Characterizing very high uncertainty episodes. N°1637, February 2014.
Date: 2014-10-03
Institutions: European Central Bank
Subjects: econometrics, economic conditions, economic cycle, microeconomics

Wo r k i n g   Pa P e r   S e r i e S
n o  16 37   /   F e b r u a r y   2014

CharaCterizing very high 
unCertainty ePiSodeS

Martin Bijsterbosch and Pierre Guérin

In 2014 all ECB 
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note: This Working Paper should not be reported as representing 
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Acknowledgements
We would like to thank Stéphane Dées, Tatevik Sekhposyan, seminar participants at the Bank of Canada and an anonymous referee 
for useful comments on a previous draft. The opinions expressed herein are those of the authors. No responsibility for them should be 
attributed to the European Central Bank or the Bank of Canada.

Martin Bijsterbosch
European Central Bank; e-mail: martin.bijsterbosch@ecb.europa.eu

Pierre Guérin
Bank of Canada; e-mail: pguerin@bank-banque-canada.ca

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Abstract

This paper uses a two-step approach to characterize the evolution of US macroe-
conomic and ﬁnancial variables during episodes of very high uncertainty. First, we
identify episodes of very high uncertainty using a regime-switching model. Second,
we assess the behavior of macroeconomic and ﬁnancial variables during these episodes
of very high uncertainty. This methodology is analogous to the approach followed
by Baele et al. (2013), who study episodes of ﬂights to safety in ﬁnancial markets.
We ﬁnd that very high uncertainty episodes are associated with a weaker growth
performance and sharp declines in stock prices. However, we ﬁnd that this relation is
non-linear in that uncertainty does not seem to matter during periods characterized
by medium or low uncertainty.

Keywords: Uncertainty, Markov-switching, Survey data.

JEL Classiﬁcation Code: C24, D80, E32, E66.

1

Non-technical summary
The role of uncertainty in the low growth performance of many advanced economies
in recent years has received increasing attention among researchers and policy-makers.
Macroeconomic theory suggests that uncertainty can have a powerful impact on macroe-
conomic activity, for example because it may give ﬁrms an incentive to delay investment
and employment (Bernanke (1983)). Similarly, consumers may postpone their acquisition
of durable goods if uncertainty increases (Romer (1990)). Moreover, uncertainty may push
up the cost of ﬁnance via an increase in risk premia (e.g., Gilchrist et al. (2010)) or have
an impact on stock prices as it increases discount rates and hence decreases the net present
value of future proﬁtability (P´astor and Veronesi (2012), Bansal and Yaron (2004) and
Bekaert et al. (2009)).

In practice, however, measuring uncertainty and capturing its actual impact on macroe-
conomic variables have proved challenging. An increasing number of empirical studies have
addressed this issue in recent years, mainly focusing on the US. Following Bloom (2009),
several recent empirical investigations have found a signiﬁcant counter-cyclical link between
uncertainty and macroeconomic activity (see, for example, Baker et al. (2012), Leduc and
Liu (2013), IMF (2012) and Denis and Kannan (2013)), although others, such as Knotek
and Khan (2011), ﬁnd only a modest link using data on US households. Changes in uncer-
tainty may have diﬀerent macroeconomic impacts, depending, for example, on the state of
the business cycle or the level of uncertainty. Indeed, our main ﬁndings indicate that the
role of uncertainty becomes more important when the level of uncertainty in the economy is
higher, that is, we ﬁnd a non-linear impact of uncertainty on macroeconomic and ﬁnancial
variables.

This paper uses a two-step approach to characterize the evolution of macroeconomic and
ﬁnancial variables during episodes of high uncertainty in the US. First, we identify episodes
of very high uncertainty using a regime-switching model. Second, we study the behavior of
macroeconomic and ﬁnancial variables during these episodes of very high uncertainty. This
approach, which stems from the ﬁnance literature, allows us to quantify diﬀerent macroeco-
nomic eﬀects of uncertainty in three diﬀerent types of uncertainty episodes - high, medium
and low uncertainty periods. In addition, as uncertainty is not directly measurable, we
identify these diﬀerent uncertainty regimes using three alternative measures: the economic
policy uncertainty index (Baker et al. (2012)), the implied volatility index on the S&P 500
(VIX index) and a combination of both these measures.

Our key ﬁndings are that very high uncertainty episodes are associated with a consistent
and signiﬁcant weakening of economic activity, a decline in inﬂation, an increase in unem-
ployment and a decline in bond yields and stock prices. Also after controlling for the impact
of business cycle conditions, ﬁnancial and other factors, macroeconomic and ﬁnancial con-
ditions weaken whenever the economy is in a very high uncertainty episode. Uncertainty
matters especially during the relatively rare episodes of very high uncertainty, but does not
seem to play a signiﬁcant role during normal times. High uncertainty seems to have an
impact in particular on survey data and regressions based on the Federal Reserve Bank of
Philadelphia’s Survey of Professional Forecasters show that during very high uncertainty
episodes economic agents revise down their expectations of future output. Overall, our
results suggest that the exceptionally high levels of (policy) uncertainty may have played
an important role in the low growth performance of the US economy in recent years.

2

1

Introduction

The role of uncertainty in the low growth performance of many advanced economies
in recent years has received increasing attention among researchers and policy-makers.
Macroeconomic theory suggests that uncertainty can have a powerful impact on macroe-
conomic activity, for example because it may give ﬁrms an incentive to delay investment
and employment (Bernanke (1983)). Similarly, consumers may postpone their acquisition
of durable goods if uncertainty increases (Romer (1990)). Moreover, uncertainty may push
up the cost of ﬁnance via an increase in risk premia (e.g. Gilchrist et al. (2010)) or have an
impact on stock prices as it increases discount rates and hence decreases the net present
value of future proﬁtability (P´astor and Veronesi (2012), Bansal and Yaron (2004) and
Bekaert et al. (2009)).

In practice, however, measuring uncertainty and capturing its actual impact on macroe-
conomic variables have proved challenging. An increasing number of empirical studies have
addressed this issue in recent years, mainly focusing on the US. Following Bloom (2009),
several recent empirical investigations have found a signiﬁcant countercyclical link between
uncertainty and macroeconomic activity (see, for example, Baker et al. (2012), Leduc and
Liu (2013), IMF (2012) and Denis and Kannan (2013)), although others, such as Knotek
and Khan (2011), ﬁnd only a modest relationship using data on US households. While
most of the empirical literature uses linear VAR models, there is evidence that uncertainty
shocks exert diﬀerent eﬀects over diﬀerent phases of the business cycle. Caggiano et al.
(2012), for example, ﬁnd that uncertainty shocks have a larger macroeconomic impact
during recessions than during economic upturns. It may thus be appropriate to allow for
non-linearities in empirical analyses of uncertainty.

This paper uses a two-step approach to characterize the evolution of macroeconomic
and ﬁnancial variables during episodes of high uncertainty in the US. First, we aim at
identifying episodes of very high uncertainty using a regime-switching model. Second, we
study the behavior of macroeconomic and ﬁnancial variables during these episodes of very
high uncertainty.

This methodology is analogous to the approach followed by Baele et al. (2013) to identify
and characterize episodes of ﬂights to safety in ﬁnancial markets. An advantage of this
approach is that it allows us to account for non-linearities in the relationship between
uncertainty and macroeconomic variables. Instead of the uncertainty variable itself, our
uncertainty measures are based on the number of days in each month that the economy
is in the very high uncertainty regime, i.e. the highest uncertainty regime of the three
In other words, our measures of uncertainty
uncertainty regimes that we distinguish.
increase only when the economy enters a period of very high uncertainty. Finally, as
uncertainty is not directly measurable, we identify very high uncertainty regimes using
three alternative measures: the economic policy uncertainty index (Baker et al. (2012)),
the implied volatility index on the S&P 500 (VIX index) and a combination of both these
measures.

3

2 Why may uncertainty matter?

Economic theory suggests that uncertainty can aﬀect macroeconomic outcomes via sev-
eral channels. The literature has identiﬁed at least three channels through which uncer-
tainty shocks can have an impact on economic activity. First, uncertainty shocks may
aﬀect the behavior of ﬁrms (Bernanke (1983) and Pindyck (1991)). Uncertainty can have
an impact on economic activity as it may give ﬁrms an incentive to delay investment and
employment decisions. A key concept in this framework is irreversibility.
If investment
decisions are irreversible, ﬁrms must make investment decisions that trade of the extra
returns from early commitment against the beneﬁt of having more information by waiting.
Bernanke’s real options framework captures the notion that when uncertainty is high, the
option value of waiting increases as it may be beneﬁcial for ﬁrms to wait and acquire more
information before deciding to invest in a real asset.

Second, higher uncertainty may induce households to save more. Higher uncertainty
about future income may induce consumers to postpone spending, in particular on durable
goods. Romer (1990) shows that this was a key driver of the decline in demand during the
Great Depression.

Third, higher uncertainty may push up the cost of ﬁnance via an increase in risk premia
(e.g., Gilchrist et al. (2010)) or have an impact on stock prices as it increases discount
rates and thus decreases the net present value of future proﬁtability (P´astor and Veronesi
(2012)). Increased uncertainty is thus likely to have a downward impact on asset prices.
In addition, higher uncertainty may have an impact on the banking sector’s willingness
to provide loans and therefore lead to a tightening in credit conditions and depress credit
growth.

In addition to these channels, there is recent evidence on how uncertainty plays a
role in the transmission of systemic stress to the economy. For example, uncertainty can
amplify the impact of ﬁnancial instability on macroeconomic variables. Hartmann et al.
(2012) investigate the links between ﬁnancial stress and macroeconomic variables using a
regime-switching VAR model estimated with bayesian methods. They ﬁnd that ﬁnancial
shocks have a substantial impact on real variables in high ﬁnancial stress episodes but a
limited impact in normal times. This underlines the fact that an economy may function
fundamentally diﬀerently in times of systemic ﬁnancial instability compared with normal
times.

Although economic theory identiﬁes clear channels through which high uncertainty may
undermine economic performance, the results of empirical work have not been entirely
conclusive. An increasing number of empirical studies have addressed this issue in recent
years, mainly focusing on the US economy. Following Bloom (2009), several recent empirical
investigations have found a strong and signiﬁcant counter-cyclical link between uncertainty
and macroeconomic activity (see, for example, Baker et al. (2012), Leduc and Liu (2013),
IMF (2012) and Denis and Kannan (2013)), although others, such as Knotek and Khan
(2011), ﬁnd only a modest link using data on US households. One reason for these somewhat
mixed results may be that uncertainty is diﬃcult to measure (see Section 3). In addition,
uncertainty is endogenous. Hence, it is diﬃcult to disentangle the impact of other shocks,

4

such as a demand shock, from the eﬀects of idiosyncratic uncertainty shocks on the proﬁle
of economic activity. In addition, there is evidence that macroeconomic policies may have
an impact on uncertainty. Bekaert et al. (2013), for example, ﬁnd that loose monetary
policy can decrease risk aversion and uncertainty.

While most of the empirical literature uses linear VAR models, there is evidence that un-
certainty shocks exert diﬀerent eﬀects over diﬀerent phases of the business cycle. Caggiano
et al. (2012), for example, ﬁnd that uncertainty shocks have a larger macroeconomic impact
during recessions than during economic upturns. Non-linearities or asymmetries can occur
in various ways. For example, the impact of uncertainty on economic activity may restrain
households’ decisions to spend more strongly when unemployment is higher. Similarly,
increases in uncertainty may have a larger impact on output if the monetary authority is
constrained by the zero lower bound in nominal interest rates (Basu and Bundick (2012)).
Moreover, the impact of uncertainty shocks on economic activity may be larger during
periods when uncertainty is exceptionally high as opposed to periods during which un-
certainty is lower. The latter non-linearity is the key hypothesis of this paper. As some
underlying level of uncertainty always exists in an economy, this may not necessarily have
an important impact on economic growth. That impact may change, however, if uncer-
tainty increases to unusually high levels. This hypothesis implies that we need to use an
approach that allows us to study the behavior of macroeconomic and ﬁnancial variables
during diﬀerent regimes. As there are analogies with the behavior of ﬁnancial markets
during periods of high stress, we follow the approach by Baele et al. (2013) who identify
and characterize episodes of ﬂights to safety in ﬁnancial markets. Flights to safety, deﬁned
for ﬁnancial markets as a sudden increase in appetite for safe assets relative to risky assets,
may also be relevant in macroeconomics. During high stress episodes, households and com-
panies may change their economic behavior relative to ”normal” times, with implications
for macroeconomic relationships. When using the VIX as a dependent variable, the very
high uncertainty episodes we identify can be interpreted as ﬂights to safety episodes and
therefore our approach permits to study the behavior of macroeconomic variables during
such episodes.

The policy implications of better understanding the link between uncertainty and growth
may be substantial. Studying this link may help to better understand the reasons for the
disappointing growth performance of many advanced economies in recent years.
In ad-
dition, the impact of macroeconomic stimulus during periods of high uncertainty may be
smaller than under normal circumstances because ﬁrms behave in a more cautious manner
(Bloom (2009) and Bloom et al. (2012)). Thus, if a recession is characterized by a high
degree of uncertainty, the response may have to be diﬀerent from those in other recessions.

3 Measuring uncertainty and data used

A key challenge in the literature on uncertainty relates to its measurement. As un-
certainty cannot be directly observed, it is usually measured on the basis of several proxy
indicators. In most previous studies, three types of uncertainty measures have been used: ﬁ-
nancial market indicators, survey-based measures (including forecast dispersion measures)

5

and media measures based on the number of citations of a speciﬁc term (for a recent
overview, see Haddow et al. (2013)). Other measures are more microeconomic in nature
and are based on various indicators of dispersion at individual company or industry level.
As all of these measures have their disadvantages, studies of uncertainty are usually based
on a range of indicators.

This paper uses three alternative measures of uncertainty that are available at a daily
frequency: the economic policy uncertainty (EPU) index developed by (Baker et al. (2012),
the implied volatility index on the S&P 500 (VIX index) and a combination of both these
measures. Both measures capture potentially diﬀerent concepts. Whereas the EPU index
aims at capturing uncertainty associated with economic policies, the VIX index reﬂects
market uncertainty associated with future stock price movements and might proxy risk
aversion. We use a policy-related uncertainty index as many commentators have argued
that the weakness in growth in the US during recent years may have been related to uncer-
tainty about the policy response and political disagreements. We also use a ﬁnancial market
indicator because the implied volatility indices such as the VIX have been among the most
widely used uncertainty measures in the literature. By using these two diﬀerent measures
we hope to capture various types of macroeconomic uncertainty. Finally, we also employ
a composite indicator as both indicators taken together may provide a useful guide to the
general degree of uncertainty in the economy. Other measures of uncertainty, such as those
based on forecasters’ disagreement, have also been widely used in the literature, but we do
not use those here as they are not available at a daily frequency. None of the indicators we
use should be seen as a perfect proxy for uncertainty. Taken together, however, they may
provide a useful indication of the general degree of uncertainty faced by households and
companies. In addition, as the two constituting indicators of uncertainty capture diﬀerent
concepts, using them separately may shed light on the sources of uncertainty.

The daily economic policy uncertainty (EPU) index for the US is a news-based index
drawing on newspapers published in the US.1 These newspapers range from large national
papers to small local newspapers across the country. The available data series starts in
January 1985. The primary measure for this index is the number of articles that contain at
least one term out of the following three categories. The ﬁrst is ”economic” or ”economy”.
The second category is ”uncertain” or ”uncertainty”. The third set is ”legislation” or
”deﬁcit” or ”regulation” or ”congress” or ”federal reserve” or ”white house”. As the number
of newspapers in the sample has increased over time (from 18 in 1985 to over 1800 by 2008),
the index has been normalized by taking daily counts of the total number of relevant
newspaper articles and constructing a one-year moving average of this series.

Our second measure of uncertainty, the Chicago Board Options Exchange Market
Volatility or VIX index, expresses the implied volatility of the S&P 500 index anticipated
on the derivatives market. More speciﬁcally, the VIX indicates in percentage points the
change to be expected in the next 30 days for the S&P 500 stock market index. The basis
for the calculation of this index is provided by S&P 500 option contracts. The VIX is
thus a measure of perceived stock market uncertainty by the market in either direction. In

1More

information on the

construction of

the

economic policy

index

is

available

at

http://www.policyuncertainty.com/.

6

addition to using the overall VIX index, it is also possible to decompose this index into a
so-called risk aversion and uncertainty component (Bekaert et al. (2013)). We refrain from
this option here as we would need to construct a volatility forecasting model for this. The
daily VIX index used in this paper is available from January 1990 onwards.

As regards the other data used in the paper, we consider the following macroeconomic
variables for the US: industrial production, retail sales, the unemployment rate, CPI in-
ﬂation, the manufacturing index compiled by the Institute of Supply Management (ISM)
and consumer sentiment (i.e., the University of Michigan consumer sentiment index). The
ISM manufacturing index is a composite index that monitors employment, production in-
ventories, new orders and supplier deliveries in the manufacturing sector. We also consider
three ﬁnancial variables: stock prices (S&P 500 index), the nominal trade-weighted USD
exchange rate and the yield on the 10-year US Treasury bond. All these data are sampled
at the monthly frequency.

4

Identifying episodes of very high uncertainty

We consider a univariate regime switching model for the uncertainty variable yt deﬁned

as follows:

yt = µ(St) + (cid:15)t(St)

(1)

where (cid:15)t|St ∼ iidN (0, σ(St)). The regime generating process is an ergodic Markov chain
with a ﬁnite number of states St = {1, ..., M } deﬁned by the following constant transition
probabilities:

pij = P r(St+1 = j|St = i)

M
(cid:88)

j=1

pij = 1

∀i, j(cid:15){1, ..., M }

This mean-variance regime switching model is often estimated for equity returns (see e.g.
Perez-Quiros and Timmermann (2001)). Baele et al. (2013) estimate equation (1) with a
three-state regime-switching model using as a dependent variable the diﬀerence between
equity return and the return on a benchmark government bond to identify ﬂight-to-safety
episodes. We follow Baele et al. (2013) and estimate equation (1) with three regimes using
as a dependent variable each of our uncertainty variables to identify extreme uncertainty
episodes.2 The ﬁrst regime is an episode with a low level of uncertainty and volatility, while
the second regime is an episode of a high level of uncertainty and volatility. The third regime
captures the rare episodes of a very elevated level and volatility of uncertainty.3

We also consider a bivariate model, which we estimate as follows:

xt = Φc(St) + ut(St),

ut|St ∼ iidN (0, Σ(St))

2The model is estimated by quasi maximum likelihood via the Expectation Maximization (EM) algo-

rithm (see, e.g., Hamilton (1990))

3We consider a model with a switch in both the mean and the volatility of uncertainty since this
speciﬁcation is considerably preferred by standard information criteria compared with a model that only
considers a switch in the intercept.

7

where the vector xt contains the daily EPU and daily VIX, i.e. xt can be rewritten as
xt = [EP Ut, V IXt](cid:48).

Very high uncertainty episodes are infrequent: using the bivariate model (based on both
the EPU and the VIX index), the US economy was in a period of very high uncertainty
during around 20 per cent of the sample (Table 1). In more detail, Table 1 shows that,
as expected, the third regime exhibits the highest mean and variance for the EPU index,
the VIX and the bivariate model. This regime can thus be interpreted as the very high
uncertainty episode. When using the EPU index, the unconditional probability of being in
the third regime is 13.5 per cent. Using the VIX as a measure of uncertainty yields similar
results, albeit the unconditional probability of being the third regime is 23.9 per cent and
the third regime is more persistent as the transition probability of staying in this regime is
0.979 (compared with 0.693 for the model using the EPU index).

Very high uncertainty episodes have been more frequent during the most recent decade
of the sample and especially since 2008. Clearly visible are the peaks following Septem-
ber 2001, the start of the second Gulf war and, more recently, the increases associated
with various phases of the global ﬁnancial crisis and the debt ceiling debate (Figure 1).
Noteworthy is the divergence between the EPU index and the VIX towards the end of
the sample, suggesting that the high level of uncertainty in recent years may have been
associated with uncertainty about economic policies rather than ﬁnancial market volatility.
Figure 2, plotting the daily probability of being in the third regime during the past ﬁve
years, conﬁrms the recent divergence between both uncertainty measures.

5 Very high uncertainty episodes and the economic

and ﬁnancial environment

As a next step, we study the contemporaneous comovements between the high un-
certainty episodes and a number of macroeconomic and ﬁnancial variables. We consider
the following macroeconomic variables: industrial production (monthly change in the log
index), retail sales (monthly change in the log index), the unemployment rate (level), in-
ﬂation (monthly change in the log CPI), ISM manufacturing (monthly change in the log
index) and consumer sentiment (monthly change in the log index). We also consider three
ﬁnancial variables: stock prices (monthly change in the log index), exchange rate (monthly
change in the log of the nominal trade-weighted USD exchange rate) and the yield on the
10-year US Treasury bond (in level). We regress each of the monthly macroeconomic or
ﬁnancial variables on the number of days per month when the daily probability of being
in the third regime is higher than 0.5 and a set of control variables. We therefore estimate
the following regression:

yt = α + βpt + ΓXt + ut

(2)

Our set of control variables Xt consists of the Aruoba et al. (2009) daily index of
business cycle conditions (the ADS index), stock returns (the S&P500 index) as well as
the lagged dependent variable yt−1. The ADS index captures the impact of business cycle

8

conditions, the S&P500 index is a proxy for the impact of ﬁnancial factors on our variables
of interest and the lagged dependent variable captures any omitted other factors that may
be relevant. The estimation sample runs from January 1986 to December 2012 when using
the EPU index and from January 1991 to December 2012 when using the VIX index and
the bivariate model.

A potential caveat of this regression is that it may be aﬀected by endogeneity. Whereas
economic theory identiﬁes clear channels through which uncertainty may aﬀect macroeco-
nomic outcomes, in empirical studies the causality of this relationship has been diﬃcult to
establish. Although our aim is to study comovements rather than causality, controlling in
our regressions for business cycle and ﬁnancial conditions may help to isolate the impact of
uncertainty from other inﬂuences such as business cycle or ﬁnancial conditions. In addition,
the timing of the macroeconomic data releases alleviates our concerns about endogeneity to
some extent. Indeed, the macroeconomic variables are typically released with a substantial
lag, while the uncertainty index we use in regression (1) is available on a timely basis at a
daily frequency so that feedback eﬀects are mitigated in equation (2).

Table 2 shows that during very high uncertainty episodes, macroeconomic variables
tend to weaken strongly and signiﬁcantly. More speciﬁcally, industrial production, retail
sales, inﬂation as well as business and consumer sentiment are all negatively correlated
with uncertainty. All coeﬃcients have the expected signs and are highly signiﬁcant (except
for retail sales) if the economic policy uncertainty index is used. For the VIX index and
the bivariate model, the results are broadly similar, although the impact on retail sales is
signiﬁcant in contrast to the coeﬃcients for the conﬁdence indicators. Looking at ﬁnancial
variables, higher uncertainty episodes are associated with large declines in stock prices and
bond yields.

Also after controlling for other factors, our results suggest that high uncertainty has a
clear impact on our variables of interest, although the coeﬃcient for uncertainty becomes
smaller and less signiﬁcant. In these cases (columns (d) to (f) of Table 2), uncertainty has
a signiﬁcant impact on business and consumer conﬁdence, inﬂation and on stock prices,
In addition, for stock prices, the impact of
but not on the other variables of interest.
uncertainty is signiﬁcant and does not depend on the measure of uncertainty used.

To investigate whether the behavior of these macroeconomic and ﬁnancial variables is
diﬀerent during periods with lower uncertainty, we regress each of these variables on the
number of days per month when the daily probability of being in the second regime (i.e.,
the medium uncertainty regime) is higher than 0.5. Subsequently, we repeat this exercise
for the ﬁrst (i.e., low uncertainty) regime. The results suggest that there is no signiﬁcant
link anymore for any of the macroeconomic or ﬁnancial variables (Tables 3 and 4). For the
EPU index, there is only some evidence of a signiﬁcant link with industrial production and
unemployment during periods of medium-level uncertainty, but that relationship vanishes
(and has the wrong sign) once we add our control variables.

As very high uncertainty seems to have an impact in particular on soft data, we inves-
tigate this further by focusing on the link between uncertainty and survey data. We use
the Survey of Professional Forecasters (SPF) conducted by the Federal Reserve Bank of

9

Philadelphia, which is a quarterly survey of macroeconomic forecasts in the US. The vari-
ables we consider are the quarterly forecasts for real GDP, consumption, (non-residential)
investment, industrial production (all in annualized quarterly growth rates), inﬂation (an-
nual change in the CPI index) as well as the level of the 10-year Treasury bond yield. As
control variables we use the ADS index and the S&P500 index. The estimation sample
runs from 1991:Q1 to 2012:Q4.

Our results show that very high uncertainty has a signiﬁcant impact on macroeconomic
expectations as measured by the SPF, regardless of the measure of uncertainty (Tables
5 and 6). During very high uncertainty episodes, expectations for output, consumption,
investment, inﬂation and bond yields all drop, whereas they increase for unemployment
(although the impact on unemployment is not signiﬁcant if the VIX index is used). For
almost all variables, the impact of uncertainty remains signiﬁcant also after controlling for
business cycle conditions and ﬁnancial factors, although the size of the coeﬃcients drops
as can be expected. Uncertainty mainly aﬀects expectations at the 2-3 quarter horizon,
although in some cases expectations for quarters further ahead are also aﬀected. Although
the size of the impact cannot be directly compared with those in other studies based on
diﬀerent methodologies, they seem to be in line with other recent empirical studies for the
US (e.g. Baker et al. (2012)).

6 Conclusions

In this paper, we have identiﬁed episodes of very high uncertainty for the US using
a regime-switching model. Focusing on these episodes of very high uncertainty, we have
studied the behavior of macroeconomic and ﬁnancial variables. Based on this two-step
approach, we ﬁnd that very high uncertainty episodes are associated with a consistent
and signiﬁcant weakening of economic activity, a decline in inﬂation, an increase in un-
employment and a decline in bond yields and stock prices. Also after controlling for the
impact of business cycle conditions, ﬁnancial and other factors, macroeconomic and ﬁnan-
cial conditions weaken whenever the economy is in a very high uncertainty episode. The
macroeconomic impact of uncertainty is non-linear. Uncertainty matters during episodes of
very high uncertainty, but much less or not at all during periods characterized by medium
or low uncertainty. Regressions using the Survey of Professional Forecasters show that
during very high uncertainty episodes economic agents revise down their expectations of
future output. High uncertainty may thus have played an important role in the weak
growth performance of the US economy in recent years.

10

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12

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Figure 1: Economic Policy Uncertainty index and Monthly probability of
very high uncertainty episodes

Note: This ﬁgure plots the economic policy uncertainty (EPU) index and the monthly probability of being
in a very high uncertainty episode (i.e. the third regime) using the EPU index. The monthly probability
corresponds to the number of days per month when the daily probability of being in the third regime is
higher than 0.5.

14

 00.10.20.30.40.50.60.70.80.91507090110130150170190210230250Feb-1985Jan-1986Dec-1986Nov-1987Oct-1988Sep-1989Aug-1990Jul-1991Jun-1992May-1993Apr-1994Mar-1995Feb-1996Jan-1997Dec-1997Nov-1998Oct-1999Sep-2000Aug-2001Jul-2002Jun-2003May-2004Apr-2005Mar-2006Feb-2007Jan-2008Dec-2008Nov-2009Oct-2010Sep-2011Aug-2012Economic Policy Uncertainty index (LHS axis)Monthly probability of a high uncertainty episode (RHS axis)Figure 2: Daily probability of very high uncertainty episodes (2008-2012)

Note:This ﬁgure plots the probability of being in a very high uncertainty episode (i.e. the third regime)
using the EPU index, the VIX index and a bivariate model based on the EPU and VIX from January 1,
2008 to December 31, 2012.

15

 00.10.20.30.40.50.60.70.80.91EPUVIXBivariate Modele
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18

Table 5: Very high uncertainty episodes and the Survey of Professional Forecasters

Uncertainty episodes classiﬁed based on the EPU index

h = 1

h = 2

h = 3

h = 4

h = 5

Real GDP

No controls

-5.821***

-3.359***

-1.427***

-0.220

0.255

Controls

-2.208***

-1.508***

-0.670

0.012

0.106

Consumption

No controls

-4.130***

-2.836***

-1.320***

-0.533*

-0.174

Controls

-1.061*

-1.243***

-0.397

-0.143

-0.019

Investment

No controls

-17.903***

-13.597***

-8.946***

-4.208***

-1.047

Controls

-2.794

-2.700

-0.961

1.295

1.697

Industrial
Production

No controls

-10.035***

-5.117***

-1.755**

-0.123

1.068*

Controls

-2.460**

-1.101

0.031

0.650

1.094

Bond yields

No controls

-3.278***

-3.327***

-3.203***

-2.999***

-2.734***

Controls

-1.744*

-1.808*

-1.760*

-1.634*

-1.435

Unemployment No controls

4.376***

4.696***

4.810***

4.778***

4.621***

Controls

4.905***

4.823***

4.696***

4.564***

4.398***

Inﬂation

No controls

-3.405***

-1.495***

1.223***

-0.904**

-0.646*

Controls

-2.663***

-1.134**

-0.963**

-0.579

-0.350

Note: This table shows the regression results of equation (2) using the Survey of Professional Forecasters (SPF) by the Federal

Reserve Bank of Philadelphia as a dependent variable. We consider the forecasts for the annualized quarterly growth rates of

real GDP, private consumption, non-residential investment, industrial production, inﬂation (annual change in the CPI index)

as well as the level of the 10-year Treasury bond yields and the unemployment rate. All forecasts are taken as the mean of

the responses for each forecast horizon h={1,2,3,4,5} quarters. The estimation sample runs from 1991:Q1 to 2012:Q4. The

probability of being in a very high uncertainty episode corresponds to the number of days between two SPF deadline dates

(in per cent) when the daily probability of being in the third regime is higher than 0.5. The control variables are the ADS

index (taken as the mean of the ADS index between two SPF deadline dates) and the S&P500 index (taken as the percentage

change in the S&P500 index between two SPF deadline dates).

19

Table 6: Very high uncertainty episodes and the Survey of Professional Forecasters

Uncertainty episodes based on the VIX

h = 1

h = 2

h = 3

h = 4

h = 5

Real GDP

No controls

-1.858***

-1.235***

-0.526***

-0.149

0.132

Controls

-0.399

-0.500**

-0.224

-0.084

0.084

Consumption

No controls

-1.253***

-0.985***

-0.469***

-0.231*

-0.003

Controls

-0.055

-0.344*

-0.118

-0.093

0.069

Investment

No controls

-7.263***

-5.983***

-4.089***

-2.235***

-0.670

Controls

-1.924

-2.209**

-1.400**

-0.520

0.140

Industrial
Production

No controls

-3.424***

2.305***

-1.161***

-0.538*

0.082

Controls

-0.549

-0.921**

-0.651**

-0.413

-0.037

Bond yields

No controls

-1.473***

-1.500***

-1.452***

-1.373***

-1.282***

Controls

-0.876*

-0.907**

-0.889**

-0.845**

-0.791**

Unemployment No controls

0.386

0.598

0.706

0.746

0.735

Controls

0.019

0.115

0.160

0.184

0.189

Inﬂation

No controls

-1.150***

-0.806***

0.830***

-0.761***

-0.673***

Controls

-0.722**

-0.676***

-0.771***

-0.711***

-0.648***

Note: See Table 5

20