukunaga, Hirakata, and Sudo (2010) finds that the effects
of demand shocks that are specific to the global oil market do not show consistent
characteristics across countries. For the U.S. economy the demand shocks specific to the
global oil market act as negative supply shocks, while for Japanese economy, the same
shocks act as positive demand shocks. A number of papers find that large oil price
movements have significant impact on major macroeconomic variables such as GDP,
inflation, and productivity (see, for instance, Barsky and Kilian (2004)). Hamilton (2005)
argues that nine out of ten U.S. recessions that occurred between 1948 and 2001 were
preceded by a substantial increase in the price oil. Engemann, Kliesen, and Owyang
(2010) find that oil price shocks significantly increase the probability of recessions in a
number of countries. In particular, an average oil price shock increases the probability of
recession in the U.S. by about 60 percentage points over the following year.
3
An associated literature has analyzed the relationship between oil price changes and
stock returns. Huang, Masulis, and Stoll (1996) provided evidence of significant causality
running from oil future prices to stock returns of individual firms, Jones and Kaul (1996)
investigated whether oil price shocks trigger the movements of international stock
markets and the movements can be justified by future cash flows changes. Sadorsky
(1999) presents evidence that an oil price shock has a statistically significant negative
effect on stock returns. More recently, Dreisprong, Jacobsen, and Maat (2008) analyzed
whether monthly oil price changes are a predictor of world stock prices. They found that
statistically significant predictive power from 12 of the 18 developed economies and
slightly weaker power from 30 emerging economies. Pollet (2005) presented evidence
that expected changes in oil prices have forecast power for excess market returns as well
as excess returns of most U.S. industries.
The transmission mechanism by which oil price shocks affect economic activity has
also been investigated. Edelstein and Kilian (2007) argue that oil price shocks may affect
nonresidential fixed investment through either a ‘supply channel’ in which an increase in
the cost of production driven by an increase in real oil prices decreases production, or a
‘demand channel’ in which consumer spending falls in response to rising energy prices.
Lee and Ni (2002) in analysis of U.S. sectoral output provide evidence that oil prices
shocks work not through a supply channel but by demand channel. In an investigation of
the effects of oil price shocks on stock returns, Kilian and Park (2007) argue oil shocks
affect the macroeconomy through changes in the final demand for goods and services
since the industries that show the strongest responses to oil demand shocks are the
automotive, retail, consumer goods, and tourism-related industries. Gogineni (2009) finds
4
that the extent to which stock returns are negatively affected by oil price changes depends
largely on the nature of the industry, i.e., whether the industry uses oil as an input of
production or its main customers use oil as an input of production.
Advocates of the view that oil price shocks significantly influence the economy
have always been confronted by the fact that the share of oil use in the U.S. GDP is quite
small. The share of energy use in nominal GDP has been below four percent since 1983
and that of crude oil has been much smaller.1 In addition, sometimes a surge in the real
oil price is accompanied by a robust growth in GDP. Recent data suggest that both the
real price of oil and GDP rose persistently during 2003-08.2 When the world economy
collapsed in the late 2008 as a result of the financial crisis, so did the real oil price. The
positive co-movement in the real price of oil and real GDP cannot be explained by shifts
in the supply curve driven by the rise in production costs and/or the decline in
productivity.
In this paper we wish to build on related strands in the literature that focus on
investment decisions by firms. The intuition in our approach is that an oil price shock has
a greater effect on delaying a firm’s investment the greater the uncertainty faced by that
firm. In well-known papers Bernanke (1983) and Pindyck (1991) argue that changes in
energy prices create uncertainty about future energy prices, causing firms to postpone
irreversible investment decisions. This channel is consistent with firms reacting not only
to the uncertainty about future production costs but also to the uncertainty about future
sales. The other strand in the literature represents the view advanced by Leahy and
1
There were only three years, 1980, 1981, and 1982, since 1979 in which energy use in nominal GDP
exceeded four percent. See, for instance, Kilian (2007).
2
Kilian (2009b) claims that the surge in the real price of oil during 2003-08 is caused by fluctuations in the
global business cycle, driven mainly by unexpected growth in emerging Asia superimposed on strong
growth in the OECD.
5
Whited (1996), Bloom (2007), Bloom, Bond and Van Reenan (2007) that uncertainty
faced by the individual firm can be represented by its own stock price volatility. We will
construct an oil price shock variable for the individual firm that is the product of an oil
price shock and that firm’s stock price volatility. A large oil price shock driven by oil
supply disruptions, or by a jump in speculative demand, or by a sudden increase in
demand, or by a mixture of these creates uncertainty in future price of oil, future price of
other commodities, and future demand for goods and services. The rise in economy-wide
uncertainty compounds firms’ uncertainty and delays firms’ irreversible investment
decisions and as a consequence, economic activity slows. A firm’s response to the oil
price shock will be larger the greater is individual firm level uncertainty.
This paper analyzes the joint effect of an oil price shock and a firm’s uncertainty
on that firm’s investment using firm level panel data. This focus on firm data is in itself
interesting since most work on the influence of oil price shocks has usually considered
effects on fairly aggregate economic and financial data. It is found that an oil price shock
measured by the rate of change in real oil price does not delay investment by a firm nor is
higher firm uncertainty associated with less investment. However, a rise in the composite
variable of oil price shock and firm uncertainty does significantly delay investment by
that firm, both in the short and in the long-term. This oil price shock associated
uncertainty channel will be for short referred to as the uncertainty channel in much of the
paper.
The empirical evidence obtained from a large set of firm-level data strongly
supports the uncertainty channel of transmission. Among the set of 12 different
uncertainty measures, the uncertainty measure that is obtained by multiplying an
6
indicator function, that takes 1 if Hamilton (1996) type oil price shock is greater than 0
and 0 otherwise, to the stock price volatility of an individual firm provides the largest
absolute t-values in an investment equations. Since oil price shocks are playing only the
role of identifying the shock dates, the size of oil price shock, given that a shock is
identified at that time, turns out to be not very important. Also, the results suggest that a
rise in the firm-level uncertainty decreases both short- and long-run firm-level investment.
This finding indicates that oil price shocks affect the U.S. economy not because the U.S.
economy depends heavily on oil but because they identify important geopolitical or
economic events with consequences for the economy. Although the growth rate of real oil
price is not statistically significant in a firm-level investment equation, various non-linear
transformations of the growth rate of real oil price are shown to significantly affect firm
investment in the long-run.
The paper proceeds as follows. The pattern of oil price shocks since 1971 will be
discussed in section 2. A firm’s oil price shock variable is defined in section 3. The data
and variables are described in section 4. Section 5 presents the empirical model and
results and section 6 concludes.
2. Observations on oil price shocks
Most oil price shock dates coincide with the dates of geopolitical crises such as the
Yom Kippur War in October 1973 and the Arab oil embargo that followed, the Iranian
Revolution in January 1979, the eruption of Iran-Iraq War in September 1980, the Iraqi
invasion of Kuwait in August 1990 and the Gulf War that followed, the Afghan War in
October 2001, and the Iraq War in March 2003 are good examples. Figure 1 shows the
7
real oil price movements and the vertical lines indicate the timing of the outbreak of the
six major historical events. It can be seen that most dates of crisis are followed by sharp
rises in the oil price. Potential exceptions, however, are the Afghan War in October 2001
and the Iraq War in March 2003. Since October 2001, the real oil price dropped for three
consecutive months, but four months after the outbreak of the war price began to rise
significantly. Also, after the eruption of the Iraq War, the oil price decreased
consecutively for two months. A possible reason for the decrease is the expectation that
the U.S. would have improved access to the Iraqi oil reserves after the war.
Shocks to the flow demand of oil associated with the global business cycle are
claimed to be responsible for oil price fluctuations in 1973/74, 1979/89, and the persistent
rise during 2003-2008. On the other hand, the forward-looking oil traders’ speculative
demand for oil is responsible for sudden changes in oil price in 1979 (following the
Iranian revolution), in 1986 (following the collapse of OPEC), in 1990/91 (following the
invasion of Kuwait), 1997-2000 (following the Asian crisis), and in late 2008 (during the
global financial crisis).3 Some of the years identified by demand channel advocates are
also years marked by well-known geopolitical crises such as the October War in 1973 and
the ensuing oil embargo, Iranian Revolution in 1978, Iran-Iraq War in 1980, Gulf War in
1990, and Iraq War in 2003. The geopolitical crises had immediate impact to the real
price of crude oil either by embargo or by disturbances in the crude oil supply channel.
Figure 2 illustrates the growth rate of the real crude oil price in which four out of six
geopolitical events were followed immediately by sharp rises in real oil price. The last
two events, the Afghan War and the Iraq War, are not followed by rises in real oil price.
3
See Kilian (2009b) for detailed discussion.
8
The October War in 1973 was followed by 18.9 percent rise in crude oil price in January
1974 and 25.0 percent rise in February 1974. After the Iranian Revolution in 1979 real oil
price increased by 48.8 percent during nine months following the Revolution. The IranIraq War was accompanied by a short-lived price rise. During the first three months of the
Gulf War the crude price increased by 55.5 percent. Figure 2 also reveals that the
volatility of the oil price growth has increased significantly since the collapse of OPEC
cartel in the early 1986. The collapse of OPEC was accompanied by a more than 30
percent drop in the real price of oil. Since then the variability in the oil price growth rate
increased sharply.
When war broke out in the Middle East the supply of crude oil was interrupted and
the price of oil rose sharply. However, the sharp rise in oil price may not be considered as
the main factor that drives down economic activity since the share of oil use in GDP is
small. Instead, the uncertainty about the future path of the macroeconomy in the wake of
war is what shrinks the overall economic activity. A Middle East military conflict drives
up the price of oil as well as the level of uncertainty about the future course of the
economy. Regardless of the size of the oil or energy sectors in the GDP, oil price shocks
may play the role of a proxy for major geopolitical event and hence be responsible for a
downturn in economic activity.
3. Firm’s oil price shock variable
Since the finding of asymmetric effects of oil price on GDP growth by Mork (1989),
several nonlinear transformations of oil price have been suggested as shocks in the price
of oil. Lee, Ni, and Ratti (1995) used scaled oil price growth in which the growth rate of
9
oil price is divided by its time-varying conditional standard deviation obtained from
GARCH(1,1). The scaled shocks help keep the statistical significance of the effects of oil
price shocks on GDP growth high in the presence of increased volatility in oil price since
1985. Hamilton (1996) proposed the net oil price increase (NOPI) variable that is
obtained by subtracting the maximum oil price of the last four quarters from current oil
price if it is positive and zero otherwise. Both of these measures of oil price shock are
used in this paper for the robustness of empirical results.
The data on the real price oil are obtained by vertically concatenating two series.
The PPI-oil is used for the earlier period, January 1962 to December 1973, and the refiner
acquisition cost of imported crude oil provided by the U.S. Department of Energy is used
for January 1974 to December 2007.4 Both series are deflated using the U.S. CPI.
The real oil price shock at time t is defined as
12
=
if
> max(
,
, …,
)
(1)
= 0 otherwise
where
denotes the growth rate of the real price of oil.
12
is similar in
spirit to Hamilton’s (1996) net oil price increases.5
Since real oil price shocks are considered as proxies of international conflicts or
other events that affect economic uncertainty, the shocks should be converted into a
measure of uncertainty. We use four different ways of mapping energy price shocks into
the measures of uncertainty. The first measure of uncertainty of firm i,
12
, is
obtained by
4
The real oil price data are constructed following Kilian (2009). Since the refiner acquisition cost of
imported crude oil start from January 1974, PPI-oil data are used to extend backward.
5
Hamilton’s (1996) net oil price increases are obtained by subtracting the maximum price of last four
quarters from the current price if the current price is greater than the maximum, and zero otherwise.
Different time spans, such as 6 and 24 months, were also tried but the results were qualitatively the same.
10
12
where s
12
=
(2)
denotes the measure of firm i’s uncertainty obtained from the standard
deviation of daily stock returns of the past 12 months of firm i (Baum et al. (2008)). For
given
the uncertainty measure
12
increases as the magnitude of oil shock
increases and hence the uncertainty faced by firm i is allowed to reflect the size of oil
price shock. U 12
12
where
may be converted into a measure of aggregate uncertainty,
=
12
(3)
denotes the measure of aggregate uncertainty obtained from the standard
deviation of daily returns on the S&P500 index for the past 12 months. Figure 4
illustrates the measure of aggregate uncertainty. Following the October War, there was a
significant rise in the aggregate uncertainty and after the Iranian Revolution and Iran-Iraq
War, there were modest increases in uncertainty. After the Gulf War, however, there was a
huge increase in the aggregate uncertainty. This spike in uncertainty is due to the large
increase in the real oil price and a significant increase in stock price volatility following
the Gulf War as can be seen from a comparison of Figures 2 and 3. It seems that the
Afghan War and the Iraq War were not associated with a rise in the aggregate uncertainty.
The second measure replaces
by the indicator function 1(
>0)
such that the size of oil price shock does not affect the level of uncertainty faced by firm i.
12
= 1(
>0)·
(4)
where 1(·) denotes the indicator function. The uncertainty measure U 12
represents
the firm i’s stock return volatility in the month of oil price shock. Thus, the magnitude of
uncertainty faced by a firm is solely determined by its stock price uncertainty. The
aggregate counterpart of this measure of uncertainty can be obtained from
11
12
= 1(
>0)·
(5)
where s is defined in equation (3) above. The time-series plot of
12
is in Figure
5. The plot shows somewhat different pattern in the size of uncertainty. In particular, the
relative size of uncertainty following the Gulf War is significantly smaller than the one in
Figure 4. However, the levels of uncertainty after the Iranian Revolution and Iran-Iraq
War are greater than those in Figure 4.
The third measure replaces
in equation (1) by scaling real oil price growth
as in Lee, Ni, and Ratti (1995) with
/
=
where
(6)
denotes the time-varying conditional standard deviation of the growth rate of
real oil price obtained from GARCH(1,1). Then, the scaled real oil price shock can be
obtained by
12
=
if
> max(
,
, …,
) (7)
= 0 otherwise
This measure in equation (7) is also similar in spirit to Hamilton’s (1996) net oil price
increases, but the increases are scaled by conditional volatility. As is shown in Lee, Ni,
may represent ‘oil price shock’ better than pgmax 12
and Ratti (1995)
especially when the volatility of oil price is high. Following equation (2), the third
uncertainty measure is defined as
12
=
12
(8)
where again given firm i’s stock price volatility
, the firm i’s uncertainty is
proportional to the scaled oil price shock npgmax 12 . The aggregate uncertainty based
on the scaled oil price shock can be obtained from
12
12
12
=
.
(9)
Figure 6 shows the plot of the aggregate uncertainty
12 . A comparison between
Figure 4 and Figure 6 suggests that the shock dates are almost the same, but the
magnitudes of spike are different. In particular, the level of uncertainty following the
Iran-Iraq War is much higher in Figure 6 than in Figure 4.
The fourth uncertainty measure is defined as
12
12 >0)·
= 1(
(10)
where the magnitude of the scaled oil price shock does not affect the measure of firm i’s
uncertainty. Equations (2) and (4), and (8) and (10) are used to compare the empirical
results that are obtained by the uncertainty measure whose magnitude reflects the size of
oil price shock, i.e., equations (2) and (8), with those that are obtained by the uncertainty
measure whose magnitude does not, i.e., equations (4) and (10). If firm level investments
are affected by the uncertainty variables in equations (2) and (8) more than the
uncertainty variables in equations (4) and (10), then it should be the evidence that not
only uncertainty but also the real price of oil do affect firm investment. If the opposite is
the case, however, then the results suggest that oil price shocks playing the indicator role
in identifying geopolitical events.
The aggregate counterpart of equation (10) can be written as
12
12 >0)· .
= 1(
Figure 7 contains the plot of
12
.
(11)
The relative level of uncertainty on crisis
dates is different from those in Figure 6. For instance, the relative magnitudes of
uncertainty on the October War, the Iranian Revolution, the Iran-Iraq War, and the Gulf
War have decreased significantly when the scaled oil price shocks are not reflected in the
13
uncertainty measure. As a consequence, it is not easy to identify any difference in terms
of the level of uncertainty between the dates of well-known crisis and the dates of nocrisis.
4. Data description and preliminary results
4.1. Data and Variable Specification
The sample data come from three sources. The oil price is the refiner acquisition
cost of imported crude oil as provided by the U.S. Department of Energy since January
1974. We extended the price series back to January 1962 as in Barsky and Kilian (2002)
and Killian (2007). The price is deflated using the U.S. CPI based on January 1983
dollars. Stock returns are from CRSP Daily Stock Combine File. Book value and other
accounting data are from Standard and Poor’s Industrial Annual COMPUSTAT database.
The sample consists of an unbalanced panel of publicly traded manufacturing firms (SIC
code 2000 - 3999) that spans from January 1962 to December 2007 drawn from the
CRSP and COMPUSTAT databases.
We delete observations for which the values for assets, sales, capital stock, or
stock prices are negative, because these observations are likely to be erroneous.
Following Gilchrist and Himmelberg (1998) and Love (2003), we exclude stocks in the
top and bottom 1% of the variable values.
Observations with the values of the
investment-to-capital, cash flow-to-capital, debt-to-capital ratios and Tobins’ Q outside
the 5-95th percentile range are excluded. We also eliminate firms with missing or
inconsistent data and exclude firms with less than 12 months of past return data. The final
data set contains 59113 firm-years pertaining to 3322 firms with complete data for all
14
variables used in the analysis.
Table 1 presents all variables used for data screening and model estimation in this
paper. The daily stock return on the day t, r , is computed as follows:
r = 100 × ln (Pt / Pt-1),
where Pt is the daily stock price on date t. We then utilize the daily stock returns to
compute stock volatilities via a method proposed by Baum et al. (2008). The method
takes the squared first difference of the daily changes in returns divided by the square
root of the number of days intervening between calendar day (t) and (t-1). The estimated
annual volatility of the return series is then defined as the square root of the sum of daily
volatility over past one year. The use of lagged volatilities to capture firm’s uncertainty is
attractive since it provides a forward looking measure of uncertainty caused by different
factors in one scale measure over the investment horizon (Baum et al., 2007, 2008).
4.2. Descriptive Statistics and Preliminary Results
Table 2 reports the real value of all accounting variables across 20 industries
deflated by GDP deflator based on January 1983 dollars. Firms having highest investment
rates are the industry supplying machinery and except electrical products and the industry
providing electrical equipments and supplies. These firms also have highest volatilities of
stock returns among all industries, suggesting the positive linkage between investment
dynamics and uncertainty documented in Bloom et al. (2007) and Baum et al. (2008)
among others. Petroleum refining industry has relatively higher assets, capital stocks,
sales revenues and stock returns but lower investment rates, rendering the high
profitability in the industry. Table 3 presents the summary statistics of all variables used
15
over the fiscal years 1962 to 2007. The results show that investment rates increase
steadily from 1960s to 1990s and decrease substantially from the late 1990s to 2000s.
The movement of stock returns exhibits a similar pattern over time. During the period of
investment fall, the fluctuation of both stock volatilities and real oil price growth are
more pronounced (e.g., the stock volatility reached the highest at 20.53% in 2000 and the
oil shock reached the highest at 7.71% in 1999).
We first investigate the link between S&P 500 volatilities and oil price shocks.
Table 4 shows the results of four simple regressions in which the dependent variable is
the return volatility on the S&P 500 index and four independent variables are tried one at
a time.
denotes the growth rate of real oil price at t.
24
6
,
12 ,
denote the growth rate of real oil price at t if the growth rate at t is greater
than the maximum of the last 6, 12, and 24 months, respectively, zero otherwise. The first
column of Table 4 shows that the growth rate of the real oil price,
significant with its t-value reaching almost 9. All three oil shock variables,
12 ,
, is highly
6 ,
24 , are statistically significant at least at the five percent level.6
All four coefficients are positive implying that a rise in oil price shock predicts an
increase in the aggregate macroeconomic uncertainty. The results in Table 4 suggest that
the oil shock variables defined in this paper have significant explanatory powers for the
aggregate uncertainty. Thus, the hypothesis that oil shocks act as proxy for the rise in
economy-wide uncertainty is supported by the data.
5. The Empirical Model and Results
6
The level of statistical significance deteriorates as the number of months in defining an oil shock
becomes greater. This is because as the number of months increases the number of non-zero oil shocks
decreases.
16
5.1. The effects of firm-level uncertainty on firm-level investment
In this section, we use a standard investment model to analyze the relationship
between the firm-level uncertainty and the firm-level investment behavior using the
following model:
+
=
+
∆
+
∆
+
+
+
∆
+
+
+
+
is gross investment of firm i in period t,
where
cash flow,
denotes the log of real sales,
denotes the error-correction term, ∆
+
+
and
(12)
is the actual capital stock,
is the first-difference of log of real sales,
is the first-difference of
are unobserved firm-specific and time-specific effects,
firm-specific depreciation rate, and
is
is the log of capital stock,
denotes the firm-specific uncertainty of firm i in period t, ∆
uncertainty,
∆
is the
is the serially uncorrelated error term. The firm-
level investment model (12) above is used in Bloom et al. (2007) and Baum et al. (2008),
among others.
The OLS estimation results of equation (12) are reported in Table 5. Column (1) is
the standard OLS estimate without considering firm specific effects and time effects. The
coefficients of error correction term, change in uncertainty, and lagged uncertainty are
incorrectly signed. Column (2) specifies the firm specific effect in the model and Column
(3) includes both the firm-specific and time-specific effects in the model. The coefficients
of all variables are then correctly signed and statistically significant. The goodness
measure shows that the model that includes both firm and time effects is the best the three
specifications. The OLS estimate does not take into account the possibility that the proxy
for the uncertainty may contain unmeasured errors or endogenous variables are included
17
as explanatory variables. In order to avoid this potential difficulty, GMM is used to
estimate the model parameters and the results are contained in Table 6.7
The system GMM estimator is developed by Arellano and Bover (1995) and Blundell
and Bond (1998) that combines a system of equations in first differences using lagged
levels of endogenous variables as instruments with equations in levels for which lagged
differences of endogenous variables are used as instruments. Following Bloom et al.
(2007), such variables as sales, cash flows, and uncertainty are considered as endogenous.
To allow for long delays in oil-shock transmission, a set of instruments with a lag length
of 2 - 14 is used in the first-difference equations. Instrument validity is tested using a
Sargan-Hansen test of the over-identifying restrictions.
Table 6 reports the results of GMM estimation. The dependent and independent
variables are the same as those in Bloom et al. (2007) except for the definition of the
firm-level uncertainty.8 In Bloom et al. the within-year standard deviation of monthly
returns is used, whereas the standard deviation of daily returns of the past year is used in
this paper. Regardless of the variable chosen for the uncertainty measure, the lagged
investment rate
/
/
, sales growth ∆
, cash flow
/
, lagged cash flow
, and sales growth squared ∆
, error correction term
all
have the correct sign and are highly significant with the minimum t-value 4.40. For all
five models, the Lagrange multiplier tests for the presence of the second-order serial
correlation in the error term cannot reject the null hypothesis of no serial correlation. For
all five models, Sargan-Hansen tests do not reject the validity of the overidentifying
restrictions. Goodness-of-fit statistics suggest that all five models have pretty good
7
The system GMM estimation module in STATA 11 is used for the estimation.
8 Note that the lagged investment rate is not used in Bloom et al. (2007).
18
explanatory power for the firm-level data used in this paper.
Column (1) contains the results when the first-difference of the growth rate of real
oil price ∆
is used for the change in uncertainty ∆
are used in place of the lagged uncertainty
variables ∆
and
, and the lagged growth rate
. The results show that the two
are not statistically significant indicating the firm-level
investment does not respond to real oil price movements. Column (2) contains the results
obtained from a model in which ∆
firm i’s stock price volatility ∆
and
are substituted for by the change in
and the lag of firm i’s stock price volatility
. The
coefficient estimate of the change in volatility is positive and significant at the five
percent level. Baum et al. (2008) interpret the positive coefficient on the uncertainty as
the existence of a real option value for managers regarding investment decisions. In other
words, an individual firm possesses a greater opportunity to expand its presence in the
market (p. 286). Our finding shows that such effect is only a short-run influence on the
firm capital accumulation. The coefficient of the lag of stock price volatility is negative
but is not statistically significant. This finding is partially consistent with the empirical
result of Bloom et al. (2007) (in their Table 5) that neither the change of nor the lag of
volatility is statistically significant.9 An intuitive explanation about the lack of statistical
significance of firm-level uncertainty is that large movements in stock price are not
related with economic downturns during 1962.01 ~ 2007.12. For instance, the four largest
volatilities of the S&P 500 index were observed in 1987.10, 1997.10, 1998.09, and
2002.10 as can be seen from Figure 3. These volatility spikes are not associated with any
9
The coefficient of the change in uncertainty is negative but insignificant in Bloom et al. (2007). Our
findings cannot be directly contrasted with those of Bloom et al. as our uncertainty measures are based on a
different methodology proposed by Baum et al. (2008). In addition, our model includes the lagged
investment rate which they exclude and our sample period is longer that spans from 1962 to 2007.
19
of the six economic recessions during the sample period identified by NBER.10 Thus, the
results in columns (1) and (2) indicate that the growth rate of real oil price does not affect
firm-level investment and the individual firm’s stock price volatility affects firm-level
capital accumulation only in the short-term. However, the uncertainty measures
constructed using both oil price shocks and individual firm’s stock price volatility show
high statistical significance as is explained below
Column (3) contains the results obtained with the uncertainty variable
Unlike the results in column (1), both the change in uncertainty ∆
lagged uncertainty
12
12
12
.
and the
have the correct sign and are statistically significant at
least at the five percent significance level. The absolute t-value of the change in
uncertainty is slightly greater than 2, while that of the lagged uncertainty is almost 4. The
results in columns (1) and (3) provide evidence that the firm-level investment is affected
not by movements in real oil price but by the fluctuations in the firm-level uncertainty in
conjunction with oil price shocks. The statistical significance of both the change in
uncertainty ∆
and the lagged uncertainty
indicates that the firm-level
uncertainty has both short- and long-run effects on capital accumulation. The interaction
*∆
term between uncertainty and sales growth
to the fact that the two variables,
and ∆
is not significant. This may be due
, move in an opposite direction and hence
the product lose its statistical significance.11 The results in column (3) are somewhat in
contrast to those in Bloom et. al. (2007) in that while the uncertainty variables are not
statistically significant in the latter, the interaction term is. The main reason for this
10
It is assumed that the individual firm’s stock price volatility mimics the volatility of the S&P 500 index.
When the uncertainty
is high and the sales growth rate ∆
becomes negative, the absolute value
of
*∆
can be large. It should be noted, however, that since the data are annual, negative growth rates
should be rare during the sample period 1962~2007.
11
20
difference may lie in the different definitions of the measure of uncertainty. While the
interaction between real oil price shocks and stock price volatility is utilized in the
definition of uncertainty in this paper, simple stock price volatility is used in Bloom et al.
As a result, such large stock price movements as in October 1987, October 1997,
September 1998, July 2002, and October 2002 that are not associated with economic
recessions played major role in making the relationship between the firm-level
uncertainty and the firm-level investment statistically insignificant.
The results in column (3) provide only a partial support for the hypothesis in this
paper in that oil price itself does not affect real activity. This is because the magnitude of
12
the uncertainty variable
is proportional to the size of oil price shock. In order to
find evidence for the hypothesis, the magnitude of uncertainty should be independent of
the size of oil price shock.
The results in column (4) are obtained using the uncertainty measure
12
which is obtained by multiplying the indicator function, that takes 1 if the oil shock
variable
12
is greater than 0 and 0 otherwise, to the firm-level uncertainty
Thus, the magnitude of the uncertainty
12
.
is independent of the size of oil price
shock. The single role that an oil price shock does is the identification of the date in
which the firm-level uncertainty is not zero. The results in column (4) provide a strong
support for the hypothesis. Both the changes in the uncertainty ∆
uncertainty
show the correct sign and their absolute t-values are significantly
greater than those in column (3). The absolute t-value of ∆
3.00, and that of
and the lagged
increases from 2.14 to
increases from 3.89 to 6.00. The fact that stronger evidence
obtains when the size of oil price shock is eliminated from the uncertainty measure
21
confirms the claim that uncertainty, not oil price, is what affects economic activity.
12
Column (5) contains the results obtained with the uncertainty measure
that is constructed by multiplying the scaled oil price shock
12
to firm i’s
. In terms of the size of the absolute t-value of the two
stock price volatility
uncertainty variables, the results in column (5) are worse than those in column (4). While
the lagged uncertainty
12
in uncertainty ∆
is not. This suggests that the oil price shock advocated by
12
is significant with absolute t-value 3.25, the change
Hamilton (1996) works better than the scaled oil price shock advanced by Lee, Ni, and
Ratti (1995) for the particular panel data set used in this paper. When the uncertainty
12
is substituted by
12
similar results obtains as can be seen from
column (6). A significant difference between the results in columns (5) and (6) is the
decline in the absolute t-value of the lagged uncertainty from 3.25 to 1.50.12
5.2. The effects of oil price shocks on firm-level investment
How the firm-level investment react to oil price shocks is an interesting question
and worth investigation. As mentioned above, column (1) of Table 6 suggests that the
growth rate of real oil price does not affect firm-level investment. However, since the oil
price shocks that are widely used in practice are obtained through non-linear
transformations of the price of oil or the growth rate of oil price, the results in column (1)
is not a reliable indicator to what would obtain with oil price shocks. In place of the
uncertainty variables ∆
6
12
12 ,
and
, four oil price shock variables,
6 , and
12
are used one at a time in
Qualitatively same results as those in Table 6 were obtained with the uncertainty measures,
6 ,
24 , and
24 .
22
6
,
the investment equation. The first two are Hamilton’s transformation applied to the
growth rate in real oil price and the last two are Hamilton’s transformation applied to the
scaled real oil price growth rate that is obtained by dividing the growth rate of oil price
by its conditional standard deviation as in Lee, Ni, and Ratti (1995).
Table 7 contains the results obtained from the oil price shock variables in the
investment equation. The oil price shocks
6
12
and
show
statistical significance when they are lagged, but the changes in these variables are not
statistically significant. Thus, those oil price shocks only affect long-run firm-level
capital accumulation. The two oil price shocks,
6
and
12
obtained from scaled oil price growth yield similar results. The empirical results in Table
7 provide evidence for the fact that appropriately transformed oil price growth rates affect
individual firm’s long-run investment behavior.
6. Conclusion
This paper provides evidence for the effects of oil price shock driven firm-level
uncertainty on the firm-level investment. Properly chosen oil price driven firm-level
uncertainty measures are shown to affect the short- and long-run firm-level capital
accumulation. The nature of the oil price shock driven uncertainty suggests that the firmlevel investment is not responding to the size of oil price shock. Instead, the uncertainty
measured by individual firm’s stock price volatility is the crucial element that provides
statistical significance in both the short and long-run. This finding is referred to as the
uncertainty channel of transmission of oil price shocks. Oil price shocks are playing the
role of proxy for significant geopolitical events and the variable that impacts economic
23
activity is the uncertainty faced by an individual firm. Although the growth rate of real oil
price is not statistically significant in a firm-level investment equation, various non-linear
transformations of the growth rate of real oil price are shown to significantly affect firm
investment in the long-run.
The empirical evidence obtained from a large set of firm-level data strongly
supports the uncertainty channel of transmission. The time-varying individual firm’s
stock price volatility that is identified with oil price shock dates is highly significant.
Among the set of 12 different uncertainty measures, the uncertainty measure that is
obtained by multiplying an indicator function, that takes 1 if Hamilton (1996) type oil
price shock is greater than 0 and 0 otherwise, to the stock price volatility of an individual
firm provides the largest absolute t-values in an investment equations. Since oil price
shocks are playing only the role of identifying the shock dates, the size of oil price shock
turns out to be not very important. Also, the results suggest that for a given oil price
shock and/or at a given oil price shock date, a rise in the firm-level uncertainty decreases
both short- and long-run firm-level investment. This finding indicates that oil price
shocks affect the U.S. economy not because the U.S. economy depends heavily on oil but
because they identify important geopolitical or economic crisis that have significant
implications for the future U.S. economy. The proposed measure for the firm-level
uncertainty is useful in at least in two aspects. First, the uncertainty measure reflects the
uncertainty faced by a firm since it is based on the time-varying volatility of its stock
price. Secondly, oil price shocks are matched with individual firm’s stock price volatility
in the construction of the firm-level uncertainty such that those stock price volatilities
that are unrelated with oil price shocks are eliminated in the construction. The results
24
suggest that properly defined oil price shocks are highly significant in determining both
short- and long-run firm-level capital accumulations.
25
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28
Table 1. Variable definitions
Variable
Acronym
From the COMPUSTAT (1962 – 2007)
Total assets
TA
Capital stock
K
Sale
S
Investment
I/K
Net Sales
S/K
Cash Flow
CF/K
Tobin’s Q
TQ
Definition
(MM$ = million U.S. dollars)
Total assets at the beginning of the period (MM$)
Net Property, Plant and Equipment. (MM$)
Net sales at the end of period t (MM$)
Net Capital Expenditure scaled by capital(t-1)
Net sales at the end of period t scaled by capital
Income Before Extraordinary Items+Depreciation and Amortization
scaled by capital (MM$)
Market value+book value of assets minus common equity and deferred
taxes scaled by the book value of assets
Compustat Data Item
DATA6
DATA8
DATA12
DATA30/DATA8
DATA12/DATA8
(DATA18+DATA14)/DATA8
(DATA6+DATA199*DATA25
-DATA74-DATA60)/DATA6
From the CRSP (1962 - 2007) (Percentage)
Return
r
The first difference of natural logarithm of daily stock prices
Volatility
σ
The square root of the sum of daily volatility over past one year (see Baum et al., 2008)
From the U.S. Department of Energy (1974 - 2007) (Extended back to 1962 and deflated by CPI based on January 1983 dollars)
Price growth rate
pg
The first difference of natural logarithm of the refiner acquisition cost of imported crude oil
Notes: The sample is an unbalanced panel on individual manufacturing firms (with SIC 2000-3999) from 1962 to 2007. Firm-level data is eliminated if a firm has three or
less years of coverage, if there are missing values for investment, capital stock, sales, and cash flow, and if there are observations with negative values for assets, sales, or
capital stock. In addition, we follow Gilchrist and Himmelberg (1998) and Love (2003) to exclude observations with I/K > 2.5, S/K > 20, and outliers in the top and bottom
5% of the accounting data from COMPUSTAT database. We also eliminate firms with missing or inconsistent data and exclude firms with less than 12 months of past
return data from CRSP database. Stocks in the top and bottom 1% of the variable values are also excluded. The final data set contains 40823 firm-years pertaining to 3322
firms with complete data for all variables used in the analysis.
29
Table 2. Descriptive statistics across industries
TA
K
S
CF/K
I/K
S/K
TQ
r
σ
Number of
observations
Percent of
observations
Food and kindred products
15.570
5.821
22.983
0.240
0.254
5.095
1.496
8.297
13.203
4661
7.888
Industry
SIC
Industry name
1
20
2
21
Tobacco manufactures
37.524
9.494
40.672
0.463
0.228
5.734
1.823
7.110
9.489
216
0.366
3
22
Textile mill products
5.221
1.841
7.057
0.224
0.249
4.915
1.051
0.801
14.517
1675
2.835
4
23
Apparel and fabrics-based products
5.532
1.111
6.603
0.363
0.275
9.060
1.314
3.765
13.884
1665
2.818
5
24
Lumber and wood products
8.384
4.032
8.006
0.230
0.260
5.780
1.327
-0.886
14.867
1141
1.931
6
25
Furniture and fixtures
5.437
1.487
8.115
0.316
0.255
6.102
1.292
3.137
13.473
1312
2.220
7
26
Paper and allied products
20.826
11.806
19.725
0.224
0.228
3.084
1.294
3.157
12.068
2459
4.161
8
27
Printing and publishing
9.373
2.504
8.629
0.335
0.272
5.268
1.597
6.086
12.382
2583
4.371
9
28
Chemicals and allied products
15.458
5.994
15.431
-0.025
0.266
4.302
2.239
0.961
14.614
7516
12.719
10
29
Petroleum refining
34.426
20.669
38.170
0.181
0.230
2.815
1.248
12.203
13.179
1024
1.733
11
30
Rubber and plastics products
6.616
2.376
8.190
0.227
0.268
4.791
1.349
9.286
14.932
2548
4.312
12
31
Leather and leather products
3.050
0.608
5.170
0.431
0.277
9.779
1.223
-0.517
14.021
520
0.880
13
32
Stone, clay, glass, and concrete
8.719
4.290
8.457
0.180
0.224
3.019
1.158
0.765
12.919
1759
2.977
14
33
Primary metal industries
16.320
8.073
15.546
0.171
0.210
3.723
1.179
-0.021
14.285
3097
5.241
15
34
Fabricated metal products
6.169
1.852
7.232
0.264
0.255
5.311
1.263
-0.868
13.167
3768
6.376
16
35
Machinery, except electrical
7.111
1.791
7.704
0.054
0.332
6.296
1.635
-0.789
16.197
9368
15.853
17
36
Electrical and electronic machinery,
6.409
1.752
6.899
0.078
0.342
6.054
1.737
-2.516
17.886
297
0.503
18
37
Transportation equipment
16.510
5.046
22.115
0.253
0.279
5.949
1.361
2.329
14.512
4041
6.838
19
38
Instruments; Photographic,
5.620
1.442
5.667
-0.105
0.337
6.196
2.066
-0.763
17.318
7686
13.007
equipment and supplies
medical and optical goods; Clocks
20
39
Miscellaneous manufacturing industries
2.702
0.547
3.201
0.147
0.297
6.724
1.387
-1.025
15.931
1757
2.973
Notes: The sample comes from COMPUSTAT and CRSP databases. Variables are discussed and defined in Table A-1: TA - total assets in million U.S dollar units; K - capital stocks in million U.S. dollar units; S net sales in million U.S. dollars; CFK - ratio of cash flow to capital stock; IK - ratio of investment to capital stock (t-1); SK: ratio of sales to capital stock; TQ - Tobin's Q; r - stock returns in percentage; σ - stock
standard deviation in percentage. All accounting data are real that is deflated by GDP deflator based on January 1983 dollars.
30
Table 3. Descriptive statistics over fiscal years
Year
TA
K
S
CF/K
I/K
S/K
TQ
r
σ
pg
Number of observations
Percent of observations
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Average/Total
11.936
15.233
15.236
13.797
12.013
11.269
10.742
9.955
8.932
8.718
8.375
8.665
9.060
8.518
8.779
9.100
9.155
8.966
9.082
8.999
8.360
8.674
8.473
7.775
8.050
9.061
9.396
9.896
10.110
9.920
10.235
10.047
9.583
9.954
9.976
9.789
10.514
11.451
11.638
12.612
12.521
13.457
15.047
16.111
16.062
10.561
5.254
6.664
6.750
5.910
5.148
4.769
4.387
4.065
3.732
3.579
3.371
3.309
3.365
3.302
3.379
3.481
3.430
3.343
3.485
3.556
3.355
3.482
3.336
3.067
3.072
3.415
3.457
3.701
3.773
3.719
3.871
3.729
3.485
3.549
3.648
3.628
3.705
3.910
3.803
4.007
4.019
4.117
4.373
4.430
3.963
3.931
14.047
16.977
17.203
6.455
14.532
13.354
12.847
11.503
10.125
9.858
9.859
10.837
11.954
11.047
11.652
12.173
12.441
12.444
12.338
11.958
10.504
10.633
10.596
9.340
9.175
10.347
10.677
11.237
11.163
10.611
0.743
10.237
9.866
10.441
10.251
9.939
10.135
10.910
11.254
11.522
11.560
12.474
14.264
15.374
15.624
11.388
0.380
0.333
0.372
0.388
0.391
0.365
0.357
0.318
0.266
0.261
0.324
0.348
0.323
0.320
0.350
0.342
0.358
0.339
0.278
0.257
0.180
0.195
0.139
0.048
0.054
0.049
0.000
-0.009
0.037
0.090
0.112
0.104
0.051
0.135
0.055
-0.028
-0.133
-0.096
-0.221
-0.317
-0.253
-0.109
0.002
0.032
0.067
0.152
0.203
0.202
0.230
0.274
0.315
0.312
0.303
0.322
0.259
0.227
0.267
0.308
0.297
0.242
0.266
0.292
0.321
0.344
0.340
0.341
0.300
0.288
0.348
0.331
0.318
0.316
0.323
0.294
0.298
0.245
0.272
0.290
0.312
0.319
0.323
0.316
0.298
0.255
0.268
0.231
0.197
0.190
0.216
0.241
0.265
0.283
4.895
4.590
4.702
5.003
5.092
5.028
5.117
5.065
5.004
5.061
5.274
5.571
5.734
5.603
5.762
5.812
6.001
5.985
5.916
5.858
5.384
5.417
5.459
5.243
5.248
5.395
5.601
5.587
5.579
5.561
5.455
5.537
5.527
5.523
5.477
5.447
5.327
5.180
5.298
5.197
5.169
5.508
5.739
5.882
6.014
5.418
1.537
1.567
1.688
1.802
1.522
1.981
2.113
1.657
1.396
1.598
1.593
1.183
0.933
1.022
1.075
1.052
1.105
1.189
1.389
1.304
1.489
1.672
1.478
1.667
1.689
1.558
1.598
1.638
1.505
1.767
1.780
1.912
1.771
1.959
1.920
1.996
1.781
1.983
1.846
1.823
1.581
2.019
1.959
1.986
2.019
1.624
-4.749
7.474
8.075
12.916
-5.080
17.286
7.377
-15.046
-8.559
3.814
-0.515
-20.973
-14.454
14.583
11.950
0.673
1.806
7.452
5.966
-1.224
7.182
8.450
-7.653
6.737
-0.233
-0.819
6.293
2.610
-7.582
13.083
7.705
6.996
0.963
6.821
3.618
1.731
-8.230
-3.132
-13.108
-0.097
-7.084
12.642
4.983
-3.044
5.922
1.634
10.874
8.636
8.506
9.364
11.681
12.171
11.460
12.345
15.208
13.760
12.576
15.154
15.581
14.777
12.854
11.546
13.035
12.638
14.216
12.917
13.548
12.782
13.084
13.590
15.215
17.175
15.136
14.174
15.710
16.296
16.391
16.932
16.633
16.499
17.065
16.956
17.920
18.417
20.530
18.233
18.157
15.552
14.537
13.304
12.935
14.357
-0.100
-0.201
-0.100
-0.162
-0.149
-0.212
-0.327
-0.082
0.091
-0.163
-0.276
1.335
3.108
0.618
-1.042
0.074
-0.609
4.472
0.760
-0.628
-1.047
-1.103
-0.744
-0.874
-4.657
1.124
-1.965
2.562
1.467
-3.576
-0.658
-2.989
1.564
0.695
2.038
-3.290
-4.601
7.705
-0.074
-3.956
4.110
0.466
1.082
2.987
0.421
0.069
334
264
300
387
727
913
1070
1289
1519
1640
1700
1735
1659
1739
1735
1684
1713
1709
1718
1808
1762
1824
1868
1866
1874
1872
1825
1718
1684
1654
1724
1790
1913
1984
2053
2109
2022
1871
1734
1592
1535
1503
1402
1290
1001
69113
0.483
0.382
0.434
0.560
1.052
1.321
1.548
1.865
2.198
2.373
2.460
2.510
2.400
2.516
2.510
2.437
2.479
2.473
2.486
2.616
2.549
2.639
2.703
2.700
2.712
2.709
2.641
2.486
2.437
2.393
2.494
2.590
2.768
2.871
2.970
3.052
2.926
2.707
2.509
2.303
2.221
2.175
2.029
1.867
1.448
100
Notes: The sample comes from COMPUSTAT and CRSP databases. Variables are discussed and defined in Table A-1: TA - total assets in million U.S dollar units; K - capital stocks in million U.S. dollar units; S net sales in million U.S. dollars; CFK - ratio of cash flow to capital stock; IK - ratio of investment to capital stock (t-1); SK: ratio of sales to capital stock; TQ - Tobin's Q; r - stock returns in percentage; σ - stock
standard deviation in percentage; pg - real oil price growth rate in percentage. . All accounting data are real that is deflated by GDP deflator based on January 1983 dollars.
31
Table 4. OLS estimates of S&P 500 volatilities on crude oil prices
Dependent variables: (S&P 500 volatility)
Intercept
(1)
3.657
(0.203)
0.062
(0.007)
6
***
(2)
4.816
(0.152)
***
(3)
4.847
0.149
(4)
4.889
0.147
0.023
(0.008)
***
0.023
0.008
24
***
0.020
0.009
0.114
***
***
12
Goodness of fit (R-Squared)
***
0.014
0.013
**
0.009
Notes: ***, **, * are the significant levels at 1%, 5%, and 10%.
is the monthly oil price growth rate,
6 ,
12 ,
24 , are
current growth rates of oil price if the current rates are greater than the maximum of the last 6, 12, and 24 months, 0 otherwise. The dependent variable is the
monthly S&P 500 stock price volatility (Baum et al., 2008).
32
Table 5. Least squares estimates using U.S. manufacturing company data
Dependent variables: (Iit / Ki,t-1)
Lagged investment rate (Iit-1 / Ki,t-2)
Sales growth (Δyit)
Cash flow (Cit / Ki,t-1)
Lagged cash flow (Ci,t-1 / Ki,t-2)
Error correction term (y-k)i,t-1
Sales growth squared (Δyit)2
Change in uncertainty (ΔUit)
Lagged uncertainty (Ui,t-1)
Uncertainty * sales growth (Ui,t*Δyit)
(1)
0.423
(0.004)
0.234
(0.005)
0.026
(0.002)
0.035
(0.002)
-0.029
(0.001)
0.035
(0.003)
0.053
(0.005)
0.091
(0.005)
-0.158
(0.017)
Firm effect
Year effect
Goodness of fit
no
no
0.665
***
***
***
***
***
***
***
***
***
(2)
0.143
(0.004)
0.221
(0.005)
0.025
(0.002)
0.051
(0.002)
0.107
(0.002)
0.039
(0.003)
-0.011
(0.005)
-0.032
(0.007)
-0.044
(0.016)
yes
no
0.767
***
***
***
***
***
***
**
***
***
(3)
0.146
(0.004)
0.259
(0.005)
0.011
(0.002)
0.040
(0.002)
0.201
(0.003)
0.045
(0.003)
-0.029
(0.009)
-0.038
(0.011)
-0.040
(0.016)
***
***
***
***
***
***
***
***
**
yes
yes
0.783
Notes: The uncertainty (%) is obtained by multiplying the monthly stock price volatility (Baum et al., 2008) to the real growth rate of oil price that the growth rate is greater
than the maximum of the last 12 months, 0 otherwise. The number of observations in all column is 69113, using an unbalanced panel of 3322 firms over 1962-2007.
Column (1) is the OLS estimate. Columns (2) and (3) are one-way and two-way fixed effect least-square estimates. ***, **, * are the significant levels at 1%, 5%, and
10%.
33
Table 6. Econometric estimates using U.S. manufacturing company data
Dependent variables: (Iit / Ki,t-1)
(1)
(2)
(3)
(4)
(5)
(6)
U(12)01it
NU(12)it
NU(12)01it
U(12)it
sit
Lagged investment rate (Iit-1 / Ki,t-2)
0.174 ***
0.160 ***
0.171 ***
0.172 ***
0.174 ***
0.170 ***
(0.011)
(0.010)
(0.011)
(0.011)
(0.011)
(0.010)
Sales growth (Δyit)
0.207 ***
0.155 ***
0.219 ***
0.211 ***
0.174 ***
0.201 ***
(0.017)
(0.045)
(0.019)
(0.022)
(0.019)
(0.020)
Cash flow (Cit / Ki,t-1)
0.046 ***
0.041 ***
0.044 ***
0.047 ***
0.046 ***
0.049 ***
(0.011)
(0.011)
(0.010)
(0.011)
(0.010)
(0.010)
Lagged cash flow (Ci,t-1 / Ki,t-2)
0.080 ***
0.072 ***
0.080 ***
0.077 ***
0.075 ***
0.076 ***
(0.009)
(0.009)
(0.009)
(0.009)
(0.009)
(0.009)
Error correction term (y-k)i,t-1
0.027 ***
0.047 ***
0.022 ***
0.027 ***
0.033 ***
0.025 ***
(0.003)
(0.005)
(0.003)
(0.003)
(0.003)
(0.003)
Sales growth squared (Δyit)2
0.057 ***
0.064 ***
0.056 ***
0.062 ***
0.058 ***
0.061 ***
(0.014)
(0.015)
(0.015)
(0.016)
(0.014)
(0.015)
Change in uncertainty (ΔUit)
0.000
0.002 **
-0.015 **
-0.003 ***
-0.002
-0.001
(0.000)
(0.001)
(0.007)
(0.001)
(0.042)
(0.001)
Lagged uncertainty (Ui,t-1)
-0.001
-0.001
-0.035 ***
-0.006 ***
-0.195 ***
-0.003 *
(0.001)
(0.001)
(0.009)
(0.001)
(0.060)
(0.002)
Uncertainty * sales growth (Ui,t*Δyit)
-0.006
0.003
-0.045
0.003
0.492
0.000
(0.005)
(0.003)
(0.065)
(0.008)
(0.362)
(0.012)
Firm effect
yes
yes
Yes
yes
yes
yes
Year effect
yes
yes
Yes
yes
yes
yes
Serial correlation (p-value)
0.429
0.231
0.394
0.375
0.489
0.346
Sargan-Hansen (p-value)
0.232
0.545
0.453
0.481
0.368
0.383
Goodness of fit
0.464
0.465
0.461
0.463
0.467
0.461
Notes:
is the growth rate of real oil price.
is firm i’s stock price volatility (Baum et al. 2008).
12
is a measure of uncertainty that is obtained by multiplying the oil shock variable pgmax(12)t to the firm
i’s stock price volatility
. pgmax(12)t is equal to the growth rate of real oil price at time t,
, if
is greater than the maximum growth rate of the last 12 months, zero otherwise.
12
denotes a measure of
as representing the uncertainty of firm i (Baum et. al. 2008).
12
denotes a measure of uncertainty
uncertainty obtained by multiplying the oil shock variable pgmax(12)t to the firm i’s stock price volatility
obtained by multiplying scaled oil shock variable
12 to the firm i’s stock price volatility
. The scaled oil price shock
12 is obtained by dividing the real growth rate of oil price by its timevarying conditional standard deviation obtained from GARCH(1,1) and following the same process as the one used in the construction of pgmax(12)t.
12
denotes a measure of uncertainty obtained by
multiplying an indicator function, that takes 1 if the oil shock variable pgmax(12)t is positive and zero otherwise, to the firm i’s stock price volatility
.
12
denotes a measure of uncertainty obtained by
. The estimates are obtained by applying the Arellanomultiplying an indicator function, that takes 1 if the scaled oil shock variable npgmax(12)t is positive and zero otherwise, to the firm i’s stock price volatility
Bond two-step system GMM method that produces heteroscedasticity robust standard errors. Sales, stock-returns, and uncertainty are considered as endogenous, with the instruments in the first-differenced equations
that are similar to Bloom et al. (2007): Ii,t-1/Ki,t-2, Cit/Ki,t-1, Cit-1/Ki,t-2, Δyit, (y-k)i,t-1 with lags 2-12, and Ui,t-1 with lags 2-14 to allow for a long uncertainty delay, and in the level equations: Δ(Iit-1/Ki,t-2), Δyit-1, ΔΔCit-1/Ki,t-2,
ΔΔ(y-k)i,t-2, ΔΔUit-1. Second-order serial correlation in the first-differenced residuals is tested using a Lagrange multiplier test (Arellano and Bond, 1991). Instrument validity is tested using a Sargan-Hansen test of the
overidentifying restrictions. The goodness of fit measure is the squared correlation coefficient between actual and predicted levels of the dependent variable (Windmeijer, 1995). Standard errors in parentheses are
robust to autocorrelation and heteroscedasticity. ***, **, * denote the significant levels at 1%, 5%, and 10%, respectively.
pgt
34
Table 7. Using oil shock variables to proxy the uncertainty measures
Dependent variables: (Iit / Ki,t-1)
(1)
(2)
(4)
(5)
pgmax(6)t
pgmax(12)t
npgmax(6)t
npgmax(12)t
Lagged investment rate (Iit-1 / Ki,t-2)
0.174 ***
0.176 ***
0.178 ***
0.178 ***
(0.011)
(0.011)
(0.011)
(0.010)
Sales growth (Δyit)
0.202 ***
0.215 ***
0.158 ***
0.168 ***
(0.021)
(0.019)
(0.021)
(0.019)
Cash flow (Cit / Ki,t-1)
0.046 ***
0.046 ***
0.047 ***
0.047 ***
(0.011)
(0.011)
(0.010)
(0.010)
Lagged cash flow (Ci,t-1 / Ki,t-2)
0.081 ***
0.081 ***
0.077 ***
0.077 ***
(0.009)
(0.009)
(0.009)
(0.009)
Error correction term (y-k)i,t-1
0.021 ***
0.022 ***
0.027 ***
0.029 ***
(0.003)
(0.003)
(0.003)
(0.003)
Sales growth squared (Δyit)2
0.051 ***
0.052 ***
0.057 ***
0.055 ***
(0.014)
(0.013)
(0.013)
(0.013)
Change in oil shocks (Δpgmaxt)
-0.001
-0.001
0.003
0.002
(0.001)
(0.001)
(0.007)
(0.006)
Lagged oil shocks (pgmaxt-1)
-0.006 ***
-0.005 **
-0.033 ***
-0.034 ***
(0.002)
(0.002)
(0.010)
(0.009)
Oil shocks * sales growth (pgmaxt*Δyit)
-0.012
-0.016
0.119
0.104
(0.011)
(0.011)
(0.070)
(0.069)
Firm effect
yes
yes
yes
yes
Year effect
yes
yes
yes
yes
Serial correlation (p-value)
0.438
0.487
0.598
0.557
Sargan-Hansen (p-value)
0.381
0.329
0.290
0.262
Goodness of fit
0.460
0.462
0.464
0.466
Notes: Table 7 presents the estimate of oil shocks when pgmax(6)t, pgmax(12)t, npgmax(6)t, and npgmax(12)t, substitute for U(6)it, U(12)it, NU(6)it, and NU(12)it in the investment model (12) respectively.
pgmax(6)t npgmax(12)t, and pgmax(24)t are equal to the growth rate of real oil price at time t if they are greater than the maximum growth rate of the last 6 and 12, and 24 months respectively, zero otherwise. The
scaled oil price shocks npgmax(6)t, npgmax(12)t, and npgmax(24)t are obtained by dividing the real growth rate of oil price by their time-varying conditional standard deviation obtained from GARCH(1,1) and
following the same process as the one used in the construction of pgmax(6)t, pgmax(12)t, and pgmax(24)t. U(6)it, U(12)it, and U(24)it, are measures of uncertainty that are obtained by multiplying the oil shock
variable pgmax(6)t, pgmax(12)t, and pgmax(24)t to the firm i’s stock price volatility sit respectively. NU(6)it, NU(12)it, and NU(24)it are measures of uncertainty that are obtained by multiplying the oil shock
variables npgmax(6)t, npgmax(12)t, and npgmax(24)t to the firm i’s stock price volatility sit respectively. The estimates are obtained by applying the Arellano-Bond two-step system GMM method that produces
heteroscedasticity robust standard errors. Sales, stock-returns, and uncertainty are considered as endogenous, with the instruments in the first-differenced equations that are similar to Bloom et al. (2007): Ii,t-1/Ki,t-2,
Cit/Ki,t-1, Cit-1/Ki,t-2, Δyit, (y-k)i,t-1 with lags 2-12, and Ui,t-1 with lags 2-14 to allow for a long uncertainty delay, and in the level equations: Δ(Iit-1/Ki,t-2), Δyit-1, ΔΔCit-1/Ki,t-2, ΔΔ(y-k)i,t-2, ΔΔUit-1. Second-order serial
correlation in the first-differenced residuals is tested using a Lagrange multiplier test (Arellano and Bond, 1991). Instrument validity is tested using a Sargan-Hansen test of the overidentifying restrictions. The
goodness of fit measure is the squared correlation coefficient between actual and predicted levels of the dependent variable (Windmeijer, 1995). Standard errors in parentheses are robust to autocorrelation and
heteroscedasticity. ***, **, * denote the significant levels at 1%, 5%, and 10%, respectively.
35
Figure 1: Real Price of Crude Oil, 1971.1 - 2007.12, Index Based on January 1983 Dollars
50
45
Real Price (Dollar per Barrel)
40
Iraq War
2003.3
35
30
Gulf War
1990.8
25
Iranian
Revolution
20
15
10
October War/Embargo
1973.10
Iran-Iraq War
1980.9
5
0
Jan-1971
Afghan War
2001.10
Jan-1976
Jan-1981
Jan-1986
Jan-1991
Jan-1996
Jan-2001
Jan-2006
Notes: The oil price series is the refiner acquisition cost of imported crude oil as reported by the U.S. Department of Energy. We
extended the series from 1974.1 back to 1962.1 as in Kilian (2009). The graph shows the data period from 1971.1 to 2007.12 since not
much movements show on the extended series. The oil price has been deflated by the U.S. CPI based on January 1983 dollars.
36
Figure 2: The Growth Rate of Real Crude Oil Price, 1971.1 - 2007.12,
Index Based on January 1983 Dollars
50
Gulf War
Iraq War
2003.3
40
30
Iran-Iraq War
1980.9
Afghan War
2001.10
Real Growth Rate
20
10
0
-10
-20
Iranian Revolution
1979.1
October
War/Embargo
-30
-40
Jan-1971
Jan-1976
Jan-1981
Jan-1986
Jan-1991
Jan-1996
Jan-2001
Jan-2006
Notes: The oil price series is the refiner acquisition cost of imported crude oil as reported by the U.S. Department of Energy. We extended the
series from 1974.1 back to 1962.1 as in Kilian (2009). The graph shows the data period from 1971.1 to 2007.12 since not much movements
show on the extended series. The oil price has been deflated by the U.S. CPI based on January 1983 dollars.
37
Figure 3: Monthly Volatility of S&P 500 Index
40
35
Iraq War
2003.3
Standard Deviation (Percentage)
30
Iran-Iraq War
1980.9
Afghan War
2001.10
25
20
15
Gulf War,
1990.8
Iranian Revolution
1979.1
October War/Embargo
1973.10
10
5
0
Jan-1971
Jan-1976
Jan-1981
Jan-1986
Jan-1991
Jan-1996
Jan-2001
Jan-2006
Notes: The monthly volatiltiy of S&P 500 index is constructed using S&P 500 daily stock index returns as in Baum et al. (2008). The sample in
the text is from January 1962 to December 2007. The graph shows the same data period from 1971.1 to 2007.12 as in Figure 1 and Figure 2 for
the convenience purpose.
38
Figure 4. Uncertainty PSt Obtained by Multiplying Monthly S&P 500 Volatiltiy and the Real Oil Price Growth
Rate That is Greater than the Maximum of the Last 12 Months
400
350
Iran-Iraq War
1980.9
300
Iranian Revolution
1979.1
250
Percentage
Iraq War
2003.3
Gulf War
1990.8
Afghan War
2001.10
200
150
October War/Embargo
1973.10
100
50
0
Jan-1971
Jan-1976
Jan-1981
Jan-1986
Jan-1991
Jan-1996
Jan-2001
Jan-2006
Notes: The monthly volatiltiy of S&P 500 index is constructed using S&P 500 daily stock index returns as in Baum et al. (2008). The uncertainty12 is
measured by the multiplication of monthly S&P 500 standard deviation and real oil price growth rate that is greater than the maximum of the last 12
months.The sample in the text is from January 1962 to December 2007. The graph shows the same period from 1971.1 to 2007.12 as in previous
39
Figure 5. Uncertainty PS01t Obtained by Multiplying Monthly S&P 500 Volatility by the Indicator
Function Whose value is 1 if the Real Oil Price Growth Rate is Greater than the Maximum of the Last 12
Months, 0 otherwise
20
18
Afghan War
2001.10
Iran-Iraq War
1980.9
16
14
Percentage
12
Iraq War
2003.3
Gulf War
1990.8
Iranian Revolution
1979.1
10
8
October War/Embargo
1973.10
6
4
2
0
Jan-1971
Jan-1976
Jan-1981
Jan-1986
Jan-1991
Jan-1996
Jan-2001
Jan-2006
Notes: The monthly volatiltiy of S&P 500 index is constructed using S&P 500 daily stock index returns as in Baum et al. (2008). The
uncertainty12(0-1) is measured by the multiplication of monthly S&P 500 standard deviation and indicator-1 if real oil price growth rate
is greater than the maximum of the last 12 months.Otherwise, it is 1. The sample in the text is from January 1962 to December 2007. The
graph shows the same data period from 1971.1 to 2007.12 as in previous figures for the convenience purpose.
40
Figure 6. Uncertainty QSt Obtained by Multiplying Monthly S&P500 Volatillity by
the Normalized Real Oil Price Growth Rate
40
35
30
Iranian Revolution
1979.1
25
Percentage
Iraq War
2003.3
Iran-Iraq War
1980.9
Gulf War
1990.8
20
15
October War/Embargo
1973.10
Afghan War
2001.10
10
5
0
Jan-1971
Jan-1976
Jan-1981
Jan-1986
Jan-1991
Jan-1996
Jan-2001
Jan-2006
Notes: The monthly volatiltiy of S&P 500 index is constructed using S&P 500 daily stock index returns as in Baum et al. (2008). The
uncertaintyo12 is measured by the multiplication of monthly S&P 500 standard deviation and standardized real oil price growth rate that
is greater than the maximum of the last 12 months.The sample in the text is from January 1962 to December 2007. The graph shows the
same data period from 1971.1 to 2007.12 as in previous figures for the convenience purpose.
41
Figure 7. Uncertainty QS01t Obtained by Multiplying Monthly S&P 500 Volatility by the Indicator
Function Whose Value is 1 If the Standardized Real Oil Price Growth Rate is Greater than the
Maximum of the Last 12 Months, 0 Otherwise
20
Afghan War
2001.10
18
16
Iran-Iraq War
1980.9
14
Iranian Revolution
1979.1
Percentage
12
Iraq War
2003.3
Gulf War
1990.8
10
8
October War/Embargo
1973.10
6
4
2
0
Jan-1971
Jan-1976
Jan-1981
Jan-1986
Jan-1991
Jan-1996
Jan-2001
Jan-2006
Notes: The monthly volatiltiy of S&P 500 index is constructed using S&P 500 daily stock index returns as in Baum et al. (2008). The
uncertaintyo12(0-1) is measured by the multiplication of monthly S&P 500 standard deviation and indicator-1 if standardized real oil price
growth rate is greater than the maximum of the last 12 months.Otherwise, it is 1. The sample in the text is from January 1962 to December
2007. The graph shows the same data period from 1971.1 to 2007.12 as in previous figures for the convenience purpose.
42
43