Download Macroeconomic Activity Under Uncertainty: How Firm

Macroeconomic Activity Under Uncertainty: How
Firm-Level and Aggregate-Level Uncertainties Interact
Alon Kalay, Suresh Nallareddy, and Gil Sadkay
January 26, 2016
Abstract
Prior studies suggest that both aggregate and …rm-level uncertainty adversely a¤ect
economic growth, because they reduce investment and impede the reallocation of
resources in the economy. In this paper, we hypothesize and …nd that the e¤ects
of …rm-level and aggregate-level uncertainty interact. Therefore, the e¤ects of each
type of uncertainty (…rm-level or aggregate-level) are exacerbated in the presence of
the other, and their combined e¤ect is larger than the sum of the individual e¤ects.
We further show that the interaction term explains the puzzling relation between the
equity premium and earnings dispersion, and predicts unemployment and industrial
production forecast errors. Our …ndings with respect to the interaction of di¤erent
forms of uncertainty furthers our understating of how uncertainty a¤ects macroeconomic
activity and has implications for policy setters.
JEL classi…cation: E32, G12, G14, M41
Keywords: stock prices; aggregate earnings; illiquidity; expected returns; expected earnings
We would like to thank an anonymous reviewer and Associate Editor, Dan Amiram, Mary E. Barth,
Tuhin Biswas, Samuel Bonsall (discussant), John Donaldson, Trevor Harris, Bjorn Jorgensen, Urooj Khan,
Mauricio Larrain, Emi Nakamura, Maria Ogneva, Stephen Penman, Shyam Sunder (discussant), and the
participants at AAA annual meeting, the Burton Conference at Columbia University, The Indian School
of Business Conference, Tel Aviv University Conference, and research seminar at University of Miami and
SUNY Bu¤alo for their helpful comments and suggestions. We are grateful to Ethan Rouen for the excellent
research assistance. Any remaining errors are our own.
y
Alon and Suresh are from Columbia University. Gil is from University of Texas at Dallas. email:
[email protected], [email protected], [email protected].
1
Introduction
A recent stream of literature in economics (e.g., Bloom, 2009; Bloom, Floetotto, Jaimovich,
Saporta-Eksten, and Terry, 2012; Baker and Bloom, 2013) suggests that uncertainty
is an important driver of economic activity.
Analytical and empirical results suggest
that uncertainty lowers investment and impedes the reallocation of labor and capital.
Consequently, higher uncertainty damps economic activity. This paper extends this line
of research and shows that the e¤ects of …rm-level uncertainty are exacerbated when
macroeconomic uncertainty is high and vice versa. We hypothesize and show that the
combined e¤ects of …rm-level and aggregate-level uncertainty are larger than the sum of
the individual a¤ects.
In a recent review, Bloom (2014) describes the varieties of uncertainty that investors
and …rms face. One form is aggregate uncertainty, regarding macroeconomic conditions. A
second is …rm-level uncertainty, that is, uncertainty about which …rm, industry, or sector is
more likely to grow. While the literature acknowledges that these di¤erent types exist, it
does not consider their interaction. Thus existing studies suggest that uncertainty, regardless
of its form, impedes economic growth because it hinders investment and the reallocation of
labor and capital.
In this study, we focus on uncertainty with respect to the inability to forecast
investment outcomes. Our analysis shows that the correlations between cross-sectional
dispersion and policy uncertainty— common measures of …rm- and macro-level uncertainty,
respectively— are approximately 20%. This suggests that these two types of uncertainty
di¤er and that there are multiple periods when only one type of uncertainty is high. Thus
investors and …rms can face both types of uncertainty independently or simultaneously. We
further hypothesize that the e¤ects of uncertainty are most pronounced when aggregate and
…rm-level uncertainty are high simultaneously, exacerbating one another. Our hypothesis is
based on two economic arguments.
First, portfolio theory suggests that aggregate uncertainty limits investors’ options to
mitigate the e¤ects of …rm-level uncertainty and vice versa. When …rms and investors
face only one type of uncertainty, diversi…cation and resource reallocation allow them to
mitigate the e¤ects of a speci…c form of uncertainty. For example, if …rms/investors are
uncertain about which sector of the economy will grow faster but they have more clarity
regarding overall growth, they can invest in a large portfolio of projects/…rms to mitigate
the e¤ect of …rm-level uncertainty. By investing in this way, they gain the average growth
of the economy. However, if they are also uncertain about the aggregate growth in the
economy, diversi…cation simply substitutes one form of uncertainty with another. A related
argument, based on resource reallocation, can be made during periods with minimal …rm-level
uncertainty and signi…cant aggregate uncertainty (see Section 2 below). Therefore, while
uncertainty hurts investment, as shown by Bloom et al. (2012), the e¤ect of …rm-level
uncertainty is exacerbated when economic agents also face aggregate uncertainty and vice
versa.
Second, uncertainty a¤ects the reallocation of labor and impedes sectoral shifts (Lucas
and Prescott, 1974; Lilien, 1982; Abraham and Katz, 1986; Hosios, 1994; Lazear and Spletzer,
2012).1 The sectoral shift theory suggests that cross-sectional dispersion in performance
increases unemployment. This occurs because employees migrate from poorly performing
…rms or sectors to better performing ones, and this migration takes time due to frictions
in the labor market (such as search costs). Thus unemployment increases with layo¤s.
All else equal, higher dispersion implies a greater proportion of poorly performing …rms,
which inevitably lay o¤ employees. Consequently, unemployment increases during periods
of high dispersion. We extend this logic and argue that the relation between unemployment
and sectoral shifts also depends on the overall state of the economy. Speci…cally, periods
when both dispersion is high and macroeconomic conditions are poor, are the periods with
the highest proportion of poor-performing …rms. More struggling …rms increases layo¤s and
unemployment. In other words, the e¤ects of dispersion are higher when the overall economy
is struggling. Thus, according to the sectoral shift hypothesis, the interaction between
…rm-level and aggregate-level performance a¤ects unemployment, even if it is unrelated
1
Loungani, Rush, and Tave (1990) …nd that dispersion in stock prices is associated with higher
unemployment.
2
to uncertainty. The sectoral shift theory also assumes a constant hiring rate. In this
paper, we further hypothesize that uncertainty slows the migration process because it lowers
hiring rates, which exacerbates the frictions described in the sectoral shift hypothesis and
results in even greater unemployment. Following our discussion above, we further expect the
implications of …rm-level uncertainty on hiring rates and unemployment to increase when
faced with aggregate uncertainty, and vice versa.
Our empirical analysis examines the contemporaneous relation between three
macroeconomic indicators, measures of aggregate uncertainty, and measures of …rm-level
uncertainty.
Our measures of macroeconomic activity are investment, unemployment,
and industrial production. To proxy for aggregate-level uncertainty, we employ aggregate
pro…tability, economic policy uncertainty, and the CBOE Volatility Index (VIX). For
…rm-level uncertainty, we employ cross-sectional earnings dispersion as our main measure,
but we also use idiosyncratic return volatility. As noted above, the state of the economy
and earnings dispersion relate to unemployment even if they do not capture uncertainty.
Consistent with prior studies, we …nd that our measures of aggregate uncertainty and
the cross-sectional dispersion in …rm-level performance are associated with lower levels
of investment and industrial production and higher levels of unemployment. To test the
existence of an interactive relation between …rm-level and aggregate-level uncertainty, we
add the interaction term for aggregate uncertainty and earnings dispersion. Consistent with
our predictions, we …nd that the e¤ects of dispersion are exacerbated in periods with high
aggregate uncertainty, and that the combined e¤ects of dispersion and aggregate uncertainty
are larger than the sum of the individual e¤ects. The explanatory power of the model
increases signi…cantly when we add the interaction term.
While one cannot determine causality in a model that relies on an interaction term,
we attempt to provide more causal evidence on the relation between …rm-level and
aggregate-level uncertainty and macroeconomic activity. We use several shocks employed
by Bloom et al. (2012) when examining the relationship. We attempt to identify shocks
that likely a¤ect both aggregate and …rm-level uncertainty and compare their e¤ects to
those of shocks that a¤ect only one type of uncertainty. If shocks that increase both have
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stronger e¤ects than those that increase aggregate uncertainty alone, we can infer a stronger
causal link between the interaction e¤ect and macroeconomic activity. Consistent with our
hypotheses, we …nd that the election of Bill Clinton in 1992, which only a¤ected policy
uncertainty, had a relatively mild e¤ect on macroeconomic conditions. In contrast, the
terrorist attacks of Sept. 11, 2001, and the collapse of Lehman Brothers in 2008, which
increased both …rm-level and aggregate uncertainty, led to more signi…cant declines in
macroeconomic activity. While we cannot eliminate the causality concerns related to our
study, this analysis helps mitigate them to some degree.
We conduct two additional analyses. First, we build on the above …ndings to shed
light on the relation between cross-sectional earnings dispersion and aggregate stock returns.
We examine whether the relation between earnings dispersion, the interaction of micro
and macro uncertainty, and the macroeconomy help explain the robust relation between
earnings dispersion and aggregate stock returns (Jorgensen, Li, and Sadka, 2012). Our
results show that the relation between aggregate stock returns and future earnings dispersion
is conditional on the amount of aggregate uncertainty in the economy. Thus our …ndings
suggest that economic theories on the e¤ects of uncertainty can help explain, at least in part,
the somewhat surprising relation between earnings dispersion and aggregate stock returns.
Second, we …nd that our interaction term partly predicts macroeconomists’forecast errors.2
Our …ndings have several important policy implications. First, if the economy is in
a period of high …rm-level uncertainty, adopting more stable and predictable policies,
which lower aggregate uncertainty, should improve macroeconomic conditions. In as similar
vein, all else equal, initiating aggregate (macro) policies during periods of low …rm-level
uncertainty mitigates the potential negative implications of heightened policy (aggregate)
2
Our paper also extends the literature that shows accounting information is useful for understanding and
predicting macroeconomic activity. Much of the prior evidence suggests that aggregate earnings contain
macroeconomic information (e.g., Anilowski et al., 2007; Shivakumar, 2007; Kothari et al., 2013; Hann et
al., 2012; Bonsall et al., 2013; Ogneva, 2013) and that aggregate earnings predict the U.S. Federal Reserve’s
monetary policy (Gallo et al., 2013). While prior literature uses aggregate earnings to understand and predict
macroeconomic indicators, we employ dispersion in earnings and conditional dispersion to further highlight
the usefulness of accounting information in understanding macroeconomic activity. In a contemporaneous
paper, Nallareddy and Ogneva (2015) …nd that earnings dispersion predicts errors in initial macroeconomic
estimates released by government statistical agencies.
4
uncertainty. Finally, our study implies that policies that potentially increase both …rm-level
and aggregate-level uncertainty have more signi…cant negative implications for economic
growth. Therefore such policies have higher economic costs.
The rest of the paper is organized as follows. Section 2 describes our hypotheses in more
detail. Section 3 discusses our research design and variable measurement. Section 4 presents
our main empirical results. Section 5 presents our additional analysis related to stock returns
and macroeconomist forecast errors. Section 6 concludes.
2
Uncertainty and Macroeconomic Activity
Recent analytical and empirical analyses suggest uncertainty is a signi…cant determinant of
macroeconomic activity (e.g., Bloom, 2009; Bloom, Floetotto, Jaimovich, Saporta-Eksten,
and Terry, 2012; Baker and Bloom, 2013). These studies show that macroeconomic activity
slows during periods of high uncertainty, because investors and …rms are more uncertain
about the prospects of their investments, which increases the option value of postponing
investments. These postponements lead to signi…cant shortfalls in hiring, investment, and
output. Consequently, the reallocation of capital and labor is then hindered, resulting in
lower growth in investments, production, productivity, and employment. Consistent with
this theory, empirical analysis shows that shocks to uncertainty result in lower growth and
may even cause recessions.
In a recent review of this literature, Bloom (2014) highlights that uncertainty may take
di¤erent forms. Agents in the economy can be uncertain about overall macroeconomic
conditions and future growth, the prospects of di¤erent individual …rms, or both.
Consistently, prior studies employ both aggregate uncertainty measures, such as VIX
and policy uncertainty, and …rm-level measures of uncertainty, such as the cross-sectional
dispersion in earnings and stock returns, when examining the e¤ect of uncertainty on
macroeconomic activity.
Prior evidence suggests that both types of uncertainty, while
di¤erent, can lower economic growth because they hinder investments and limit the
reallocation of resources (from poorly performing to better performing projects). The two
5
types of uncertainty not only di¤er but may also occur independently, as shown in Table
2. Economic agents can face uncertainty about economic policy and overall growth, while
having a clearer understanding of which sector/industry/…rm in the economy is more likely to
succeed and capture a larger portion of overall growth. In contrast, investors can be con…dent
about the overall state of the economy but uncertain about which sectors/industries/…rms
are more likely to succeed. Take, for example, the development of the high-de…nition video
market. Several companies and technologies competed for the same market, with only one
potential winner. While there was relative certainty regarding the demand for the product
and the overall growth in the market, it was not clear which …rm and technology would win.
Prior studies explore both types of uncertainty but fail to consider how they overlap and
interact. Bloom, Floetotto, Jaimovich, Saporta-Eksten, and Terry (2012) show that both
microeconomic and macroeconomic uncertainty ebb and ‡ow through the business cycle, by
examining uncertainty at the plant, …rm, and industry level (see also Kozeniauskas, Orlik,
and Veldkamp, 2014). They build a dynamic stochastic general equilibrium (DSGE) model
to study how the economy reacts to uncertainty shocks. In their model, a single process
determines the economy’s uncertainty regime, leading to two possible scenarios. Speci…cally,
both micro- and macroeconomic uncertainty can be either high or low. In this study, we
extend their analysis and empirically examine the e¤ects of high macro and microeconomic
uncertainty relative to scenarios where only one type of uncertainty is high, in addition to
scenarios where both types of uncertainty are low (four possible cases). We argue that the
e¤ects of uncertainty are most pronounced when economic agents are uncertain about both
macroeconomic growth and …rm-level prospects because the combined/interactive e¤ects are
larger than the sum of the individual a¤ects. We aim to show that the e¤ects of uncertainty
are not driven solely by high levels of uncertainty but also by the interaction of …rm-level
and macroeconomic uncertainty.
Our hypothesis is based on how investors and …rms react to di¤erent types of uncertainty.
Consider an economy with two …rms, A and B. In the …rst scenario, investors have some
certainty about overall economic growth but are uncertain about which …rm will succeed.
Investors will …nd it di¢ cult to reallocate resources between the …rms, as they cannot identify
6
where capital and labor will be most productive.3 However, because investors know that the
overall economy— that is, …rms A and B together— will grow, they can invest in both …rms.
On average, their investment will succeed as long as the economy grows. While productivity
and growth will su¤er from over- (under-) investment in the less (more) productive …rm,
investment will still occur as long as investors can diversify and invest in both …rms. In sum,
as long as the overall economy grows, investment will occur, though at somewhat slower pace
than when there is no …rm-level uncertainty.
Alternatively, consider a second scenario where investors know that …rm A will be more
productive than …rm B but are uncertain about the growth of the economy. In this scenario,
investors do not know how much to invest overall, because of the di¢ culty forecasting growth.
However, they do know that, all else equal, diverting labor and capital from …rm B to
…rm A will increase e¢ ciency and thus productivity and growth. Thus overall aggregate
investment will be lower due to aggregate uncertainty, but some reallocation of resources
and investment will still occur, increasing productivity and growth. Similar to the …rst
scenario, diversi…cation and resource allocation allow investors and …rms to react to, and
mitigate, the e¤ects of a single and speci…c form of uncertainty.
Finally, consider the scenario where investors are uncertain about both overall economic
growth as well as which …rm in the economy will be more productive and pro…table. In
this scenario, not only will overall investment decline due to uncertainty about aggregate
performance, but investment will also be a¤ected by the di¢ cultly of reallocating resources.
In such a scenario, diversi…cation helps mitigate the e¤ects of …rm-level uncertainty but
does not mitigate the e¤ects of aggregate-level uncertainty. Hence it simply replaces one
form of uncertainty with another and does not help investors/…rms protect themselves
against the e¤ects of uncertainty. Thus aggregate uncertainty exacerbates the e¤ects of
…rm-level uncertainty. Also, cross-sectional resource allocation, which helps mitigate the
e¤ects of aggregate uncertainty on investments, is di¢ cult in this scenario because it is
not clear which …rm, if any, will better employ the resources. Thus …rms and investors
have less ability to react to, and mitigate, the e¤ects of aggregate uncertainty during such
3
Temporary resource misallocation can also be an outcome of ambiguity aversion (Caskey 2009).
7
periods. Thus …rm-level uncertainty exacerbates the e¤ects of macroeconomic uncertainty.
Therefore we expect the negative e¤ects of uncertainty on macroeconomic activity to be
most pronounced during periods when aggregate as well as …rm-level uncertainty are high,
due to their interactive e¤ect.
2.1
Sectoral Shifts and Unemployment
The relation between earnings dispersion and macroeconomic conditions is also driven by
the reallocation of labor in the economy. In addition to business cycles, economists have long
recognized that sectoral shifts are one of the main drivers of unemployment. The underlying
causes for unemployment under this theory are frictions in the labor market and layo¤s.4
Lucas and Prescott (1974) and Lilien (1982) develop and empirically test the sectoral shift
theory.5 They argue that unemployment depends not only on the state of the economy and
the overall hiring rate but also on the amount of layo¤s. Since it takes time for employees to
…nd new jobs, layo¤s increase unemployment, irrespective of the overall hiring rate. Consider,
for example, an economy that is growing at 2%, where all …rms have a growth rate of 2%. In
such an economy, layo¤s are minimal because all …rms are using employees e¢ ciently, and
no gains are achieved by reallocating employees across …rms. In contrast, consider a di¤erent
economy that is also growing at 2%. However, in this economy, …rms grow at di¤erent rates.
Some …rms grow at rates signi…cantly above 2%, while others have negative growth rates. In
such an economy, the poor performers lay o¤ employees. Since these employees need time to
move to the more productive …rms, unemployment rises. Thus while both economies grow at
the same rate, dispersion in performance is associated with higher levels of unemployment.
According to the sectoral shift hypothesis, macroeconomic conditions as well as dispersion
a¤ect unemployment through layo¤s. We extend the sectoral shift hypothesis and argue
4
These frictions include job training, education, geographical distance, and search costs, among others.
They are the main reason why economists consider a four to six percent unemployment rate as full
employment as opposed to a zero unemployment rate.
5
Since the work of Lilien (1982), the economics literature has debated whether sectoral shifts or business
cycles are the main driver of unemployment (e.g., Abraham and Katz, 1986; Hosios, 1994; Lazear and
Spletzer, 2012). This literature does not examine how these two e¤ects interact.
8
that, since layo¤s occur when …rm performance is low, overall layo¤s are highest during
periods of low aggregate growth and high dispersion. In other words, there is an interactive
e¤ect. This is because the proportion of …rms performing poorly, which are likely to layo¤
employees, is highest when the economy is performing poorly and cross-sectional dispersion
is high. Thus Lilien’s (1982) model suggests that unemployment is jointly determined by
aggregate economic growth and dispersion. Prior studies include aggregate economic growth
and dispersion independently. However, the correct speci…cation with which to examine
Lilien’s (1982) model is to employ an interaction term. The use of an interaction term
captures the idea that aggregate performance and dispersion jointly determine layo¤s and
unemployment.
Lilien’s (1982) model also assumes a constant hiring rate, which implies that the
reallocation of employees is time invariant (an assumption questioned by Lilien as well).
In other words, in this model, dispersion a¤ects unemployment only because it increases
layo¤s. We extend Lilien’s (1982) model to include the uncertainty framework introduced
by Bloom (2009). As discussed above, empirical and analytical studies on uncertainty suggest
that employers are generally more reluctant to hire during periods when uncertainty is
high. Therefore, according to these studies, hiring rates are not constant. Hence employee
migration during periods of poor economic growth and high dispersion will take longer,
due to increased uncertainty, and lengthier migration results in greater unemployment. In
other words, when the uncertainty framework introduced by Bloom (2009) is superimposed
onto Lilien’s (1982) sectoral shift model, the hiring rate becomes a decreasing function of
uncertainty. Since we hypothesize that the implications of uncertainty are most pronounced
during periods of both high aggregate and …rm-level uncertainty, we expect unemployment to
increase more signi…cantly during periods when both macro- and microeconomic uncertainty
are high. In sum, one reason why the impact of uncertainty is most pronounced when both
…rm-level and aggregate-level uncertainty interact, is because unemployment increases more
signi…cantly due to their interactive e¤ect.
9
3
Variable Measurement and Research Design
3.1
Estimating
Aggregate
Uncertainty
Shocks,
Firm-Level
Uncertainty Shocks, and the Interaction of Aggregate and
Firm-Level Uncertainty
Our empirical analysis focuses on the relation between the interaction of aggregate and
…rm-level uncertainty, and macroeconomic conditions. A large literature in economics and
…nance suggests that persistent variables can provide misleading predictive evidence (e.g.,
Yule 1926; Granger and Newbold, 1974; Ferson et al., 2003). Speci…cally, if two variables are
highly persistent over time, a regression including one as a dependent variable and one as an
independent variable is likely to …nd evidence of predictability, even if the two variables are
unrelated. Persistent variables are ones that have large autocorrelations. Macroeconomic
variables are highly persistent, as are aggregate earnings and dispersion measures.
To overcome the spurious regression bias, Campbell (1991) and Ferson et al. (2003)
suggest stochastic de-trending of persistent variables. That is, removing the persistent
component in both the independent and dependent variables. In our analysis, we use an
AR (2) model to isolate the persistent component of all the key variables. We then employ
the residuals from the AR(2) models in our analysis. We refer to the residuals as shocks.
This should alleviate concerns related to the spurious regression bias. In addition, since our
variables have no serial correlation, there is no serial correlation in the error term. Therefore
we do not need to correct for any time-series autocorrelation in the residuals when employing
the shocks in our regression analysis. In untabulated robustness tests we re-estimate our main
regression models using Newey West adjusted standard errors and …nd identical results.
To measure aggregate uncertainty, we utilize a performance based measure and the
economic policy uncertainty index employed in Baker et al., 2015. We view periods of
low aggregate performance and periods of high policy uncertainty, as periods with high
aggregate uncertainty (e.g., Barry and Brown, 1985, and Bloom et al., 2012). We also employ
a performance based measure because the sectoral shift hypothesis described in Section 2
10
predicts that the interaction of dispersion and aggregate performance a¤ects unemployment,
even if aggregate performance does not capture uncertainty. Our performance-based measure
is an aggregate earnings measure, computed using forward-looking analyst forecasts. We
estimate aggregate earnings shocks in three steps. First, we measure the revision in earnings
expectations for …rm i during quarter t for the next …scal year (one-year-ahead):
Et (earni;s+1 ) Et
Pi;t 1
t
revi;s+1
=
1
(earni;s+1 )
(1)
t
where revi;s+1
is the revision in quarter t. s represents the current year, while s + 1
represents the next full year. Et (earni;s+1 ) is the median analyst earnings forecast at the
end of quarter t for …rm i, for the year s + 1. Pi;t
end of quarter t
1
is the price per share for …rm i at the
1.
Second, we estimate aggregate revisions for each quarter as the equally weighted average
of all …rm-level revisions:
Nt
1 X
AggREVt =
revit
N i=1
(2)
where AggREVt equals the aggregate revision for quarter t, and N is the number of
eligible …rms (common stocks) during that quarter.
Third, because aggregate revisions in earnings are more persistent than at the …rm-level,
we use the following AR (2) model to estimate the aggregate earnings shock:
AggREVt =
0
+
1
AggREVt
1
+
2
AggREVt
2
+ et
(3)
where et is the aggregate earnings shock for quarter t. Finally, we convert aggregate
earnings shocks into a binary variable (REVt ). Speci…cally, aggregate earnings (revisions)
shocks in the lowest quartile are assigned a value of one, and the rest of the observations
are assigned a value of zero. (We explain the rationale for converting aggregate earnings
11
shocks into a binary variable below.) Put di¤erently, the most negative quartile of aggregate
earnings shock quarters is assigned a value of one, and the remaining quarters receive a value
of zero.6 REVt is the variable we use in our analysis (see model 5a below).
As an alternative measure of aggregate uncertainty, we employ the economic policy
uncertainty index (Baker et al., 2015). We follow similar steps to the ones used when
computing REVt . First, we use an AR(2) model similar to equation (3) to compute the
aggregate uncertainty shock.7 Next, we de…ne the variable U nc, which is an indicator variable
equal to one for the highest quartile of political uncertainty shocks. In other words, the
variable U nc receives the value of one for the most positive quartile of political uncertainty
shock quarters and zero otherwise. We view periods of low performance and high policy
uncertainty as periods of high aggregate, or macroeconomic, uncertainty. Therefore we
employ the variable U nc in our alternative speci…cation (see model 5b below).
To measure …rm-level (microeconomic) uncertainty, we employ a dispersion based
measure (e.g., Bloom et al., 2012). To estimate earnings dispersion, we estimate earnings
revisions as described in step 1 above. Next, we estimate earnings dispersion as the standard
deviation of revisions in a quarter as follows:
v
u
Nt
u1 X
t
Dist =
(revit
N i=1
AggRevt )2
(4)
where Dist is the dispersion for quarter t, AggRevt is the aggregate earnings revision for
quarter t, and n is the number of …rms (eligible common stocks) during that quarter. Finally,
to isolate the nonpersistent component in earnings dispersion, we employ an AR (2) model,
similar to equation (3) above. The AR (2) residual is the proxy we use to measure earning
dispersion shocks (Rev_Disp).8 Rev_Disp is the variable we employ in our analysis (see
models 5a and 5b).
6
The AR (1) coe¢ cient of aggregate revision shocks is 0.02 and is statistically insigni…cant (t-value 0.21),
suggesting that the AR (2) process does a good job of isolating aggregate revision shocks.
7
The AR (1) coe¢ cient of aggregate uncertainty shocks is -0.03 and is statistically insigni…cant (t-value
-0.28), suggesting that the AR (2) process does a good job of isolating aggregate uncertainty shocks.
8
The AR (1) coe¢ cient for earnings dispersion shocks is -0.02 and statistically insigni…cant (t-value -0.23),
suggesting that the AR (2) process does a good job of isolating earnings dispersion shocks.
12
Finally, we de…ne the interaction term between aggregate uncertainty shocks and
…rm-level uncertainty shocks (earnings dispersion shocks).
We convert the aggregate
uncertainty variables into binary variables (as described above). We do this because both
…rm-level uncertainty shocks and aggregate uncertainty shocks can obtain positive and
negative values, which in turn a¤ect the sign of the interaction term. For example, a
positive shock to both aggregate uncertainty and earnings dispersion, and a negative shock
to both aggregate uncertainty and earnings dispersion, both result in a positive sign for
the interaction term. However, the two scenarios are economically di¤erent. To address
this issue, we convert the aggregate uncertainty variables into binary variables. Thus our
aggregate uncertainty measures are always nonnegative, and the interaction term will not
have the same sign in the two scenarios described above. In our empirical models, we
include aggregate GDP as an additional continuous control variable. This variable is added
to ensure that our results are not driven by the use of a binary variable to measure aggregate
uncertainty.
3.2
Research Design
We test whether the interaction of aggregate and …rm-level uncertainty is associated with
investment, unemployment, and industrial production. To test our hypothesis, we estimate
the following time-series regression model for each of our macroeconomic indicators.
M acro_indt =
0
+
1
Revt +
Rev_Dispt +
2
[Revt Rev_Dispt ]
3
+
4
[D_GDPt ] +
5
[D_Const ] +
+
7
[D_Y ieldt ] +
8
[D_Inft ] +
6
9
(5a)
[D_T ermt ]
[D_Deft ] + "t
where M acro_indt is one of the three macroeconomic indicators examined, D_Invt ,
D_U nempt , and D_Indt , which equal the residuals from an AR (2) model of quarterly
investment, unemployment rates, and industrial production growth, respectively. Rev is an
aggregate earnings dummy equal to one for the lowest quartile of aggregate earnings shocks.
13
Rev_Disp measures earnings dispersion shocks using the residuals from an AR(2) model of
earnings dispersion. As we note above, since the variables employed in the regression are
residuals from AR(2) models, they are not serially correlated. Therefore, we do not need to
correct for any time series correlation in the error terms.
Our predictions for investment and industrial production are as follows. First, we expect
a negative coe¢ cient on aggregate earnings shocks,
1
< 0. A negative coe¢ cient implies
that investment and industrial production are lower when aggregate pro…tability is lower.
In other words, investment and production are lower during contractions. Second, if higher
levels of …rm-level uncertainty (dispersion in earnings) impede investment, we expect the
coe¢ cient for earnings dispersion to be negative, that is,
2
< 0. Finally, we hypothesize
that the e¤ects of dispersion are conditional on the amount of aggregate uncertainty in
the economy because the two types of uncertainty have an interactive e¤ect. Speci…cally, we
expect the impact of dispersion on investment and industrial production to increase (become
more negative) when aggregate pro…tability is lower. Therefore we expect the coe¢ cient on
the interaction term to be negative, that is,
3
< 0. In other words, the adverse e¤ects of
…rm-level uncertainty on investment and production are exacerbated during periods of low
or negative economic growth, when aggregate uncertainty is high.
Our predictions for unemployment have the opposite sign. First, we expect a positive
coe¢ cient on aggregate earnings shocks,
1
> 0.
A positive coe¢ cient implies that
unemployment is higher when aggregate pro…tability is lower. In other words, unemployment
is higher during contractions. Second, if higher levels of dispersion in earnings increase
unemployment, we expect the coe¢ cients on the dispersion measures to be positive, that
is,
2
> 0. Finally, we hypothesize that the e¤ects of dispersion are conditional on the
state of the economy. Speci…cally, we expect the impact of dispersion on unemployment to
increase (become more positive) when aggregate pro…tability is lower. Therefore we expect
the coe¢ cient on the interaction term to be positive, that is,
3
> 0. In other words, the
adverse e¤ects of dispersion on employment are exacerbated during periods of low or negative
economic growth, when aggregate uncertainty is high.
In an alternative speci…cation, we replace our performance-based measure with the policy
14
uncertainty index, and estimate the following model:
M acro_indt =
+
0
1
U nct +
Rev_Dispt +
2
[U nct Rev_Dispt ]
3
+
4
[D_GDPt ] +
5
[D_Const ] +
+
7
[D_Y ieldt ] +
8
[D_Inft ] +
[D_T ermt ]
6
9
(5b)
[D_Deft ] + "t
where U nc is an indicator variable equal to one for the highest quartile of policy
uncertainty shocks.
1;
2;
3,
Our predictions for
1;
2;
3
are the same as our predictions for
because we expect aggregate uncertainty to be higher during periods of low
performance and high policy uncertainty.
3.3
Unemployment and Industrial Production Forecast Error
Data
Unemployment and industrial production forecast error data are obtained from the Survey
of Professional Forecasters (SPF). We are interested in the precision of the predictions as
well as the forecast errors. Therefore in addition to the signed forecast errors, we examine
absolute forecast errors. The forecast error is estimated as follows:
F E = Actualq+1
F orecastq+1
q
(6)
where F E equals the forecast error, Actualq+1 is the realized macroeconomic value
released in quarter t+1, and F orecastq+1
is the consensus SPF forecast of the macroeconomic
q
variable, based on the median forecast for quarter t+1, as of quarter t. The absolute forecast
errors are de…ned as follows:
AF E = Actualq+1
15
F orecastq+1
q
(7)
where AFE equals the absolute forecast error (Baghestani, 2009).
3.4
Sample
Our sample is constructed from the intersection of I/B/E/S, CRSP, and Compustat during
1985 to 2011. We restrict the sample to ordinary common shares (share codes 10, 11) that
are traded on the NYSE, AMEX, or NASDAQ exchanges. Further, to align data across
…rms, we include only …rms with …scal year-ends in March, June, September, or December.
Finally, every quarter, we winsorize the extreme two percent of observations when calculating
the aggregate earnings revision measures.9
Macroeconomic data are collected from FRED, the Federal Reserve Bank of St. Louis.
Investment equals the quarter-over-quarter growth in seasonally adjusted U.S. aggregate
gross private domestic investment (measured and reported by the U.S. Department of
Commerce’s Bureau of Economic Analysis). The quarterly average unemployment data
(averaged over 3 months) is the seasonally adjusted civilian unemployment rate, which is
de…ned as the number of unemployed people as a percent of the labor force (measured and
reported by the U.S. Department of Labor’s Bureau of Labor Statistics). The quarterly
average industrial production data (averaged over three months) is the seasonally adjusted
real output in production, expressed as a percentage growth term (measured and reported
by Board of Governors of the Federal Reserve System). Economic policy uncertainty is
the average quarterly (three month average) economic policy uncertainty index from Baker
et al., 2015. The index is constructed from three components: (1) newspaper coverage of
policy-related economic uncertainty, (2) the number of federal tax code provisions set to
expire in future years, and (3) disagreement among economic forecasters.10 Unemployment
and industrial production forecast error data is obtained from the Survey of Professional
Forecasters (SPF).11
9
Our results are robust to winsorizing the extreme one percent of observations.
The
economic
policy
uncertainty
data
is
taken
from
the
webpage:
http://www.policyuncertainty.com/us_monthly.html.
11
http://www.phil.frb.org/research-and-data/real-time-center/survey-of-professional-forecasters/
10
16
3.5
Descriptive Statistics
Table 1 presents descriptive statistics for the macro and uncertainty variables. Our sample
includes 105 quarters from the fourth quarter of 1985 up to and including the fourth quarter
of 2011. The mean values of the macro and dispersion variables are zero by construction,
as these estimates are residuals from various AR (2) speci…cations.
More speci…cally,
the mean values of investment shocks (D_Invt ), unemployment shocks (D_U nempt ),
industrial production shocks (D_Iprodt ), and earnings dispersion shocks (Rev_Dispt ),
hereafter earnings dispersion, are zero. The median dispersion measure has a negative value,
while the median unemployment and industrial production shocks have a value of zero.
The median investment shock is slightly positive. One-quarter-ahead mean and median
unemployment forecast errors are negative. That is, unemployment forecasts are higher
than their realizations. This evidence suggests that unemployment forecasts are optimistic.
Industrial production forecast errors are on average negative, but the median forecast error
is positive. Therefore unlike unemployment forecasts, we do not observe any optimism in
industrial production forecasts. This evidence is consistent with …ndings in prior literature
that show macro forecasts are not systematically optimistic (Hann et al. 2012).
The univariate results are presented in Table 2.
Consistent with our expectations,
aggregate earnings shocks are negatively related to investment and industrial production
shocks and positively related to unemployment shocks. For example, aggregate earnings
shocks have a Pearson (Spearman) correlation of -0.36 (-0.31) with investment shocks.
That is, investment shocks are lower during periods of lower aggregate pro…tability. The
correlations between political uncertainty shocks, our alternative measure of aggregate
uncertainty, and the macroeconomic indicators yield similar inferences.
Additionally,
investment and industrial production shocks are negatively correlated with earnings
dispersion, while unemployment shocks are positively related to earnings dispersion. Finally,
investment and industrial production shocks are negatively correlated with the interaction
term, and unemployment shocks are positively related to the interaction term. For example,
the Pearson correlation between investment shocks and the interaction terms ranges from
17
-0.45 to -0.50, depending on the measure of aggregate uncertainty employed.
4
4.1
Empirical Analysis
The
Interaction
of
Firm-level
and
aggregate-Level
Uncertainty, and Macroeconomic Shocks
The multivariate results are presented in Tables 3–5. Panel A in each table presents the
results using our earnings-based measure of aggregate uncertainty. Panel B presents the
results using the policy uncertainty index as our measure of aggregate uncertainty. The
investment-related results are presented in Table 3. Consistent with our predictions, the
coe¢ cients for the aggregate uncertainty indicator variables are negative. The coe¢ cient
estimates range from -1.79 to -2.50.
These results suggest that periods with negative
aggregate earnings shocks (in the bottom quartile) and positive policy uncertainty shocks,
have lower investment shocks. These results are statistically signi…cant at the 1 percent level.
Aggregate uncertainty explains 6–13 percent of the variation in investment shocks. Firm-level
uncertainty shocks are also negatively related to investment shocks and explain more of the
variation, with an R-squared of 18 percent. When both aggregate and …rm-level uncertainty
shocks are included simultaneously in the regression model, the R-squared increases to 20
percent. However, when we interact the measures of aggregate uncertainty and …rm-level
uncertainty, the interaction term is signi…cantly negative, and the R-squared increases to
24 and 25 percent across the speci…cations. Furthermore, the joint e¤ect of …rm-level and
aggregate-level uncertainty on investment is statistically signi…cant in both panels (F -values
of 16.64 and 22.17). These results highlight that the combined e¤ects of aggregate and
…rm-level uncertainty are larger than the sum of the individual e¤ects. In other words,
uncertainty impedes investment most when investors and …rms are uncertain about the
overall growth in the economy, and also about which …rms/industries/sectors are likely to
succeed. Figure 1 present the results graphically, further highlighting the strong relation
between our interaction term and investment shocks.
18
The unemployment-related results are presented in Table 4.
Consistent with our
predictions, the coe¢ cient on our aggregate uncertainty measures are positive.
coe¢ cient estimates range from 0.08 to 0.15 across the panels.
The
These results suggest
that periods with high aggregate uncertainty shocks have higher unemployment shocks
and are consistent with the considerable amount of prior evidence which demonstrates
that unemployment rises during periods of low economic growth (e.g., Abraham and Katz,
1986). Aggregate uncertainty shocks explain 2-10 percent of the variation in unemployment
shocks.
The results in column (2), in Panels A and B, are also consistent with the
arguments presented in Lilien (1982). On its own, dispersion explains 16 percent of the
shocks to unemployment. Including aggregate uncertainty and dispersion together increases
the explanatory power of the model. Speci…cally, the R-squared in column (3) increases
weakly to 16–22 percent, depending on the measure of aggregate uncertainty employed.
This evidence suggests that aggregate earnings and earnings dispersion together do a better
job of explaining unemployment shocks, relative to each measure independently.
More interestingly, the results in column (4) are consistent with our hypothesis that
the e¤ects of dispersion on unemployment are conditional on the state of the economy.
The coe¢ cient for the interaction term in Panel A is positive and statistically signi…cant.
Moreover, the R-squared increases by over 50% when we include the interaction term. These
results support our hypothesis that the correct speci…cation with which to test Lilien (1982)’s
model is with an interaction term. We …nd similar results when we employ the policy
uncertainty index as a measure of aggregate uncertainty, in Panel B. The joint e¤ect of
the interaction terms on unemployment is also statistically signi…cant across the panels.
(F -values of 29.53 and 20.81). Figure 2 further highlights the strong relation between the
interaction term and unemployment shocks. Taken together, this evidence highlights the
importance of the relation between dispersion, and aggregate performance and uncertainty,
when explaining unemployment shocks.
The multivariate regression results related to industrial production are presented in Table
5. These results provide further evidence consistent with our predictions. The coe¢ cient
on aggregate uncertainty shocks in column (1) is negative across both panels. Aggregate
19
uncertainty explains 5–7 percent of the variation in industrial production shocks. Next, we
examine the relation between dispersion and industrial production. In column (2), we show
that dispersion explains 14 percent of the shocks to industrial production. Furthermore,
including aggregate earnings or policy uncertainty and dispersion together (column (3))
increases the explanatory power of the model. Speci…cally, the R-squared increases weakly
to 14–17 percent depending on the measure of aggregate uncertainty used.
The results in Table 5 are also consistent with our hypothesis that the e¤ects of dispersion
on industrial production are conditional on the state of the economy. The coe¢ cient on
the interaction term in column (4) is negative and statistically signi…cant in both models.
Furthermore, the interaction term explains an additional 5-6 percent of the variation in
industrial production shocks. Moreover, the joint e¤ect of dispersion and the interaction on
industrial production is statistically signi…cant (F -values of 17.09 and 21.71). The evidence in
these analyses suggests that aggregate uncertainty and …rm-level uncertainty shocks jointly
explain industrial production shocks better than each measure individually. The relation
between the interaction term and industrial production shocks presented in Figure 3 also
highlights the importance of the relation between aggregate uncertainty shocks and …rm-level
uncertainty, or dispersion, in understanding industrial production shocks.
Additionally, in Tables 3-5, the speci…cation in column (5) includes the various
macroeconomic control variables. Including these control variables adds to the explanatory
power of the model. For example, in Table 3 Panel A, the adjusted R-squared increases by
2 percent when we include the control variables. Not surprisingly, real GDP growth has a
signi…cant association with investment, unemployment, and industrial production. While the
control variables enhance the explanatory power of the model, they do not signi…cantly alter
the relation between the interaction term and the macroeconomy. These …ndings suggest that
the observed relation between the interaction term and the macroeconomy is not attributable
to prior known factors.
In sum, both aggregate uncertainty and cross-sectional dispersion in …rm-level
performance, or …rm-level uncertainty, are associated with lower levels of macroeconomic
activity (investment, unemployment, and industrial production shocks).
20
Furthermore,
we …nd that the e¤ects of dispersion are exacerbated during periods of high aggregate
uncertainty. Therefore the addition of an interaction between aggregate uncertainty and
earnings dispersion substantially improves our understanding of how uncertainty relates to
the macroeconomy.
4.2
Alternative Cuto¤s
As we explain in Section 3, the measures of aggregate uncertainty employed in our analyses
are indicator variables that measure aggregate earnings shocks in the lowest quartile (Rev)
and the most positive quartile of political uncertainty shock quarters (U nc). We further
explain the requirement to use indicator variables in Section 3.2. To ensure that our results
are not sensitive to the cuto¤ used to de…ne the indicator variables, we reestimate the models
presented in Tables 3–5, column (5), using alternative cuto¤s to de…ne the indicator variables.
More speci…cally, we rede…ne Rev to equal 1 for observations in the lowest quintile, tercile,
median, and for all negative shocks. Similarly, we rede…ne U nc to equal 1 for observations
in the highest quintile, tercile, median, and for all positive shocks. The results from this
analysis are presented in Table 6. The results for investment, unemployment, and industrial
production shocks are presented in Panels A, B, and C, respectively. Our main results
are robust to the alternative cuto¤s. We continue to …nd a robust relation between our
interaction term and macroeconomic indicators. The main di¤erence relative to Tables
3–5, is that the relation between industrial production and the interaction term using the
policy uncertainty index (Panel C) is more sensitive to alternative cuto¤s. However, the
coe¢ cient remains negative in all the speci…cations, with t-statistics ranging form -1.49
to -2.77. Furthermore, the R-squared values presented across the panels are similar in
magnitude to those presented in Tables 3–5.
In untabulated analysis, we also examine how the relation between the interaction
between …rm-level and aggregate-level uncertainty and macroeconomic indicators di¤ers
across groups of high and low aggregate uncertainty. We …nd that the relation between the
interaction term and the macroeconomic shocks is signi…cantly stronger for the high group
21
(quarters with the highest quartile or tercile of aggregate uncertainty shocks) compared to
that of the low group.
4.3
Alternative Measures of Firm-Level and Aggregate-Level
Uncertainty
As an additional robustness test, we employ additional measures of uncertainty which are
commonly employed in the …nance literature. These measures are based on stock prices.
As an alternative measure of aggregate uncertainty, we employ the Chicago Board Options
Exchange (CBOE) market volatility index (the VIX index). As an alternative measure
of …rm-level uncertainty, we employ the average idiosyncratic-return volatility. For our
aggregate uncertainty based measure we …rst estimate the residuals from an AR (2) model
of the VIX index. We then de…ne the variable VIX, which is an indicator variable equal to
one for the highest quartile of residuals (VIX shocks).
We estimate the …rm-level uncertainty measure using the following steps. First, for
each …rm and quarter we estimate the daily idiosyncratic returns as the residuals from
the regression of daily stock returns on daily market, size, book-to-market, and momentum
factors (Fama and French (1993); Carhart, 1997). Second, for each …rm-quarter, we compute
the standard deviations of daily idiosyncratic returns (from step 1) and de…ne them as
…rm-level idiosyncratic volatility.
Third, using all …rms in a quarter, we compute the
average …rm-level idiosyncratic volatility (from step 2) and de…ne it as aggregate idiosyncratic
volatility. Finally, shocks to aggregate idiosyncratic volatility, Idio_vol, are de…ned as the
residuals from an AR (2) model of aggregate idiosyncratic volatility (from step 3).
The results using these alternative measures are reported in Table 7. The results are
consistent with our main …ndings. The addition of the interaction term between …rm-level
and aggregate-level uncertainty, signi…cantly increases the explanatory power of the model.
Also, the coe¢ cient on our interaction term is statistically signi…cant as well. Finally, adding
the interaction term signi…cantly reduces the independent e¤ect of idiosyncratic volatility,
and marginally reduces the e¤ect of VIX. These …nding further support our hypothesis that
22
the interaction of aggregate and …rm-level uncertainty helps explain macroeconomic activity.
4.4
Uncertainty Shocks and the Macroeconomy
One caveat of our empirical analysis so far is that the results only report associations and do
not imply a causal link. Bloom (2014) discusses the di¢ culty in determining causality in this
literature, as uncertainty can arise endogenously during recessions. Thus to identify causality,
empirical studies must identify exogenous shocks or instrumental variables to identify the
causal link between uncertainty and macroeconomic activity.
Dealing with causality is particularly challenging in our study because we focus on an
interaction term. This makes identifying exogenous variation di¢ cult, as we need exogenous
variation that is related to our interaction term but not to the individual components. In
other words, we would require a shock to the interaction term that does not impact dispersion
and aggregate uncertainty. Since such exogenous variation does not exist, determining
causality in our study is di¢ cult.
Nevertheless, we employ several exogenous shocks employed in Bloom et al. (2012)
to highlight the importance of the interaction between …rm-level and aggregate-level
uncertainty. We attempt to identify shocks that likely a¤ect aggregate uncertainty as well as
…rm-level uncertainty and compare them to shocks that a¤ect only one type of uncertainty. In
particular, we focus on three shocks. First, we focus on Bill Clinton’s presidential election in
1992, which represents the …rst Democratic president after 12 consecutive years of Republican
presidents. We expect such a shock to a¤ect policy uncertainty but not …rm-level uncertainty
(or dispersion). Second, we examine the Sept. 11, 2001, terrorist attacks. We expect the
attacks to a¤ect policy and aggregate uncertainty, as the U.S. was considering how to deal
with the implications of the event and future threats. In addition, since the attacks a¤ected
some industries more than others (e.g., airline and transportation), we expect dispersion and
…rm-level uncertainty to rise as well. Finally, we focus on the collapse of Lehman Brothers in
2008. We expect both aggregate uncertainty as well as …rm-level uncertainty to rise following
this event. The prospects of new …nancial regulations following the collapse increased policy
23
uncertainty in 2008. Also, since …rms vary in their sensitivity to shocks in the …nancial
industry (e.g., real estate and housing, banks), we expect dispersion to rise following this
event as well.
Consistent with our expectations, we …nd that only aggregate uncertainty rises following
the Clinton election in 1992, while both aggregate uncertainty and …rm-level uncertainty
rise following the other two other shocks. In other words, Sept. 11 and the collapse of
Lehman Brothers resulted in higher …rm-level and aggregate-level uncertainty. By comparing
the macroeconomic implications of these di¤erent shocks, we can examine how higher
levels of both …rm-level and aggregate-level uncertainty a¤ect the economy, compared to
how aggregate uncertainty alone a¤ects macroeconomic activity. If shocks that increase
both micro and macro uncertainty have more pronounced e¤ects compared to shocks to
aggregate uncertainty alone, then we can infer a more causal link between uncertainty and
macroeconomic activity.
We conduct our analysis at the annual level.12 For each event, we plot investment,
unemployment, and industrial production shocks surrounding the event. Our results are
presented in Figures 4–6. Consistent with our regression results presented earlier, in Figure 4,
we …nd that the Clinton election, which only a¤ected policy uncertainty, had a relatively mild
e¤ect on macroeconomic conditions. In contrast, Figure 5 shows how the Sept. 11 terrorist
attacks, which resulted in higher …rm-level and aggregate-level uncertainty, led to noticeable
negative declines in macroeconomic activity. Investment and industrial production shocks
are lowest in 2001, and employment shocks reach their peak during 2001 as well. A similar
picture emerges following the collapse of Lehman (Figure 6). For example, investment, which
experienced negative shocks throughout 2006 - 2009, reaches it lowest point in 2008. While
the e¤ects of Sept. 11 on macroeconomic activities were relatively short lived, the e¤ects of
the Lehman Brother’s collapse were longer lasting. One reason for this di¤erence is that,
in the post Sept. 11 period, the interaction term decreased (see Figures 1–3). In contrast,
the collapse of Lehman Brothers was followed by more periods of high uncertainty, both
12
For this analysis, we use the average of the quarterly residuals employed in Tables 3–5 to compute annual
levels of investment, unemployment, and industrial production shocks.
24
…rm-level and aggregate-level uncertainty. Therefore the collapse of Lehman Brothers had a
more severe and prolonged negative impact on macroeconomic activity.
In addition to these shocks, in unreported results, we also examine China’s accession to
the World Trade Organization (WTO) in 2005. Consistent with Bloom et al. (2012), we
…nd that dispersion increased following this event.13 However, as depicted in Figures 1–3,
the interaction term is not high during the same period. In other words, while dispersion
increased following the event, aggregate uncertainty did not. Therefore consistent with our
hypothesis, China’s accession to the WTO did not have signi…cant adverse e¤ects for U.S.
macroeconomic activity. These …ndings, which are similar to those presented in Bloom et
al. (2012), further show that the e¤ects of uncertainty on macroeconomic activity are most
pronounced when both …rm-level and aggregate-level uncertainty are high simultaneously,
because of their interactive e¤ect.
While the causality concerns related to our study remain, this analysis helps mitigate
the concern to some degree. The analysis (weakly) implies a more causal link between
uncertainty and macroeconomic activity. We further note that, in our analysis, we focus on
only three main events. We focus on these events because they are not surrounded by other
confounding e¤ects. For example, another identi…ed event, Black Monday (1987), follows
a signi…cant tax reform implemented in 1986, which makes it di¢ cult to draw inferences
related to the e¤ects of the Black Monday shock.
4.5
Employing Normalized Variables
In our primary analysis we use an indicator variable to measure aggregate uncertainty. As an
alternative approach, we normalize the earnings dispersion and aggregate-level uncertainty
shocks, and then add a constant to ensure that both variables are positive. Thus each variable
as well as the interaction term is always positive. We then use the normalized variables when
estimating models 5a and 5b, capturing the full variation in each variable. Our …ndings using
this approach are largely consistent with our primary results, using the indicator variable.
13
We …nd that the increase in dispersion occurred largely in the third quarter of 2006.
25
However, the standardized model is not intuitive to interpret. For brevity, we do not report
these results.
5
Additional Analysis
5.1
Firm-Level and Aggregate-Level Uncertainty and Market
Returns
As we note above, Jorgensen, Li, and Sadka (2012) show that earnings dispersion has a
strong association with aggregate stock returns. Speci…cally, they …nd a negative association
between aggregate stock returns and the cross-sectional standard deviation of earnings
changes (scaled by stock prices). The negative association between aggregate stock returns
and earnings dispersion is most pronounced between aggregate stock returns and future
earnings dispersion. Their …ndings are consistent with the vast amount of …rm-level evidence
showing that information in prices leads earnings (e.g., Collins, Kothari and Rayburn, 1987,
and Collins and Kothari, 1989).
Our …ndings in Tables 3–5 suggest that the relation
between earnings dispersion and the macroeconomy is conditional on the state of the
economy. Therefore we extend the analysis in Jorgensen, Li, and Sadka (2012) to examine
whether the relation between aggregate stock returns and future earnings dispersion is also
conditional on the state of the economy. Since the relation between earnings dispersion and
the macroeconomy is conditional on the state of the economy, we expect the relation between
aggregate stock returns and earnings dispersion to depend on the state of the economy.
The results from this analysis are presented in Table 8. To be consistent with Jorgensen,
Li, and Sadka (2012) we begin the analysis by computing aggregate earnings and earnings
dispersion using changes in quarterly earnings as opposed to revisions of earnings forecasts.
The results using these measures are reported in Panel A. Consistent with the results reported
in Jorgensen, Li, and Sadka (2012), we …nd a negative coe¢ cient on EARt+1 , which implies
that higher contemporaneous stock returns are associated with higher future earnings. As
for dispersion, our …ndings are also consistent with Jorgensen, Li, and Sadka (2012). We
26
show that lower aggregate stock returns are associated with higher future dispersion in
earnings (column (2)), and that the relation between aggregate stock returns and future
dispersion dominates the relation between aggregate stock returns and aggregate earnings
growth (column (3)). More importantly, the results in Table 8 are consistent with the
conclusions drawn from Tables 3–5. The relation between aggregate stock returns and
future earnings dispersion is conditional on the state of the economy. The coe¢ cient on
the interaction term is negative and statistically signi…cant. In addition, the explanatory
power of the model increases when the interaction term is included. For example, in Panel
A, the explanatory power increases from 12 to 16 percent.
We …nd similar results when we employ our performance based measure of aggregate
uncertainty (Panel B) and the policy uncertainty index (Panel C). In both panels, the
interaction term is negative and signi…cant, and the explanatory power of the model increases
when the interaction terms is included. Taken together, these …ndings suggest that the
surprising relation between aggregate stock returns and earnings dispersion is driven (at least
in part) by the relation between earnings dispersion, the interaction of earnings dispersion
and aggregate performance, and the macroeconomy.
5.2
Uncertainty and Macroeconomic Forecast Errors
In this section, we examine whether unemployment and industrial production forecast
errors are predicted by the interaction of …rm-level and aggregate-level uncertainty.14
Unemployment and industrial production forecast error data are obtained from the Survey
of Professional Forecasters (SPF).15 We are interested in the precision of the predictions as
well as the level of errors. Therefore we examine both absolute forecast errors and signed
forecast errors.
We examine both signed forecast errors and absolute forecast errors because each
14
In a similar vein, Konchitchki and Patatoukas (2013a) and Konchitchki and Patatoukas (2013b) show
that aggregate earnings predict GDP forecast errors.
15
The SPF does not forecast investment. Hence we restrict this analysis to unemployment and industrial
production.
27
one re‡ects a di¤erent aspect of forecasting.
Our tests using signed forecast errors
examine whether forecasters incorporate the implications of …rm-level and aggregate-level
uncertainty when making forecasts. More speci…cally, our analysis with signed forecast errors
examines whether forecasters understand that periods of high …rm-level and aggregate-level
uncertainty are followed by slower economic growth. In contrast, absolute forecast errors
measure the accuracy of the forecast. Since our interaction term is a measure of uncertainty,
we expect absolute forecast errors to be positively associated with the interaction term
because forecasting is more di¢ cult in periods of high uncertainty. This tests also helps us
validate that our interaction term is indeed a measure of uncertainty.
For this analysis, we only employ our performance-based measure of aggregate
uncertainty. Table 9, Panel A, presents the relation between unemployment forecast errors
and the interaction term. The results in columns (1) and (2) show that the interaction of
…rm-level and aggregate-level uncertainty predicts macroeconomists’absolute forecast errors.
The results in columns (3) and (4) show that the interaction term predicts macroeconomists’
signed forecast errors but not when controlling for other macroeconomic indicators. However,
while the coe¢ cient for the interaction term in column (4) is no longer signi…cant, it remains
positive and has a similar economic magnitude to the coe¢ cient in column (3). Furthermore,
the reduction in signi…cance likely arises from the number of variables included in the
regression, none of which have a signi…cant relation with the forecast error. Taken together,
this evidence implies that macroeconomic forecasters do not incorporate the relation between
…rm-level and aggregate-level uncertainty and unemployment in their forecasts. We note that
in the case of unemployment, the interaction term may explain macroeconomic activity even
in the absence of uncertainty, due to sectoral shifts.
In Panel B, we test whether our interaction term predicts absolute forecast errors related
to industrial production. We …nd that it predicts macroeconomists’absolute forecast errors,
as well as their signed forecast errors. Taken together, the results in Table 9 highlight
the importance of the interaction term in understanding and predicting macroeconomic
activity. In addition, the positive relation between …rm-level and aggregate-level uncertainty
and absolute forecast errors supports the hypothesis that the interaction term is related to
28
uncertainty about macroeconomic growth.
6
Conclusion
Economic theory suggests that both dispersion in performance, a measure of …rm-level
uncertainty, and aggregate uncertainty result in lower levels of macroeconomic activity (e.g.,
Bloom, 2009). We hypothesize and …nd that the e¤ects of uncertainty on macroeconomic
activity are most pronounced when both aggregate uncertainty and …rm-level uncertainty
are high simultaneously, because each type of uncertainty exacerbates the e¤ect of the
other.
Our …ndings imply that policies that potentially increase both …rm-level and
aggregate-level uncertainty have more signi…cant negative implications for economic activity,
and thus higher economic costs. We further show that the interaction between …rm-level and
aggregate-level uncertainty explains the puzzling relation between the equity premium and
earnings dispersion, and predicts unemployment and industrial production forecast errors.
29
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32
Figure 1. The Interaction of Aggregate Uncertainty and Firm-Level Uncertainty, and Investment
Shocks
15
0.035
0.03
10
0.025
0.02
5
0.015
0
0.01
0.005
‐5
‐0.005
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
0
‐10
‐0.01
‐0.015
‐15
Interaction of Aggregate and Firm‐Level Uncertainty
Investment Shocks
Figure 1 presents shocks to aggregate investment for 105 quarters from Q4:1985 to Q4:2011. Shocks to
investment equal the residuals from an AR (2) model of investment. The interaction of Aggregate and FirmLevel Uncertainty equals the interaction between aggregate uncertainty shocks and firm-level uncertainty
shocks (earnings dispersion shocks). Aggregate uncertainty shocks equal 1 for the highest tercile of shocks
to the economic policy uncertainty index and zero otherwise. The magnitudes of investment shocks are
displayed on the right vertical axis, and the magnitudes of the interaction term are displayed on the left
vertical axis.
33
Figure 2. The Interaction of Aggregate Uncertainty and Firm-Level Uncertainty, and
Unemployment Shocks 1
0.035
0.03
0.8
0.025
0.6
0.02
0.4
0.015
0.2
0.01
0.005
0
‐0.005
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
0
‐0.2
‐0.4
‐0.01
‐0.015
‐0.6
Interaction of Aggregate and Firm‐Level Uncertainty
Unemployment Shocks
Figure 2 presents shocks to aggregate unemployment rates for 105 quarters from Q4:1985 to Q4:2011.
Shocks to unemployment rates equal the residuals from an AR (2) model of unemployment rates. The
interaction of Aggregate and Firm-Level Uncertainty equals the interaction between aggregate uncertainty
shocks and firm-level uncertainty shocks (earnings dispersion shocks). Aggregate uncertainty shocks equal
1 for the highest tercile of shocks to the economic policy uncertainty index and zero otherwise. The
magnitudes of unemployment rate shocks are displayed on the right vertical axis, and the magnitudes of the
interaction term are displayed on the left vertical axis.
34
Figure 3. The Interaction of Aggregate Uncertainty and Firm-Level Uncertainty, and Industrial
production Shocks 3
0.035
0.03
2
0.025
0.02
1
0.015
0
0.01
0.005
‐1
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
‐0.005
1985
0
‐2
‐0.01
‐0.015
‐3
Interaction of Aggregate and Firm‐Level Uncertainty
Industrial Production Shocks
Figure 3 presents shocks to aggregate industrial production for 105 quarters from Q4:1985 to Q4:2011.
Shocks to industrial production equal the residuals from an AR (2) model of industrial production. The
interaction of Aggregate and Firm-Level Uncertainty equals the interaction between aggregate uncertainty
shocks and firm-level uncertainty shocks (earnings dispersion shocks). Aggregate uncertainty shocks equal
1 for the highest tercile of shocks to the economic policy uncertainty index and zero otherwise. The
magnitudes of industrial production shocks are displayed on the right vertical axis, and the magnitudes of
the interaction term are displayed on the left vertical axis.
35
Figure 4. Clinton Election (1992)
1.5
1
0.5
0
1990
1991
1992
1993
‐0.5
‐1
‐1.5
‐2
‐2.5
Unemployment Shocks
Industrial Production Shocks
Investment Shocks
Figure 4 presents shocks to macroeconomic indicators (investment, unemployment, and industrial
production) for the period before and after the Clinton election, event years 1990–1993. The shocks to the
macroeconomic indicators equal the residuals from an AR (2) model of the respective indicator. The
magnitudes of the macroeconomic indicator shocks are displayed on the left vertical axis. Years are
displayed on the horizontal axis. 36
Figure 5. Sept. 11 Terrorist Attacks (2001)
1
0.5
0
1999
2000
2001
2002
‐0.5
‐1
‐1.5
‐2
‐2.5
‐3
Unemployment Shocks
Industrial Production Shocks
Investment Shocks
Figure 5 presents shocks to macroeconomic indicators (investment, unemployment, and industrial
production) for the period before and after the Sept. 11 terrorist attacks, event years 1999–2002. The shocks
to the macroeconomic indicators equal the residuals from an AR (2) model of the respective indicator. The
magnitudes of the macroeconomic indicator shocks are displayed on the left vertical axis. Years are
displayed on the horizontal axis. 37
Figure 6. Lehman Brothers Collapse (2008)
0.5
0
2006
2007
2008
2009
‐0.5
‐1
‐1.5
‐2
‐2.5
‐3
‐3.5
‐4
Unemployment Shocks
Industrial Production Shocks
Investment
Figure 6 presents shocks to macroeconomic indicators (investment, unemployment, and industrial
production) for the period before and after the collapse of Lehman Brothers, event years 2006–2009. The
shocks to the macroeconomic indicators equal the residuals from an AR (2) model of the respective
indicator. The magnitudes of the macroeconomic indicator shocks are displayed on the left vertical axis.
Years are displayed on the horizontal axis.
38
Table 1: Descriptive statistics
D_Invt
Mean
0.00
Std Dev
3.01
5 Perc
-5.11
Median
0.02
95 Perc
4.69
D_Unempt
0.00
0.20
-0.27
0.00
0.32
D_Iprodt
0.00
0.85
-1.39
0.00
1.18
Mktrett-1 (%)
1.16
9.21
-17.21
2.64
14.53
Unemp_FEt+1
-0.03
0.13
-0.23
-0.05
0.22
Unemp_AFEt+1
0.11
0.08
0.00
0.09
0.27
Iprod_FEt+1
-0.22
2.61
-4.58
0.06
3.27
Iprod_AFEt+1
2.02
1.65
0.16
1.56
5.27
Revt
0.25
0.43
0.00
0.00
1.00
Rev_Dispt (*100)
0.00
0.65
-0.65
-0.08
0.88
Revt (x) Rev_Dispt (*100)
0.14
0.38
0.00
0.00
0.76
UNCt
0.26
0.44
0.00
0.00
1.00
Unct (x) Rev_Dispt (*100)
0.06
0.38
-0.25
0.00
0.56
D_GDPt
0.00
0.54
-0.86
0.01
0.80
D_Const
0.00
0.49
-0.80
-0.05
0.89
D_Termt
0.00
0.42
-0.68
-0.05
0.76
D_Yieldt
0.00
0.17
-0.26
0.01
0.32
D_CPIt
0.00
0.49
-0.61
0.02
0.68
D_Deft
0.00
0.21
-0.21
-0.01
0.25
This table presents descriptive statistics for the macro, aggregate earnings, and uncertainty variables. The
sample includes 105 quarters from Q4:1985–Q4:2011. D_Invt is the residual from an AR (2) model of
quarterly private domestic investments, in quarter t. D_Unempt is the residual from an AR (2) model of
quarterly unemployment rates, in quarter t. D_Iprodt is the residual from an AR (2) model of quarterly
industrial production growth measures in percentages. Mktret is the quarterly value-weighted market return.
39
FE (AFE) is the forecast error (absolute forecast error) related to the macroeconomic indicator forecasted.
All forecast error data is obtained from the Survey of Professional Forecasters.
Rev is an indicator variable equal to one for the lowest quartile of aggregate earnings shocks. Aggregate
earnings shocks equal the residual from an AR (2) model of aggregate earnings revisions.
Aggregate earnings revisions are defined as the equally weighted average of firm-level earnings forecast
revisions, for one-year-ahead earnings, that occur during the current quarter, deflated by the beginning of
the quarter stock price.
t
i , s 1
rev
1
 E ( earni ,s 1 )  Et 1 ( earni , s 1 )  ,
 t
 Agg Re vt 


Nt
Pi ,t 1


Nt
(rev
i 1
t
i ,s 1
)
Earnings dispersion (Rev_Disp) is the residual from an AR (2) model of aggregate earnings dispersion
(AggDis). AggDis is the standard deviation of firm-level earnings forecast revisions, deflated by beginning
of the quarter stock price.
AggDist 
1
Nt
Nt
 ( rev
i 1
i ,t
 Agg Re vt ) 2
UNC is an indicator variable equal to one for the highest quartile of economic policy uncertainty shocks.
Economic policy uncertainty shocks equal the residuals from an AR (2) model of the economic policy
uncertainty index.
For the calculation of aggregate earnings revisions and dispersion, to align data across firms in a quarter,
we only include firms with fiscal year-ends in March, June, September, and December. Further, every
quarter, we winsorize the top and bottom 2 percent of observations when calculating aggregate earnings
and revision measures.
D_GDP is the AR (2) residual of seasonally adjusted quarterly real gross domestic product. D_CON is the
AR (2) residual of seasonally adjusted quarterly real personal consumption expenditures. D_Term is the
AR (2) residual of change in term spread (10-Year Treasury constant maturity rate minus three-Month
Treasury bill secondary market rate). D_Yield is the AR(2) residual of change in yield spread (effective
Federal Funds Rate minus three-Month treasury bill secondary market rate). D_Def is the AR (2) residual
of change in default spread (Moody’s seasoned Baa corporate bond yield minus Moody’s seasoned Aaa
corporate bond yield). D_CPI is the AR (2) residual of seasonally adjusted quarterly consumer price index.
40
Table 2: Correlation matrix
Inv
Rest
Unemp
Rest
Iprod
Rest
Mktrett1
Unemp
FEt+1
Unemp
AFEt+1
Iprod
FEt+1
Iprod
AFEt+1
Revt
Rev
Dispt
Revt (x)
Rev_Dispt
Unct
Unct (x)
Rev_Dispt
1
-0.39
0.51
0.32
-0.17
-0.08
0.26
0.06
-0.36
-0.43
-0.50
-0.26
-0.45
Unemp_Rest
-0.35
1
-0.55
-0.24
-0.04
0.23
-0.07
0.06
0.16
0.41
0.46
0.34
0.46
Iprod_Rest
0.47
-0.50
1
0.41
-0.02
0.06
-0.01
0.18
-0.25
-0.38
-0.44
-0.28
-0.46
Mktrett-1
0.13
-0.10
0.27
1
-0.10
0.03
0.08
0.07
-0.31
-0.56
-0.49
-0.31
-0.51
Unemp_FEt+1
-0.14
-0.09
-0.03
-0.11
1
-0.16
-0.55
0.47
0.24
0.12
0.16
0.06
0.13
Unemp_AFEt+1
-0.08
0.22
0.14
0.02
-0.28
1
-0.12
0.21
0.01
0.12
-0.05
0.01
-0.02
Iprod_FEt+1
0.21
-0.01
-0.01
0.05
-0.48
0.00
1
-0.41
-0.18
-0.25
-0.15
0.05
-0.16
Iprod_AFEt+1
0.18
0.01
0.20
0.15
0.36
0.13
-0.10
1
-0.05
0.16
-0.08
-0.06
-0.08
Revt
-0.31
0.12
-0.22
-0.21
0.26
-0.01
-0.21
-0.09
1
0.49
0.63
0.22
0.39
Rev_Dispt
-0.30
0.25
-0.25
-0.41
0.08
0.08
-0.11
-0.07
0.65
1
0.66
0.20
0.59
Revt (x) Rev_Dispt
-0.34
0.18
-0.25
-0.23
0.25
0.00
-0.22
-0.10
0.99
0.68
1
0.25
0.81
Unct
-0.23
0.33
-0.25
-0.27
0.04
0.00
0.03
-0.07
0.22
0.21
0.23
1
0.25
Unct (x) Rev_Dispt
-0.25
0.18
-0.34
-0.30
0.16
-0.01
-0.16
-0.16
0.44
0.49
0.45
0.18
1
Inv_Rest
This table presents Pearson (above diagonal) and Spearman (below diagonal) correlations among the key variables of interest for 105 quarters
from Q4:1985–Q4:2011. The sample and variable definitions are described in Table 1. Correlations significant at the 10% level or better are
highlighted in bold.
41
Table 3: Firm-level and aggregate-level uncertainty, and contemporaneous investment
shocks
Panel A: Measures of aggregate earnings and contemporaneous investment shocks
Dependent Variable : D_INVt
Intercept
Revt
(1)
(2)
(3)
(4)
(5)
0.62
(1.95)*
0.00
(0.00)
0.34
(1.07)
0.47
(1.53)
0.45
(1.47)
-1.36
(-1.95)*
-0.41
(-0.54)
0.15
(0.19)
-80.76
(-1.53)
-72.02
(-1.36)
-2.50
(-3.93)***
Rev_Dispt
-199.14
-154.81
(-4.86)*** (-3.34)***
Revt (x) Rev_Dispt
-269.70
-355.08
(-2.67)*** (-3.35)***
D_Const
-1.62
(-2.78)***
D_Termt
-0.05
(-0.08)
D_Yieldt
-1.56
(-1.00)
D_CPIt
-0.06
(-0.10)
D_Deft
-2.26
(-1.51)
Adj. R2
0.13
0.18
F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0
42
0.20
0.25
0.27
16.64***
22.42***
Table 3: Continued
Panel B: Measures of economic policy uncertainty and contemporaneous investment shocks
Dependent Variable : D_INVt
Intercept
Unct
(1)
(2)
(3)
(4)
(5)
0.46
(1.39)
0.00
(0.00)
0.32
(1.05)
0.38
(1.26)
0.36
(1.22)
-1.25
(-2.05)**
-0.98
(-1.62)
-0.86
(-1.37)
-182.55
(-4.44)***
-111.09
(-2.28)**
-108.99
(-2.24)**
-219.96
(-2.57)**
-256.03
(-2.93)***
-1.79
(-2.74)***
Rev_Dispt
-199.14
(-4.86)***
Unct (x) Rev_Dispt
D_Const
-1.53
(-2.68)***
D_Termt
0.10
(0.17)
D_Yieldt
0.01
(0.01)
D_CPIt
-0.26
(-0.43)
D_Deft
-1.71
(-1.13)
Adj. R2
0.06
0.18
0.20
F-test: Rev_Dispt+ Unct (x) Rev_Dispt =0
0.24
0.26
22.17***
25.27***
This table reports the contemporaneous relation between investment shocks and measures of uncertainty
for 105 quarters from Q4:1985 ̶ Q4:2011. All the variables are defined in Table 1. Panel A reports the
contemporaneous relation between investment shocks and uncertainty, where aggregate uncertainty is
derived from aggregate earnings. Panel B presents the contemporaneous relation between investment
shocks and uncertainty, where aggregate uncertainty is derived from the economic policy uncertainty index.
***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
43
Table 4: Firm-level and aggregate-level uncertainty, and contemporaneous unemployment
shocks
Panel A: Measures of aggregate earnings and contemporaneous unemployment shocks
Dependent Variable : D_UNEMPt
(1)
(2)
(3)
(4)
(5)
Intercept
-0.02
(-0.87)
0.00
(0.06)
0.01
(0.25)
-0.01
(-0.37)
0.00
(-0.16)
Revt
0.08
(1.69)*
-0.02
(-0.48)
-0.11
(-2.14)**
-0.10
(-1.99)**
13.44
(4.22)***
6.72
(1.90)*
7.00
(2.00)**
24.46
(3.63)***
20.32
(2.83)***
Rev_Dispt
12.69
(4.59)***
Revt (x) Rev_Dispt
D_GDPt
-0.10
(-2.57)**
D_Const
-0.01
(-0.32)
D_Termt
-0.03
(-0.80)
D_Yieldt
0.10
(0.94)
D_CPIt
0.02
(0.47)
D_Deft
0.03
(0.31)
Adj. R2
0.02
0.16
0.16
F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0
44
0.25
0.30
29.53***
20.12***
Table 4: Continued
Panel B: Measures of economic policy uncertainty and contemporaneous unemployment shocks
Dependent Variable : D_UNEMPt
Intercept
Unct
(1)
(2)
(3)
(4)
(5)
-0.04
(-1.89)*
0.00
(0.06)
-0.03
(-1.61)
-0.04
(-1.83)*
-0.03
(-1.66)*
0.12
(3.02)***
0.10
(2.60)**
0.10
(2.41)**
11.07
(4.08)***
6.24
(1.94)*
7.02
(2.23)**
14.88
(2.64)***
10.44
(1.78)*
0.15
(3.63)***
Rev_Dispt
12.69
(4.59)***
Unct (x) Rev_Dispt
D_GDPt
-0.08
(-2.27)**
D_Const
-0.04
(-0.99)
D_Termt
-0.05
(-1.19)
D_Yieldt
0.02
(0.17)
D_CPIt
0.02
(0.47)
D_Deft
-0.07
(-0.67)
Adj. R2
0.10
0.16
0.22
F-test: Rev_Dispt+ Unct (x) Rev_Dispt =0
0.27
0.31
20.81***
12.69***
This table reports the contemporaneous relation between unemployment shocks and measures of
uncertainty for 105 quarters from Q4:1985–Q4:2011. All the variables are defined in Table 1. Panel A
reports the contemporaneous relation between unemployment shocks and uncertainty, where aggregate
uncertainty is derived from aggregate earnings. Panel B presents the contemporaneous relation between
unemployment shocks and uncertainty, where aggregate uncertainty is derived from the economic policy
uncertainty index. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
45
Table 5: Firm-level and aggregate-level uncertainty, and industrial production shocks
Panel A: Measures of aggregate earnings and contemporaneous industrial production shocks
Dependent Variable : D_IPRODt
Intercept
Revt
(1)
(2)
(3)
(4)
(5)
0.13
(1.37)
0.01
(0.08)
0.05
(0.49)
0.09
(0.95)
0.05
(0.57)
-0.16
(-0.77)
0.13
(0.57)
0.15
(0.71)
-44.79
(-3.29)***
-22.41
(-1.45)
-23.44
(-1.67)*
-81.52
(-2.76)***
-56.67
(-1.97)*
-0.49
(-2.61)**
Rev_Dispt
-49.94
(-4.22)***
Revt (x) Rev_Dispt
D_GDPt
0.57
(3.83)***
D_Const
0.05
(0.28)
D_Termt
0.24
(1.48)
D_Yieldt
-0.34
(-0.82)
D_CPIt
0.02
(0.11)
D_Deft
-0.58
(-1.44)
Adj. R2
0.05
0.14
0.14
F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0
46
0.19
0.36
17.09***
10.73***
Table 5: Continued
Panel B: Measures of economic policy uncertainty and contemporaneous industrial production
shocks Dependent Variable : D_IPRODt
Intercept
Unct
Rev_Dispt
(1)
(2)
(3)
(4)
(5)
0.14
(1.54)
0.01
(0.08)
0.11
(1.25)
0.13
(1.50)
0.07
(0.94)
-0.40
(-2.29)**
-0.31
(-1.82)*
-0.15
(-0.95)
-44.61
(-3.77)***
-21.22
(-1.53)
-23.14
(-1.83)*
-71.99
(-2.96)***
-48.11
(-2.04)**
-0.53
(-2.91)***
-49.94
(-4.22)***
Unct (x) Rev_Dispt
D_GDPt
0.54
(3.66)***
D_Const
0.08
(0.50)
D_Termt
0.28
(1.72)*
D_Yieldt
-0.12
(-0.31)
D_CPIt
-0.01
(-0.09)
D_Deft
-0.47
(-1.18)
Adj. R2
0.07
0.14
F-test: Rev_Dispt+ Unct (x) Rev_Dispt =0
0.17
0.23
21.71***
0.38
13.10***
This table reports the contemporaneous relation between industrial production shocks and measures of
uncertainty for 105 quarters from Q4:1985–Q4:2011. All the variables are defined in Table 1. Panel A
reports the contemporaneous relation between unemployment shocks and uncertainty, where aggregate
uncertainty is derived from aggregate earnings. Panel B presents the contemporaneous relation between
industrial production shocks and uncertainty, where aggregate uncertainty is derived from the economic
policy uncertainty index. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively.
47
Table 6: Firm-level and aggregate-level uncertainty, and contemporaneous macro shocks: alternative cut-offs
Panel A: Contemporaneous investment shocks
Quintile
Intercept
Revt
(1)
0.34
(1.14)
0.45
(0.50)
Unct
Rev_Dispt
Revt (x) Rev_Dispt
Unct (x) Rev_Dispt
-91.32
(-1.75)*
-346.11
(-3.10)***
(2)
0.38
(1.35)
-1.11
(-1.67)*
-104.00
(-2.20)**
-275.46
(-3.15)***
D_Const
-1.80
-1.63
(-3.05)*** (-2.89)***
D_Termt
-0.01
0.16
(-0.02)
(0.27)
D_Yieldt
-1.49
-0.16
(-0.93)
(-0.10)
D_CPIt
-0.13
-0.17
(-0.22)
(-0.28)
D_Deft
-2.50
-1.63
(-1.63)
(-1.10)
Adj. R2
0.26
0.29
F-test: Rev_Dispt+ Interaction =0
20.76*** 26.72***
Tercile
-269.62
(-3.11)***
-1.66
-1.45
(-2.85)*** (-2.54)**
-0.09
-0.11
(-0.14)
(-0.18)
-1.38
-0.27
(-0.87)
(-0.18)
-0.09
-0.37
(-0.15)
(-0.60)
-2.44
-2.16
(-1.64)
(-1.44)
0.25
0.26
Median
(5)
(6)
0.43
0.46
(1.12)
(1.30)
0.02
(0.03)
-0.69
(-1.34)
-70.04
-74.15
(-1.23)
(-1.41)
-321.55
(-3.43)***
-281.87
(-3.50)***
-1.59
-1.36
(-2.72)*** (-2.42)**
-0.14
-0.02
(-0.23)
(-0.04)
-1.49
0.09
(-0.94)
(0.06)
-0.15
-0.36
(-0.25)
(-0.60)
-2.35
-2.15
(-1.57)
(-1.47)
0.26
0.27
Negative/Positive
(7)
(8)
0.38
0.34
(1.04)
(1.00)
0.16
(0.26)
-0.43
(-0.83)
-84.75
-74.50
(-1.49)
(-1.42)
-304.65
(-3.14)***
-291.10
(-3.58)***
-1.57
-1.38
(-2.69)*** (-2.45)**
-0.09
0.02
(-0.14)
(0.03)
-1.26
0.03
(-0.80)
(0.02)
-0.15
-0.38
(-0.24)
(-0.62)
-2.33
-2.28
(-1.55)
(-1.55)
0.25
0.27
25.65***
29.19***
25.60***
(3)
0.31
(0.92)
0.63
(0.91)
-88.63
(-1.59)
-339.03
(-3.29)***
(4)
0.42
(1.30)
-0.64
(-1.13)
-92.97
(-1.83)*
27.08***
48
34.46***
35.04***
Table 6: Continued
Panel B: Contemporaneous unemployment shocks
Quintile
Intercept
Revt
(1)
-0.01
(-0.44)
-0.10
(-1.73)*
Unct
Rev_Dispt
Revt (x) Rev_Dispt
Unct (x) Rev_Dispt
D_GDPt
6.25
(1.84)*
22.26
(3.02)***
(2)
-0.02
(-1.27)
0.09
(1.99)**
8.02
(2.53)**
8.11
(1.33)
-0.09
(-2.26)**
-0.04
(-0.87)
-0.05
(-1.28)
0.03
(0.27)
0.01
(0.24)
-0.06
(-0.64)
0.28
-0.09
(-2.58)**
D_Const
-0.01
(-0.25)
D_Termt
-0.03
(-0.84)
D_Yieldt
0.10
(0.96)
D_CPIt
0.02
(0.51)
D_Deft
0.04
(0.38)
Adj. R2
0.30
F-test: Rev_Dispt+ Interaction=0
20.10***
9.77***
Tercile
(3)
-0.02
(-0.75)
-0.05
(-1.15)
4.89
(1.35)
20.98
(3.06)***
(4)
-0.04
(-1.91)*
0.08
(2.29)**
5.38
(1.64)
-0.09
(-2.43)**
-0.02
(-0.51)
-0.04
(-0.90)
0.12
(1.18)
0.01
(0.36)
0.02
(0.17)
0.30
13.25
(2.28)**
-0.07
(-2.03)**
-0.05
(-1.18)
-0.03
(-0.80)
0.03
(0.35)
0.03
(0.75)
-0.02
(-0.24)
0.32
21.08***
15.82***
49
Median
-0.09
(-2.37)**
-0.03
(-0.74)
-0.04
(-0.88)
0.11
(1.01)
0.01
(0.36)
0.00
(0.03)
0.27
10.73
(1.90)*
-0.08
(-1.96)*
-0.05
(-1.23)
-0.04
(-1.09)
0.01
(0.13)
0.02
(0.55)
-0.03
(-0.27)
0.30
Negative/Positive
(7)
(8)
-0.01
-0.04
(-0.39)
(-1.93)*
-0.04
(-1.14)
0.08
(2.43)**
5.53
6.01
(1.50)
(1.74)*
18.42
(2.86)***
11.18
(1.98)**
-0.09
-0.08
(-2.57)** (-2.02)**
-0.02
-0.05
(-0.54)
(-1.17)
-0.03
-0.05
(-0.86)
(-1.15)
0.11
0.03
(1.07)
(0.32)
0.02
0.02
(0.47)
(0.60)
0.01
-0.02
(0.13)
(-0.24)
0.30
0.30
16.65***
15.96***
22.02***
(5)
-0.02
(-0.82)
0.00
(0.04)
5.28
(1.40)
15.14
(2.34)**
(6)
-0.04
(-1.81)*
0.08
(2.30)**
6.28
(1.79)*
16.04***
Table 6: Continued
Panel C: Contemporaneous industrial production shocks
Quintile
(1)
(2)
Intercept
0.03
0.04
(0.36)
(0.48)
Revt
0.25
(1.09)
Unct
-0.05
(-0.27)
Rev_Dispt
-25.96
-28.22
(-1.90)*
(-2.22)**
Revt (x) Rev_Dispt
-59.10
(-1.99)**
Unct (x) Rev_Dispt
-37.00
(-1.51)
D_GDPt
0.59
0.56
(4.02)*** (3.73)***
D_Const
0.02
0.07
(0.10)
(0.44)
D_Termt
0.24
0.27
(1.44)
(1.68)*
D_Yieldt
-0.30
-0.17
(-0.73)
(-0.41)
D_CPIt
0.00
0.02
(0.03)
(0.11)
D_Deft
-0.63
-0.48
(-1.55)
(-1.19)
Adj. R2
0.36
0.36
F-test: Rev_Dispt+Interaction=0
11.05***
9.93***
Tercile
0.55
(3.82)***
0.05
(0.28)
0.24
(1.48)
-0.41
(-1.02)
0.01
(0.05)
-0.61
(-1.57)
0.38
-63.73
(-2.77)***
0.49
(3.39)***
0.11
(0.68)
0.23
(1.46)
-0.17
(-0.45)
-0.06
(-0.37)
-0.59
(-1.53)
0.40
Median
(5)
(6)
0.11
0.15
(1.13)
(1.62)
-0.06
(-0.37)
-0.27
(-1.98)*
-14.50
-23.88
(-0.98)
(-1.71)*
-58.69
(-2.32)**
-33.44
(-1.49)
0.54
0.52
(3.68)*** (3.40)***
0.08
0.13
(0.50)
(0.82)
0.23
0.26
(1.44)
(1.64)
-0.39
-0.05
(-0.96)
(-0.13)
0.01
-0.01
(0.06)
(-0.06)
-0.56
-0.46
(-1.41)
(-1.19)
0.38
0.38
Negative/Positive
(7)
(8)
0.11
0.13
(1.13)
(1.49)
-0.02
(-0.15)
-0.25
(-1.80)*
-14.53
-22.50
(-1.00)
(-1.63)
-62.27
(-2.44)**
-36.92
(-1.64)*
0.55
0.52
(3.81)*** (3.47)***
0.07
0.12
(0.42)
(0.75)
0.24
0.27
(1.49)
(1.67)*
-0.39
-0.11
(-0.96)
(-0.29)
0.00
-0.02
(0.01)
(-0.11)
-0.58
-0.49
(-1.48)
(-1.25)
0.38
0.38
14.94***
18.01***
13.93***
14.40***
(3)
0.08
(0.97)
0.09
(0.53)
-15.27
(-1.06)
-71.12
(-2.62)**
(4)
0.11
(1.34)
-0.17
(-1.15)
-14.98
(-1.15)
50
11.48***
12.00***
This table reports the contemporaneous relation between macroeconomic shocks and measures of uncertainty for 105 quarters from Q4:1985–
Q4:2011 using alternative cutoffs to measure aggregate uncertainty. Rev is an indicator variable equal to one for the lowest quintile of aggregate
earnings shocks for the “Quintile” partition, the lowest tercile for the “Tercile” partition, below the median for the “Median” partition, and below
zero for the “Negative/Positive” partition.
UNC is an indicator variable equal to one for the highest quintile of economic policy uncertainty shocks for the “Quintile” partition, the highest
tercile for the “Tercile” partition, above the median for the “Median” partition, and above zero for the “Negative/Positive” partition.
All the remaining variables are defined in Table 1. Panel A reports the contemporaneous relation between investment shocks and uncertainty. Panel
B presents the contemporaneous relation between unemployment rate shocks and uncertainty. Panel C presents the contemporaneous relation
between industrial production shocks and uncertainty. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
51
Table 7: VIX, aggregate idiosyncratic volatility, and contemporaneous macroeconomic
shocks
Panel A: Contemporaneous investment shocks
Dependent Variable : D_INVt
Intercept
VIXt
(1)
(2)
(3)
(4)
(5)
0.60*
(1.68)
-0.01
(-0.02)
0.52
(1.52)
0.58*
(1.78)
0.60*
(1.78)
-2.05***
(-3.01)
-1.89***
(-2.89)
-1.89***
(-2.73)
-33.53
(-0.39)
-15.36
(-0.16)
-2.36***
(-3.36)
Idio_Volt
-232.83*** -202.49***
(-3.40)
(-3.06)
VIXt (x) Idio_Volt
-361.96*** -400.29***
(-2.84)
(-2.81)
D_Const
-0.71
(-1.00)
D_Termt
-0.61
(-0.84)
D_Yieldt
-0.63
(-0.32)
D_CPIt
-0.18
(-0.25)
D_Deft
-1.24
(-0.72)
Adj. R2
0.11
0.11
F-test: Idio_Volt+ VIXt (x) Idio_Volt =0
52
0.19
0.25
0.22
18.05***
17.29***
Table 7: Continued
Panel B: Contemporaneous unemployment shocks
Dependent Variable : D_UNEMPt
(1)
(2)
(3)
(4)
(5)
Intercept
-0.04
(-1.62)
0.00
(0.17)
-0.04
(-1.48)
-0.04*
(-1.76)
-0.03
(-1.30)
VIXt
0.17***
(3.50)
0.16***
(3.20)
0.15***
(3.09)
0.11**
(2.22)
10.28**
(2.14)
-2.33
(-0.37)
-4.69
(-0.69)
27.00***
(2.93)
27.76***
(2.73)
Idio_Volt
12.62**
(2.52)
VIXt (x) Idio_Volt
D_GDPt
-0.06
(-1.40)
D_Const
-0.02
(-0.43)
D_Termt
-0.03
(-0.64)
D_Yieldt
-0.06
(-0.42)
D_CPIt
0.06
(1.20)
D_Deft
0.06
(0.49)
Adj. R2
0.12
0.06
0.15
F-test: Idio_Volt+ VIXt (x) Idio_Volt =0
53
0.22
0.22
13.42***
9.91***
Table 7: Continued
Panel C: Contemporaneous industrial production shocks
Dependent Variable : D_IPRODt
Intercept
VIXt
Idio_Volt
(1)
(2)
(3)
(4)
(5)
0.12
(1.09)
-0.01
(-0.12)
0.10
(0.91)
0.11
(1.08)
0.02
(0.26)
-0.42**
(-2.00)
-0.38*
(-1.85)
-0.03
(-0.16)
-55.37***
(-2.71)
-15.49
(-0.57)
-3.51
(-0.13)
-85.43**
(-2.13)
-75.87*
(-1.91)
-0.50**
(-2.35)
-61.56***
(-3.00)
VIXt (x) Idio_Volt
D_GDPt
0.49***
(2.73)
D_Const
0.28
(1.28)
D_Termt
0.21
(1.05)
D_Yieldt
-0.22
(-0.41)
D_CPIt
-0.04
(-0.21)
D_Deft
-0.65
(-1.36)
Adj. R2
0.05
0.09
F-test: Idio_Volt+ VIXt (x) Idio_Volt =0
0.12
0.15
11.91***
0.32
7.75***
This table reports the contemporaneous relation between macroeconomic shocks and measures of
uncertainty for 86 quarters from Q3:1990–Q4:2011 using alternative measures of aggregate and firm-level
uncertainty. VIX is an indicator variable equal to one for the highest quartile of VIX shocks. VIX shocks
equal the residuals from an AR (2) model of the VIX index, which is the Chicago Board Options Exchange
(CBOE) market volatility index.
54
Idio_Vol is defined as the shocks to aggregate idiosyncratic volatility. Idio_Vol is estimated as follows:
First, for each firm and quarter daily idiosyncratic returns are estimated as the residuals from the regression
of daily stock returns on daily market, size, book-to-market, and momentum factors (Fama and French
(1993); Carhart, 1997). Second, for each firm-quarter the standard deviations of daily idiosyncratic returns
(from step 1) are computed and defined as firm-level idiosyncratic volatility. Third, using all firms in a
quarter, the average firm-level idiosyncratic volatility (from step 2) is computed and defined as aggregate
idiosyncratic volatility. Finally, shocks to aggregate idiosyncratic volatility are defined as the residuals from
an AR (2) model of aggregate idiosyncratic volatility (from step 3).
All the remaining variables are defined in Table 1. Panel A reports the contemporaneous relation between
investment shocks and uncertainty. Panel B presents the contemporaneous relation between unemployment
rate shocks and uncertainty. Panel C presents the contemporaneous relation between industrial production
shocks and uncertainty. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively.
55
Table 8: Firm-level and aggregate-level uncertainty, and market returns
Panel A: Aggregate earnings and market returns
Dependent Variable : Market Rett
(1)
(2)
(3)
(4)
(5)
Intercept
0.02
(1.98)*
0.01
(1.39)
0.02
(1.76)*
0.02
(2.03)**
0.02
(1.71)*
Eart+1
-0.04
(-1.69)*
-0.02
(-1.11)
-0.02
(-0.94)
0.00
(0.23)
-1.46
(-3.52)***
-0.40
(-0.67)
-0.68
(-1.13)
-1.99
(-2.46)**
-2.04
(-2.57)**
Ear_Dispt+1
-1.54
(-3.78)***
Eart+1 (x) Ear_Dispt+1
D_GDPt+1
0.00
(0.28)
D_Const+1
0.01
(0.46)
D_Termt+1
0.02
(0.94)
D_Yieldt+1
0.01
(0.18)
D_CPIt+1
-0.01
(-0.62)
D_Deft+1
-0.15
(-2.94)***
Adj. R2
0.02
0.11
F-test: Ear_Dispt+1+ Eart+1 (x) Ear_Dispt+1=0
0.16
18.61***
56
0.12
0.21
21.63***
Table 8: Continued
Panel B: Aggregate earnings revisions and market returns
Dependent Variable : Market Rett
(1)
(2)
(3)
(4)
(5)
Intercept
0.03
(2.80)***
0.01
(1.47)
0.01
(1.55)
0.02
(1.84)*
0.02
(1.79)*
Revt+1
-0.07
(-3.33)***
-0.01
(-0.56)
0.01
(0.32)
0.01
(0.37)
-7.61
(-5.76)***
-6.16
(-4.02)***
-6.10
(-3.97)***
-5.27
(-1.80)*
-5.44
(-1.73)*
Rev_Dispt+1
-7.98
(-6.90)***
Revt+1 (x) Rev_Dispt+1
D_GDPt+1
0.00
(0.18)
D_Const+1
0.02
(0.88)
D_Termt+1
0.03
(1.68)*
D_Yieldt+1
0.02
(0.44)
D_CPIt+1
-0.01
(-0.61)
D_Deft+1
-0.10
(-2.18)**
Adj. R2
0.10
0.31
F-test: Rev_Dispt+1+ Revt+1 (x) Rev_Dispt+1 =0
57
0.31
0.32
21.02***
0.35
18.63***
Table 8: Continued
Panel C: Economic policy uncertainty and market returns
Dependent Variable : Market Rett
(1)
(2)
(3)
(4)
(5)
Intercept
0.03
(2.87)***
0.01
(1.47)
0.02
(2.63)***
0.02
(2.86)***
0.02
(2.71)***
Unct+1
-0.07
(-3.35)***
-0.04
(-2.57)**
-0.04
(-2.20)**
-0.03
(-1.79)*
-7.37
(-6.43)***
-5.55
(-4.05)***
-5.53
(-4.08)***
-5.53
(-2.31)**
-6.20
(-2.47)**
Rev_Dispt+1
-7.98
(-6.90)***
Unct+1 (x) Rev_Dispt+1
D_GDPt+1
-0.01
(-0.63)
D_Const+1
0.02
(1.05)
D_Termt+1
0.03
(1.90)*
D_Yieldt+1
0.05
(1.11)
D_CPIt+1
-0.02
(-0.96)
D_Deft+1
-0.08
(-2.00)**
Adj. R2
0.09
0.31
F-test: Rev_Dispt+1+ Unct+1 (x) Rev_Dispt+1=0
0.35
0.38
32.02***
0.40
31.47***
This table reports the relation between market returns and measures of uncertainty for 105 quarters from
Q4:1985–Q4:2011. Aggregate realized earnings (EARt+1) and realized earnings dispersion (Ear_Dispt+1)
are computed using realized seasonal random walk earnings, following the methodology used to compute
Rev and Rev_Dsip. All the remaining variables are defined in Table 1. Panel A reports the relation between
market returns and an interaction term based on aggregate realized earnings. Panel B presents the relation
between market returns and an interaction term based on aggregate earnings revisions. Panel C reports the
relation between market returns and interaction term based on the economic policy uncertainty index. ***,
**, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
58
Table 9: Firm-level and aggregate-level
macroeconomist forecast errors
uncertainty,
and
one-quarter-ahead
Panel A: Measures of aggregate earnings and unemployment forecast errors
UNEMP_AFEt+1
(1)
(2)
0.09
(9.59)***
0.10
(9.51)***
-0.04
(-2.74)***
-0.04
(-2.46)**
0.01
(0.60)
0.01
(0.32)
-0.02
(-0.50)
-0.02
(-0.60)
Rev_Dispt
-2.93
(-1.75)*
-3.17
(-1.83)*
0.72
(0.28)
0.79
(0.30)
Revt (x) Rev_Dispt
6.41
(2.01)**
6.09
(1.72)*
9.82
(1.97)*
7.29
(1.36)
Intercept
Revt
(3)
(4)
D_GDPt
-0.01
(-0.29)
-0.04
(-1.29)
D_Const
-0.01
(-0.62)
-0.04
(-1.35)
D_Termt
0.02
(1.23)
0.00
(0.01)
D_Yieldt
-0.03
(-0.63)
-0.07
(-0.87)
D_CPIt
0.00
(0.20)
0.02
(0.79)
D_Deft
0.03
(0.65)
0.10
(1.31)
Adj. R2
0.04
F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0
1.64
0.02
0.05
0.10
0.94
6.16**
3.17*
59
UNEMP_FEt+1
Table 9: Continued
Panel B: Measures of aggregate earnings and industrial production forecast errors
IPROD_AFEt+1
(1)
Intercept
(2)
IPROD_FEt+1
(3)
(4)
1.73
(9.60)***
1.82
(0.52)
0.18
(0.60)
0.08
(0.33)
Revt
0.23
(0.51)
0.12
(0.27)
-0.19
-(0.26)
-0.15
(-0.23)
Rev_Dispt
2.76
(0.09)
-5.07
(-0.17)
44.89
(0.89)
55.57
(1.28)
154.02
(2.61)**
126.32
(2.09)**
-254.37
(-2.64)***
-194.54
(-2.19)**
Revt (x) Rev_Dispt
D_GDPt
-0.61
(-1.93)*
1.15
(2.48)**
D_Const
-0.52
(-1.51)
1.44
(2.83)***
D_Termt
0.32
(0.94)
0.17
(0.34)
D_Yieldt
-0.06
(-0.07)
-0.12
(-0.10)
D_CPIt
-0.15
(-0.44)
-0.31
(-0.63)
D_Deft
1.34
(1.57)
-3.21
(-2.56)**
Adj. R2
0.14
F-test: Rev_Dispt+ Revt (x) Rev_Dispt =0
9.70***
0.26
0.08
0.36
5.58**
6.50**
3.38*
This table reports the relation between one-quarter-ahead macroeconomist forecast errors (signed, and
absolute) and uncertainty for 104 quarters from Q4:1985–Q3:2011. All the variables are defined in Table
1. Panel A reports the relation between one-quarter-ahead unemployment forecast errors and uncertainty.
Panel B presents the relation between one-quarter-ahead industrial production forecast errors and
uncertainty. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
60