Staff Working Paper/Document de travail du personnel 2018-50

Monetary Policy Uncertainty:
A Tale of Two Tails

by Tatjana Dahlhaus and Tatevik Sekhposyan

Bank of Canada staff working papers provide a forum for staff to publish work-in-progress research independently from the Bank’s Governing
Council. This research may support or challenge prevailing policy orthodoxy. Therefore, the views expressed in this paper are solely those of theauthors and may differ from official Bank of Canada views. No responsibility for them should be attributed to the Bank.

www.bank-banque-canada.ca

Bank of Canada Staff Working Paper 2018-50
September 2018

Monetary Policy Uncertainty: A Tale of Two TailsbyTatjana Dahlhaus1 and Tatevik Sekhposyan2
1 International Economic Analysis Department

Bank of Canada
Ottawa, Ontario, Canada K1A 0G9
dahl@bankofcanada.ca
2 Department of Economics

Texas A&M University
College Station, Texas, USA 77843
tsekhposyan@tamu.edu

ISSN 1701-9397© 2018 Bank of Canada

Acknowledgements
The views expressed in this paper are those of the authors and do not represent the viewsof the Bank of Canada. We thank Philippe Andrade, Michael Bauer, Efrem Castelnouvo,
Laurent Ferrara, Bruno Feunou, Marc Giannoni, Alejandro Justiniano, Oleksiy Kryvtsov,
Andy Levin, Barbara Rossi, Dongho Song, Stephen Williamson, Jonathan Wright, andparticipants of seminars at the Bank of Canada, Banque de France, Reserve Bank ofAustralia, National Science Foundation, and University of Girona and at the 2018
Barcelona GSE Summer Forum on Uncertainty in Macroeconomics and Finance; 2018
FRB Conference on Risk, Uncertainty, and Volatility; 2017 Barcelona GSE Summer
Forum on Time Series Econometrics and Applications for Macroeconomics and Finance;
2017 Melbourne Institute Macroeconomic Policy Meetings; 2017 St. Louis Fed Central
Bank Forecasting Conference; and 2017 Midwest Econometrics Group for usefulcomments and suggestions. Alexander Lam provided excellent research assistance. Former
versions of the paper were circulated under “Asymmetries in Monetary Policy Uncertainty:
New Evidence from Financial Forecasts.”iAbstract
We document a strong asymmetry in the evolution of federal funds rate expectations andmap this observed asymmetry into measures of monetary policy uncertainty. We show that
periods of monetary policy tightening and easing are distinctly related to downside (policy
rate is higher than expected) and upside (policy rate is lower than expected) uncertainty.
Downside monetary policy uncertainty decreases over time, while upside uncertainty
remains rather stable, reflecting the asymmetry in the behavior of the expectational
errors—a finding that we attribute to changes in the conduct of monetary policy. We showthat this behavior cannot be entirely explained by uncertainty in macroeconomic
fundamentals: the asymmetry remains even when we control for macroeconomic
uncertainty, emphasizing the importance of monetary policy implementation. Finally, we
assess the macroeconomic effects of monetary policy uncertainty. We find that the effects
are non-linear and conditional on the economy being in an easing or tightening regime.
Though uncertainty is, in general, recessionary, its effects are stronger in a monetary easing
regime relative to a tightening one.
Bank topics: Transmission of monetary policy; Monetary policy communications;
Econometric and statistical methods; Business fluctuations and cycles; Uncertainty andmonetary policy
JEL codes: C18; C32; E02; E43; E52

Résumé
Dans cette étude, nous rendons compte d’une forte asymétrie dans l’évolution des attentes
à l’égard du taux des fonds fédéraux, asymétrie que nous faisons correspondre à desmesures de l’incertitude concernant la politique monétaire. Nous montrons que les périodes
de resserrement monétaire sont liées à une incertitude « négative » (taux directeur supérieur
aux attentes) et celles d’assouplissement monétaire, à une incertitude « positive » (tauxdirecteur inférieur aux attentes). L’incertitude négative concernant la politique monétairediminue au fil du temps, tandis que l’incertitude positive reste plutôt stable. Cette
constatation reflète le comportement asymétrique des erreurs de prévision, que nous
attribuons aux modifications de la conduite de la politique monétaire. Nous montrons quel’incertitude liée aux fondamentaux macroéconomiques ne permet pas à elle seuled’expliquer ce comportement : l’asymétrie demeure, même lorsque nous tenons compte del’incertitude macroéconomique, ce qui fait ressortir l’importance de la mise en œuvre de lapolitique monétaire. Enfin, nous évaluons les effets macroéconomiques de l’incertitudeentourant la politique monétaire. Selon nos résultats, les effets, non linéaires, dépendent dela phase de détente ou de resserrement de la politique monétaire. Même si, en général,l’incertitude a des effets récessifs, ces derniers sont plus sensibles en périoded’assouplissement qu’en période de resserrement monétaire.
Sujets : Transmission de la politique monétaire; Communications sur la politiquemonétaire; Méthodes économétriques et statistiques; Cycles et fluctuations économiques;
Incertitude et politique monétaire
Codes JEL : C18; C32; E02; E43; E52ii

Non-Technical Summary
During the last few decades, central banks across the globe came to recognize the potentiallyvaluable role that transparency can play in stabilizing the economy. For example, the Federal
Reserve in the US started releasing a statement describing policy actions regarding the effective
policy interest rate. Further, forward guidance, an explicit communication from the Federal
Reserve about the likely future course of monetary policy, has been one of the important tools of
unconventional monetary policy in the post-financial crisis zero lower bound (ZLB) period ofpolicymaking.
It has been established that interest rate predictability has improved with increasing central banktransparency and better communication strategies, as the public could form more accurate
expectations about the current and future path of interest rates (as a function of economic
conditions). This, in turn, is thought to translate into lower levels of monetary policy uncertainty,
which typically has an expansionary effect as it encourages investment and spending.
Motivated by the distinct link of interest rate predictability and monetary policy uncertainty, werely on the accuracy of expectations of the policy rate to characterize monetary policy uncertainty.
As a basis for our analysis, we employ federal funds rate forecast errors for several horizons
implied by the Blue Chip Financial Forecasts (BCFF). For robustness, we also consider revisions
in federal funds futures prices around Federal Open Market Committee (FOMC) announcement
dates – the so-called “surprise.” It turns out that positive and negative forecast errors occur inclusters and are tightly linked to the behavior of the policy rate. Namely, negative (positive)
forecast errors dominate during the episodes of monetary easing (tightening). Moreover, theconditional (historical) forecast error distributions become tighter over time indicating that theforecast error dispersions decreased over time. Strikingly, this decline is nearly entirely driven by
the fact that the right tail (associated with positive forecast errors and tightening episodes) shortens
over time, while the left tail (associated with negative forecast errors and easing episodes) shows
no apparent downward trend and exhibits mainly cyclical variations. We attribute this behavior toincreasingly better-communicated policy actions from the Federal Reserve during monetary
tightenings.
To measure monetary policy uncertainty, we summarize the asymmetry in the forecast error
evolution using a metric that quantifies uncertainty in terms of probabilities and, therefore, allows
us to take the asymmetric tail behavior into account. More specifically, the proposed measurequantifies the ex-ante likelihood of the observed outcome, measured by the probability associatedwith being below or above the realized forecast error, at every point in time. Moreover, our metricof monetary policy uncertainty combines both left- and right-tail behaviors of forecast errors to asingle joint measure. This simplifies the analysis of the economic effects of monetary policy
uncertainty. Since our uncertainty measure is based on the forecast errors, it inherits all the
documented asymmetries and time-varying properties associated with the forecast errors.
Lastly, we investigate whether there is non-linear propagation of monetary policy uncertainty
shocks and whether it matters for macroeconomic dynamics. We assess the macroeconomic
implications of monetary policy uncertainty by analyzing its effects conditional on the economy
being in a monetary tightening or easing cycle. We find recessionary effects that are stronger ineasing relative to tightening. After purging our measure for macroeconomic uncertainty, effects
generally get dampened, but non-linearities remain.

“Central Banking [has been] traditionally surrounded by a peculiar mystique... The mystiquethrives on a pervasive impression that Central Banking is an esoteric art. Access to thisart and its proper execution is confined to the initiated elite. The esoteric nature of the art
is moreover revealed by an inherent impossibility to articulate its insights in explicit andintelligible words and sentences.” Karl Brunner, 1981 1

1Introduction

Monetary policy currently markedly differs in its conduct relative to the quote above. Nowadays, monetary policy is more transparent in its implementation, as central banks acrossthe globe have come to recognize the potentially valuable role that transparency can playin stabilizing the economy. For example, in the US, the Federal Reserve started releasing
a statement describing policy actions in regards to the federal funds rate (FFR) after theFederal Open Market Committee (FOMC) meetings in 1994, which was followed by a number of changes in the communication strategy in the subsequent years. A notable exampleof these new strategies is (various forms of) forward guidance: an explicit communicationfrom the Federal Reserve about the likely future course of monetary policy, which has been
one of the important tools of unconventional monetary policy in the post-financial crisis
zero lower bound (ZLB) period of policymaking.2
It has been established that interest rate predictability has improved with increasing
central bank transparency and innovative communication strategies (e.g., Poole and Rasche,
2003; Swanson, 2006), as the public has been able to form more accurate expectations about
the current and future path of interest rates (as a function of economic conditions). This, inturn, is thought to translate into lower levels of monetary policy uncertainty (see Bernanke,2004), which typically has an expansionary effect as it encourages investment and spending.
A large empirical literature focuses on measuring macroeconomic uncertainty and its
effects on the economy, see for instance Bloom (2009), Jurado et al. (2015), Rossi and
Sekhposyan (2015), Scotti (2016), and Jo and Sekkel (2017), among others. Monetary
policy uncertainty is peculiar, since, relative to other sources of uncertainty, the central
bank could be one of the main drivers behind it. There is a nascent and much narrower
1

This quote is taken from Blinder (2002).
See Blinder et al. (2008) and Feroli et al. (2017) for a discussion of central bank communication over
time.
2

2

literature concerned with measuring monetary policy uncertainty directly. Some recent
contributions on this are Sinha (2015), Baker et al. (2016), Creal and Wu (2017), Fontaine(2016), Husted et al. (2016), and Istrefi and Mouabbi (2017).
Our approach differs from this scarce but growing literature measuring US monetary
policy uncertainty by providing several original contributions. First, we thoroughly analyze
survey-based as well as market-based expectations of the policy rate and their conditional(historical) distributions. In doing so, we find a strong asymmetry in the evolution of these
expectations, which we show to be related to the conduct of monetary policy.3 Second,in contrast to symmetric measures of uncertainty, we map the observed asymmetry in theaccuracy of these expectations to obtain measures of monetary policy uncertainty. Third,
we quantify the importance of macroeconomic conditions for monetary policy uncertainty.
As such, we decompose the sources of monetary policy uncertainty into two components: acomponent associated with systematic monetary policy and a component associated withdiscretionary monetary policy actions. We show that the observed asymmetry is preserved.
Lastly, we identify non-linearities in the transmission of monetary policy uncertainty to themacroeconomy.
More specifically, to analyze monetary policy uncertainty we start from the premise thatwhether monetary policy is more or less uncertain depends on the predictability of the policy
rate.4 We use survey-based and market-based forecasts of the FFR to measure expectations
about current and future monetary policy decisions. In our benchmark specification we rely
on FFR forecast errors for several horizons implied by the Blue Chip Financial Forecasts(BCFF) survey as a basis for our analysis. As robustness, we also consider revisions infederal funds futures prices around FOMC announcements dates – the so-called “surprise”(Kuttner, 2001).
We start by uncovering interesting and novel facts about the asymmetric dynamics ofthe forecasts errors from the BCFF and the market-based surprises. Positive and negative
forecast errors occur in clusters and are tightly linked to the behavior of the policy rate.
Namely, negative (positive) forecast errors dominate during the episodes of monetary eas3
Therefore, we also add to the recent literature highlighting the importance of asymmetric risks inmacroeconomic forecasts. Adrian et al. (2018) analyze the time-varying asymmetric behavior of GDP growth,
while Andrade et al. (2012) do so for inflation.
4
Jurado et al. (2015), Rossi and Sekhposyan (2015), Scotti (2016), and Jo and Sekkel (2017) have asimilar approach to characterizing macroeconomic uncertainty.

3

ing (tightening). Moreover, the conditional (historical) forecast error distributions becometighter over time, indicating that the forecast error dispersions decreased over time. Strikingly, this decline in volatilities is nearly entirely driven by the right side of the conditional
distributions, as there is asymmetric time-varying behavior in the tails of the forecast error distributions. In particular, the right tail (associated with positive forecast errors andtightening episodes) shortens over time, while the left tail (associated with negative forecast
errors and easing episodes) shows no apparent downward trend and exhibits mainly cyclicalvariations. We attribute this behavior to increasingly better-communicated policy actions
from the Federal Reserve during tightenings.
To measure monetary policy uncertainty, we summarize the asymmetry in the forecast
error evolution using a metric that quantifies uncertainty in terms of probabilities and,
therefore, allows us to take the asymmetric tail behavior into account. More specifically,
the proposed measure quantifies the ex-ante likelihood of the observed outcome, measured
by the probability associated with being below or above the realized forecast error, at every
point in time. It is similar to measures such as Value at Risk (VaR) and tail risk but does
not rely on arbitrary thresholds: it rather relies on a realization. Moreover, our metric ofmonetary policy uncertainty combines both left- and right-tail behaviors of forecast errors
to a single joint measure (instead of having a time-varying measure for the right-tail andleft-tail risks, for example). This simplifies the analysis of the economic effects of monetary
policy uncertainty.
Since we quantify monetary policy uncertainty based on federal funds forecast errors,
it could potentially pick up macroeconomic uncertainty. One could argue that forecasts
are less accurate in monetary policy easing cycles since macroeconomic fundamentals are
harder to forecast in recessions, and that this may drive the observed asymmetric behavior
of FFR forecasts. In order to address this issue we also consider two alternative ways toquantify monetary policy uncertainty. These alternatives allow us to separate the uncertainty associated with the discretionary monetary policy from the uncertainty associatedwith the systematic component of monetary policy. We purge monetary policy uncertainty
of macroeconomic uncertainty using the underlying “key assumptions” provided by the survey participants in the BCFF. First, we construct uncertainty measures for output growthand inflation and obtain a measure of monetary policy uncertainty not explained by those4

(a residual-based measure). Second, we assume that agents form their interest rate expectations based on a Taylor rule and, consequently, can purge the forecast errors directly ofthe expectational errors in output and inflation. We then construct an uncertainty measure
based on these “Taylor rule residuals.” We show that the asymmetry in the evolution of
forecast errors remains, regardless of the method used to purge monetary policy uncertainty of macroeconomic uncertainty. We note that our conclusions are derived using thereal-time data for macroeconomic fundamentals (as interest rates are not revised); thus they
incorporate the natural information flow of the economy and are not contaminated by datarevisions.
We further investigate whether there is non-linear propagation of monetary policy uncertainty shocks and whether it matters for macroeconomic dynamics. We assess the macroeconomic implications of monetary policy uncertainty by analyzing its effects conditional on
the economy being in a monetary tightening or easing cycle. We find recessionary effects
that are stronger in easing relative to tightening. After purging our measure of macroeconomic uncertainty, effects generally get dampened, but non-linearities remain. Moreover,
the results are robust to the various forecast horizons used to calculate the forecast errors
and the associated uncertainty indices. However, we find significant recessionary effects
only for the longer expectations horizons in the case of tightening episodes.5
This suggests that uncertainty about discretionary monetary policy appears to have an
important role when monetary policy is both easing and tightening (for the latter case at
the longer expectational horizons). Although macroeconomic uncertainty (i.e., uncertainty
about macroeconomic fundamentals) seems to be relevant for macroeconomic transmission,
it is not the main driver of monetary policy uncertainty. Further, clearer communication intightening could be linked to better anchored expectations about the path of the monetary
policy, which in turn makes monetary policy uncertainty less relevant for macroeconomic
dynamics at the short horizons. However, expectations are less anchored at the longer
horizons given that we observe significant contractionary macroeconomic effects at thosehorizons during tightening, suggesting that monetary policy communication might aim at
pinning down shorter-term expectations better.
5
Non-linear effects of macroeconomic (as opposed to monetary policy) uncertainty have been documented
in Caggiano et al. (2014) and Mumtaz and Theodoridis (2018). However, in our setup we not only have a
way to assess asymmetric propagation, but also understand the relative importance of alternative sources ofmonetary policy uncertainty, i.e., discretionary versus systematic.

5

Though our analysis focuses on the FFR expectations and uncertainty in regards toconventional monetary policy, we also look at the longer-term interest rate expectations(10-year treasury bond yields) to gain insights about unconventional monetary policy uncertainty at the effective ZLB. We provide some evidence on how the uncertainty evolves
over time. We should note, however, that given the small sample size, these results are
merely suggestive. To assess the role of unconventional monetary policy tools for uncertainty at the ZLB we need more observations, and expanding the analysis in a cross-sectional
dimension might be helpful.
The paper proceeds as follows. In Section 2, we describe the data and discuss the dynamics of FFR forecasts and their relation to monetary policy. In Section 3, we present ourmeasure of monetary policy uncertainty. We then assess the role of uncertainty stemmingfrom macroeconomic fundamentals and compare our monetary policy uncertainty indiceswith other measures. Section 4 discusses the non-linear macroeconomic impact of monetary
policy uncertainty, and Section 5 briefly introduces a measure of (unconventional) monetary
policy uncertainty at the effective ZLB based on 10-year government bond yield forecasts.
Section 6 concludes.

2

Federal Funds Rate Forecasts and Monetary Policy

We employ survey-based expectations of the FFR taken from the BCFF. This survey works
well for our purposes since it provides forecasts of the whole yield curve including the FFR.6
Below we discuss the data and forecast properties and dynamics.

2.1

Data

Since 1982, the BCFF survey is conducted monthly, covering approximately 50 analysts
ranging from broker-dealers to economic consulting firms. The BCFF is published on thefirst day of each month and presents forecasts from a survey conducted during two consecutive business days one to two weeks earlier. The precise dates of the survey vary and are
not generally noted in the publication. Since April of 1983, each month the BCFF provides the forecasts of the average interest rate over a particular quarter, beginning with the6
For instance, the US Survey of Professional Forecasters (SPF) contains only forecasts for the 3-monthtreasury bill and 10-year treasury bond rates, starting from 1984:Q3 and 1992:Q1, respectively.

6

current quarter and up to four or five quarters into the future.7 For example, in January,
the forecast of the current quarter captures the average expected outcome over January,
February, and March, and the one-quarter-ahead forecast pertains to the average expected
outcome of April, May, and June.
Therefore, the monthly BCFF forecasts are fixed-event forecasts of interest rates over thequarter, implying that their forecast horizon changes with each month in the quarter. We
construct fixed-horizon forecasts by weighting the two given fixed-event forecasts following
Chun (2011) (or see Dovern et al., 2012, for an application to survey forecasts of GDP andprices). In order to obtain, for instance, a six-months-ahead (fixed-horizon) forecast, welook at one-quarter- and two-quarters-ahead (fixed-event) forecasts. In the first month of
the quarter, the six-months-ahead forecast is simply the forecast of the one-quarter-ahead
forecast. In the second month of the quarter, the six-months-ahead forecast is obtained bytaking the average of the one-quarter- and two-quarters-ahead forecasts with weights equal
to 2/3 and 1/3, respectively. The six-months-ahead forecast for the final month of thequarter is the weighted average of the one-quarter- and two-quarters-ahead forecasts, withweights equal to 1/3 and 2/3. Nine-months-ahead forecasts are calculated as the weighted
average of the two-quarters- and three-quarters-ahead forecasts given by the survey, withweights similar to the ones discussed above.8
We obtain forecast errors by subtracting the consensus forecasts (mean across the 50
analysts) from the realizations that are available from the Federal Reserve Board’s H.15
website. In Section 3.4, we use the difference between top-10-average and bottom-10-average
forecasts to obtain a measure of disagreement, as in Andrade et al. (2016). Our sample
period is from 1983:04 to 2018:04, though the effective sample size depends on the forecasthorizon.

2.2

Key Features of Federal Funds Rate Forecasts

Figure 1 plots the forecast errors for the FFR obtained from the BCFF over time. Forecast
errors are shown for several forecast horizons, i.e., three-months-, six-months-, nine-monthsand twelve-months-ahead. A few characteristics stand out. First, forecast errors across7

Before 1983, forecasts exist only for the current and then every other quarter.
We could conduct a robustness exercise by looking at the end-of-quarter forecasts from BCFF. This
would enable us to obtain fixed-horizon forecasts directly from the surveys at the expense of moving to alower, quarterly frequency analysis.
87different horizons co-move strongly and move in cycles. Not surprisingly, errors are small
during the ZLB period. Second, forecast errors are generally larger for longer-horizon
forecasts.
Furthermore, by looking at Figure 1, it is apparent that forecast errors in the FFR
are tightly linked to the federal funds target rate (depicted with dashed lines). Errors
are generally negative when the target rate is decreasing, i.e., during easing cycles, andpositive when the target rate is increasing, i.e., during tightening cycles. Thus, it appears
that the Fed policy rate is commonly lower than expected during easing cycles and higher
than expected in periods of tightening. Interestingly, there seems to be one exception tothis relationship related to the lift-off of policy rates following the ZLB. Surrounding theFederal Reserve’s first rate hike, forecast errors of the FFR were negative, but have turned
positive for the subsequent hikes. This suggests that analysts expected the Fed to departfrom the ZLB sooner.
Moreover, positive forecast errors appear to be smaller in magnitude than negative
forecast errors. In addition, the magnitude of positive forecast errors seems to be decreasingover time, while the magnitude of the negative forecast errors seems to be stable overtime. This essentially implies that though the analysts typically underestimate the policy
decision, they underestimate more so in easing (when interest rates are going down) relative
to tightening (when interest rates are going up), and there has been a change in how agents(forecasters, central bank, etc.) behave in interest rate tightening.
These findings are supported by Table 1, which reports the sample moments of the
BCFF forecast errors across easing and tightening subsamples. There are no well-establisheddefinitions of easing and tightening cycles in the literature. We resort to identifying easing
cycles by looking at periods where the one-month lag of the change in the federal funds target
rate is negative and tightening cycles by looking at periods after positive monthly changes
in the federal funds target rate.9 Consistent with the visual inspection of Figure 1, thesample mean of forecast errors in easing cycles is negative, while the average forecast error
in a tightening cycle is positive. Moreover, the forecast errors are, in absolute value, less intightening relative to the ones in easing: Analysts, on average, underestimate the interest
rate movements by the Federal Reserve; however, they are less surprised in tightening than9
It should be noted that tightening and easing subsamples do not sum to the overall sample used foranalysis since there are many periods where the FFR target does not change.

8

in easing. The same patterns emerge when looking at the conditional distributions of theforecast errors (see Figure 23 in the Appendix).
Table 2 conducts formal unbiasedness and rationality tests for the forecast errors.10
As the full-sample unbiasedness results suggest, the forecast errors overall are biased at
all conventional significance levels. However, subsample analysis suggests that the bias ismainly driven by easing episodes, and there is not much evidence for forecast biasedness
in monetary policy tightenings. As far as the rationality (joint test for unbiasedness andefficiency) of the forecasts goes, the forecasts predominantly fail rationality both in a fullsample as well as in subsamples, suggesting that high and low values of the FFR are treateddifferently by the forecasters.11
The analysis above shows that positive and negative forecasts errors are concentrated
during episodes of monetary policy tightening and easing, respectively, with errors being on average smaller and decreasing over time in tightening episodes. To assess thesetime-varying dynamics of federal funds forecasts errors further, we calculate time-varying
distributions of the forecast errors. We use five years of monthly data (from 1983:04 to
1988:03) to approximate the conditional distribution of forecast errors in the beginning of
the sample period. We further update the distribution with monthly observations as they
become available; i.e., we use an expanding time window. Figure 2 shows the evolution of
the forecast error distributions over time.
The distributions become tighter over time, indicating that the forecast error dispersions
decreased over time. Strikingly, this decline in volatilities is nearly entirely driven by theright side of the conditional distributions, as there is asymmetric time-varying behavior
in the tails of the forecast error distributions. In particular, the right tail (associatedwith positive forecast errors and tightening episodes) shortens over time while the left
tail (associated with negative forecast errors and easing episodes) shows mainly cyclical10
The Wald test statistics is constructed with a Newey and West (1987) HAC-estimator, with a truncation
parameter of P 1/4 , P being the effective sample size.
11
Some preliminary evidence on the dependence of the forecast errors on monetary tightening and easing regimes is provided by the New York Fed economists on the Liberty
StreetEconomics
blog
at
http://libertystreeteconomics.newyorkfed.org/2011/08/
a-look-at-the-accuracy-of-policy-expectations-1.html.
Cieslak (2018) provides evidence of
ex-post predictability of the FFR forecast errors by macroeconomic fundamentals, for instance employment
growth, and shows its further importance for bond return predictability. Though, in theory, the documented
behavior of the forecast errors could be consistent with the rationality of the forecasts under a noisy
information setting, it is rejected in Cieslak (2018).

9

variations. Figure 3 makes that point even clearer by showing the probabilities associatedwith forecasts errors one standard deviation below and above the mean – the so-called tailrisk. Since the mid-1990s the right-tail risk has declined steadily. This is also consistent withthe decline in the magnitude of positive forecasts errors as described previously. But theleft-tail risk does not have such an apparent trend and exhibits only cyclical time variations.
By visual inspection these movements seem to be related to the monetary policy cycle. In an
easing cycle, the risk associated with the left tail seems to increase. This cyclical variation
could be also related to the business cycle, an issue that we analyze later.

2.3

Relation to the Fed Communication Strategies

The natural question that follows after observing the dynamics of the FFR forecast error
distributions is whether they are related to the Federal Reserve’s communication strategies.
More specifically, one could ask whether the asymmetric time-varying behavior of forecast
errors is in any way related to the type and intensity of the communication coming from theFederal Reserve. In order to answer these questions, we look for some narrative evidence
coming from the post-FOMC-meeting releases. The analysis in this section is mainly related
to the evidence provided in Feroli et al. (2017).
Before February of 1994, there were no FOMC announcements and the public was left
to infer policy actions from the developments in the FFR markets. FOMC communication
via post-meeting statements started in February of 1994, with the onset of the 1994–95
tightening cycle. The evolution of the distributions seems to be in line with the introductionof post-meeting statements. For example, one can observe a shift in the time-varying
distributions in the mid-1990s and the right-tail risks started to decline steadily.
Initially, post-meeting statements accompanied only policy changes and were not regular. Regular post-meeting statements started in May of 1999, with their initial intent
being threefold: (a) to announce and explain policy changes, if any; (b) to provide information about the committee’s view on current and future economic developments; and (c) toprovide (some) guidance on future policy actions. From the onset of the release of the regular statements, the FOMC has been using language embedding guidance on future policy
actions.
Feroli et al. (2017) provide an index (varying from 0 to 5) of relative strength of FOMC’s

10

forward guidance in post-meeting statements since 1999 (see their chart 2.3). In general,
their index is higher (in absolute value) in the tightening cycles relative to the easing ones:
dynamics that are in line with the observed BCFF forecast patterns. Moreover, it appears
that over the tightening cycles, the Fed uses more “time-based” forward guidance, where it
commits to set policy rate at specific levels for specific future dates. This is in contrast to
the “data-based” forward guidance, where a state-contingent policy rate is communicated.
Given their methodology, the period from 2003 to 2006 is a strong time-based guidance period, where communication involved terms such as “policy accommodation can be removed
at a pace that is likely to be measured” and the like. That seems to coincide closely with
the noticeable decline of the forecast errors in the first tightening episode of the early 2000s.
However, given the small set of occurrences (two easing and two tightening cycles), thisprovides a narrative for only the asymmetric behavior of forecast errors.
Summing up, we attribute the decreasing dispersion of forecast errors to the decline in
the upside risk to federal funds forecast errors, which, following the above narrative, may
be associated with improvements in the Federal Reserve’s communication strategies during
tightening episodes. One could argue that forecasts are less accurate in easing cycles sinceeasing episodes may coincide with recessions and are, therefore, harder to communicate.
We shed light on the role of business cycle fluctuations for the asymmetric time variationsof the conditional distributions in Section 3.3.

3

Measuring Monetary Policy Uncertainty

In this section, we first lay down the framework in which we construct our measure of monetary policy uncertainty. Second, we present the uncertainty index obtained from the FFR
survey-based forecast errors. Third, we purge our index of uncertainty about macroeconomic
conditions and we compare it with alternative measures of monetary policy uncertainty. Finally, for robustness, we construct a market-based measure of monetary policy uncertainty
based on federal funds future surprises.

11

3.1

The Uncertainty Index

When thinking of measuring monetary policy uncertainty, it would be preferable to havean index that summarizes the asymmetry and the tail behavior of the forecast error distribution. Dispersion measures, such as variance or volatility, will be unable to do so since
they would penalize positive forecast errors as much as negative ones. The uncertainty
index we introduce for monetary policy quantifies uncertainty in terms of probabilities and,
therefore, allows us to take the asymmetric tail behavior into account. More specifically, itmeasures the probability associated with being below or above the realized forecast error.12
The proposed index is similar to measures such VaR and tail risk (for instance, the measure
depicted in Figure 3). However, it does not require the choice of an arbitrary thresholdcriteria (such as a probability value for VaR or one/two standard deviation cutoffs for the
tail risk).
To emphasize, we think about monetary policy being more uncertain when there is lesspredictability at any given point in time. In particular, the uncertainty index is basedon the conditional distribution of the forecast errors and captures the ex-ante probability
that one would assign to the forecast error, given the historical distribution of the forecast
errors. The further away from the (theoretical) mean–i.e., 0.5–the higher the uncertainty.
Most importantly, we can distinguish between upside and downside uncertainty, i.e., identify
situations where a forecast error is above or below the mean of the distribution. A similar
approach is applied in Rossi and Sekhposyan (2015) to assess macroeconomic uncertainty.
More formally, let the forecast error at time t + h be denoted by et+h = yt+h − Et (yt+h ),i.e., this is the forecast error associated with the h-step-ahead forecast formed with all theavailable information at time t, and t refers to the forecast origin date. Let f (e) denotethe probability density function (PDF) of the forecast errors, et+h . Given et+h and f (e),R et+hthe index at time t + h is defined as Ut+h = −∞f (e) de. In order to capture upsideand downside uncertainty and have them directly comparable to each other in magnitude,+−
we consider the index Ut+h= 21 + max Ut+h − 12 , 0 for upside uncertainty and Ut+h
=

1
1
1
1
∗
2 + max 2 − Ut+h , 0 for downside uncertainty. Ut+h = 2 + Ut+h − 2 , on the other
hand, would be the measure of overall uncertainty. Note that, by construction, the overall
12

To the extent that forecast errors seem to be correlated over time, one could obtain our index based on
the predicted forecast error, thus making the index useful for economists in real time.

12

measure of uncertainty as well as the upside and downside ones fluctuate between 0.5 and1. In general, the overall measure of the uncertainty will account for the probability mass
associated with outcomes above and below the realization.
We provide an illustrative example below. Consider Figure 4, which has three panels.
Let us consider two distinct episodes. The first one is in February of 2006 (labeled as
2006:02), when Chairman Bernanke assumed the chairmanship of the Federal Reserve, while
the second episode pertains to August of 2008 (labeled as 2008:08), i.e., a month before
Lehman declared bankruptcy. The first panel shows the probability densities of the forecast
errors associated with the six-months-ahead FFR forecasts provided by BCFF. The red PDF
uses forecast errors up to 2006:01, while the blue PDF is obtained from data up to 2008:07.
Since the probability distribution is based on the historical forecast errors, it evolves overtime. By looking at the figure, one could notice that the left tail of the distribution has
been moving, showcasing the fact that most of the forecast error realizations have been in
the left tail between these two time periods. Moreover, the figure in the first panel showsthat the forecast error associated with the change in the Fed chairmanship is positive. On
the other hand, the forecast error associated with August of 2008 is negative and much
more unlikely ex-ante than that associated with February of 2006. The second panel shows
the CDFs corresponding to the PDFs, i.e., Ut+h . The last panel shows the resulting upsideand downside uncertainty indices: there is high positive uncertainty associated with 2008:8,
while the uncertainty associated with 2007:2 is a downside one and lower.
Thus, our measure of uncertainty successfully captures the dynamics of the distributionof the forecast errors documented previously and quantifies the realization with a probability
measure that could have been attributed to it ex-ante. An implied feature of our uncertainty
index is that a smaller forecast error (in absolute value) could be associated with the samelevel of uncertainty as a larger error (in absolute value), when the forecast error distribution
becomes tighter (a fact that we have documented earlier).
In constructing the figure and throughout the paper we follow the notion that interest
rates above expectations constitute downside uncertainty, since higher interest rates are
typically deterrents for growth. On the other hand, forecast errors below expectations arelinked to upside uncertainty, since they typically stimulate economic activity. As apparent
from the example, we obtain the monetary policy uncertainty indices from the distributions

13

in real time. More specifically, at each t (point in time), the distribution is constructed using
up to t period forecasts and time t + h realizations. The initial distribution is constructedusing 15% of the sample data. The distribution is further updated based on an expanding
window scheme, i.e., incorporating additional observations as they become available throughtime (as mentioned in Section 2.2).
Needless to say, we could also choose to construct the distributions based on a fixedrolling window of observations, i.e., by truncating one initial observation as we include a
new one. This strategy would increase the overall level of uncertainty at the end of thesample relative to what is reported. Given the strategy we adopt, we have uncertainty
indices available from 1988:07 to 2018:01. The effective sample period depends on theforecast horizon.

3.2

Monetary Policy Uncertainty

To formalize the idea of uncertainty surrounding monetary policy and to summarize theobserved asymmetry in the evolution of federal funds forecast errors, we now introduce the
distribution-based measure of uncertainty described in the former subsection. For sake ofbrevity, we discuss the uncertainty measure obtained from the six-months-ahead forecast
errors. The results for other forecast horizons is delegated to the appendix (see Figures
24–26).
Figure 5 plots the real-time uncertainty index obtained from federal funds forecast errors.
We use five years of monthly data (from 1983:04 to 1988:06) to approximate the conditional
distribution of forecast errors in the beginning of the sample period. We further update the
distribution with monthly observations as they become available.
One of the key features of the uncertainty measure is that we can distinguish between
upside and downside uncertainty. Blue (light-colored) bars in Figure 5 indicate periods of
upside uncertainty and red (dark-colored) bars indicate periods of downside uncertainty.
Since our measure is based on the time-varying forecast error distribution, the findings
related to the behavior of the forecast errors directly translate into the characteristics of
the uncertainty index.
Upside and downside uncertainty are strongly clustered. Tightening cycles are associatedwith downside uncertainty (i.e., interest rates are higher than expected) and easing cycles

14with upside uncertainty (i.e., interest rates are lower than expected). As before, one possible
explanation is that the Fed generally cut or hiked rates more aggressively than anticipated.
There is no historic evidence for uncertainty related to the Fed intervening less aggressively
than anticipated. However, there is one exception: while uncertainty is generally low overthe ZLB period, upside uncertainty started to increase in late 2014, immediately after the
Fed ended purchases in October 2014. This particular episode of upside uncertainty atthe end of our sample seems to be related to the uncertainty surrounding the lift-off ofthe FFR. Agents overestimated the policy rate consistently over that particular period,
implying that they expected the Fed to increase rates from the ZLB earlier. However, we
can observe downside uncertainty since mid-2016, thus returning to the historical pattern
for the following rate hikes. Finally, downside uncertainty somewhat decreases over time
while upside uncertainty remains rather stable, reflecting the asymmetry in the tail behaviorof the forecast error distributions discussed earlier.

3.3

The Role of Macroeconomic Uncertainty

3.3.1

Purging of Macroeconomic Uncertainty

So far, our measure of monetary policy uncertainty is based on forecast errors of the FFR.
Thus, we assume that monetary policy is more or less uncertain given how predictable thepolicy rate is. However, it is possible that the uncertainty associated with the interest rate
forecasts is due to the uncertainty about the macroeconomy. For instance, it is reasonable
to think that uncertainty about inflation and real economic activity is higher in recessions.
This could be reflected in interest rate uncertainty, yet it is capturing macroeconomic uncertainty instead of monetary policy uncertainty. Hence, given our current measure ofmonetary policy uncertainty, it is unclear whether the unpredictability in the FFR is due touncertainty about economic conditions. If uncertainty stems from uncertainty about underlying economic conditions, this cannot be related to uncertainty surrounding the conductof monetary policy (such as communication).
To address this issue, we purge the monetary policy uncertainty index of uncertainty
in macroeconomic conditions, that is, output growth and inflation uncertainty. In the
BCFF, survey participants also provide their forecasts for the underlying macroeconomic
conditions. Therefore, we use the same framework to construct GDP growth uncertainty
15

and GDP deflator-based inflation uncertainty using the six-months-ahead GDP growth andinflation forecast errors, respectively. The results for other forecast horizons are delegatedto the appendix (see Figures 27–29). Assume, for example, that there was an unexpected
event influencing GDP growth: this will be reflected in an unusually high GDP growthforecast error and, thus, a high level of output growth uncertainty (relative to the historical
distribution of forecast errors). Both GDP and the GDP deflator are revised multiple times
throughout the quarter. In calculating the forecast errors associated with these variables
we rely on the Advance releases of the data, i.e., the first publically released values of the
GDP and GDP deflator that are available in the first month of the subsequent quarter.
Figures 6 and 7 show the uncertainty index obtained for six-months-ahead BCFF forecast errors of GDP growth and inflation, respectively. As expected, neither output growth
nor inflation uncertainty distinctively relate to the monetary policy cycle. Output growthand inflation uncertainty are generally high during recessions. In contrast to our monetary
policy uncertainty index, which, by construction, attains low values during the ZLB period,
macroeconomic uncertainty is relatively volatile and can reach high values over that period.
The purged monetary policy index is obtained as a residual from regressing the monetary
policy uncertainty index on the indexes of output growth and inflation uncertainty. It would
be possible to construct a monetary policy uncertainty index after purging the interest rate
forecast errors from the errors associated with output growth and inflation. This is thestrategy we follow in the next section. Here, it is assumed that the higher moment dynamics
between inflation, output, and interest rates are distinctly different from the first momentdynamics. Figure 8 plots the purged and the original monetary policy uncertainty indices.13
The purged index exhibits similar dynamics and correlates with the original measure witha correlation coefficient of 0.73 (in the overlapping period). Episodes of high uncertainty,
such as the early and late 2000s easing cycles that are concurrent with recessions, remainhigh when controlling for macroeconomic uncertainty.
3.3.2

Purging Forecast Errors

As noted previously, an alternative approach one can take is to purge the FFR forecast
errors of the forecast errors associated with output growth and inflation before obtaining
13

The purged index starts in 1990:09, which is later than the starting date of the original index. This isbecause of the availability of inflation forecasts in the BCFF.

16

the uncertainty measure. Thus, we regress the six-months-ahead interest rate forecast errors
on output and inflation forecasts. We obtain the residuals and construct the uncertainty
index discussed before based on these residuals.14 The purged errors exhibit similar patterns
to the original (unpurged) BCFF forecast errors discussed in Section 2.2 (Figure 9). First,
the Taylor-rule residuals are tightly linked to the monetary policy cycle, i.e., they are on
average positive during tightening episodes and negative during easing episodes (see Table
3, column labeled as “Residual”). Second, the asymmetric evolution in the tails of the
distribution based on the purged errors remains. As is the case for the original forecast
errors, Figure 10 shows that the right tail of the residual-based conditional distribution
tightens over time, while the left tail does not exhibit this downward trend.
Figure 11 shows the associated residual-based uncertainty index. As we can see, though
the uncertainty index is more noisy (it is based on a regression residual), the relationshipwith the federal funds target rate remains: monetary policy tightenings are associated withdownside uncertainty, while easings with upside uncertainty. Downside uncertainty is typically lower in magnitude relative to upside uncertainty and seems to be decreasing over time,again reflecting the notion that tightenings have been increasingly better communicated tothe market relative to easings.

3.4

Comparison with Alternative Uncertainty Indices

We compare our metric of monetary policy uncertainty at the six-months-ahead horizon withother alternatives. In particular, we consider the measures of policy uncertainty proposedby Baker et al. (2016) (BBD, hereafter) based on news announcements from Access WorldNews and 10 major papers, the refined news-based measure by Husted et al. (2016) (HRS,
hereafter), realized volatility of six-months-ahead federal funds futures, and disagreementin the BCFF at the six-month horizon. Figure 12 shows the alternative indices over time,and Table 4 shows the correlations with our measure in full sample and conditional on the14

In doing so we overwrite the decision making of the agents in forming interest rate expectations andassume they use a Taylor-type rule in forming their expectations. For instance, assume that the interest
rate is set based on a textbook example of it = ρ + φπ πt + φy yt + νt , where νt is a conditionally mean
zero disturbance. Then, the forecast error associated with the interest rate is it − Et−1 (it ) = φπ (πt −Et−1 (πt )) + φy (yt − Et−1 (yt )) + νt . By regressing the forecast errors associated with the FFR on the forecast
errors associated with output growth (yt ) and inflation (πt ), we obtain a “pure” measure monetary policy
uncertainty νˆt , a component not affected by macroeconomic uncertainty. The same expression would holdeven if we assumed interest rate smoothing in the Taylor rule. We note that neither approach used forpurging takes into account the parameter estimation error in the construction of the uncertainty index.

17monetary policy cycle.
The upper left panel of Figure 12 displays the BBD monetary policy uncertainty (MPU)
measures. The MPUs based on Access World News and 10 major papers exhibit spikes
during the Fed easing cycles in the early 1990s and 2000s as well as the one following thefinancial crisis of 2007. In contrast to our measure of uncertainty, during Fed tightening
episodes, the BBD measures remain generally at low levels. Interestingly, our index captures
the heightened uncertainty surrounding the lift-off of the FFR, while the BBD measure
based on Access World News remains at historically low levels. The measure based on 10
major newspapers, however, reports high historically unprecedented levels of uncertainty.
The refined news-based measure by HRS behaves very similarly to the BBD measure basedon 10 major newspapers, with the only exception that uncertainty is very low during thepost-crisis easing cycle (see upper right panel of Figure 12). Given the observed patternsof the news-based measures, it is not surprising that our measure positively correlates
more during easing cycles than during tightening cycles with both BBD measures and the
HRS measure. However, once we compare our measure that is purged of macroeconomic
uncertainty, correlations with the news-based measures become smaller and even slightly
negative.
Further, we compare our measure of monetary policy uncertainty with realized volatility
obtained from daily six-months-ahead federal funds futures data (see lower left panel of
Figure 12). Realized volatility spikes during the easing cycles in the early 1990s, 2000s,
and the post-crisis period, as well as during the tightening cycle in the mid-1990s. Realized
volatility remains historically low surrounding the lift-off of interest rates from the ZLB. As
is the case for the news-based measures, our measure has a higher positive correlation withrealized volatility during easing cycles. Conditional correlations practically vanish once wecompare realized volatility with our purged measure of monetary policy uncertainty.
Finally, we consider a measure of disagreement (lower right panel of Figure 12) definedas the difference between top-10-average and bottom-10-average forecasts. Disagreement
is high at the beginning of our sample and decreases continuously, strongly reflecting the
dynamics of the policy rate. For example, when the level of FFR is high, disagreement ishigh, too. Conditional correlations between disagreement and our monetary policy index
are negative and again shrink significantly once we consider the purged version of monetary18

policy uncertainty.
Intriguingly, as Table 4 shows, our measure of monetary policy uncertainty correlateswith the news-based measure and realized volatility, especially during periods of easing.
This is not surprising given our finding that levels of monetary policy uncertainty havestayed more or less stable over time in periods of monetary policy easing. However, thekey observation that monetary policy uncertainty has behaved asymmetrically over time inthe sense that it has decreased in periods of tightening is masked by these alternative measures. Further, given that the monetary policy uncertainty, once purged of macroeconomic
uncertainty, does not seem to correlate as much with the alternative measures (especiallyduring easing) suggests that the alternative measures might be capturing a sizeable part ofmacroeconomic uncertainty.
We rely on the historical forecast error distributions to capture monetary policy uncertainty. There are other potential alternatives in the literature. For instance, one could
use the conditional predictive distributions from the surveys. Most of the surveys do notprovide such information about the FFR. The ones that do, start later; see for reference
the Survey of Primary Dealers, conducted by the New York Fed, which starts from 2011,
making the survey mostly irrelevant for the analysis of conventional monetary policy.15
Another option would be to back out the option-implied-volatilities as suggested inWright (2017). The drawback of that approach, again, is that the sample will be shorterand the obtained measure, since it is a volatility measure, will be symmetric. A close
alternative would be to use the Merrill Lynch Option Volatility (MOVE) Index, which is
not derived from the FFR options, but instead from over-the-counter options on treasuries
maturing in two to 30 years.
Documenting and analyzing the skewness and the implied asymmetry is the main interestof the current paper. Recently, there has been interest in market measures such as SKEW,
which could be one potential avenue for further investigation.16 Given the fact that in order15

See https://www.newyorkfed.org/medialibrary/media/markets/survey/2018/jun-survey-pd.pdf.
The US Survey of Economic Forecasters provides density forecasts for inflation and output growth, but
not interest rates. Clements (2018) compares the conditional predictive densities of these variables withthe unconditional ones obtained based on the historical distribution of forecast errors, and finds the unconditional distributions to be more accurate. This gives us confidence that the distribution we are using is
informative and not necessarily inferior to the alternatives.
16
See
some
discussion,for
instance,athttps://www.blackrockblog.com/2017/09/08/
low-volatility-telling-us/.

19

to back out the implied skewness, we would need to make strong modeling assumptions, asis the case for implied volatility, we do not investigate this approach in this paper.

3.5

A Market-based Measure of Monetary Policy Uncertainty

In addition to survey forecasts, we also use market-based forecasts of the FFR to see whether
the observed characteristics of the BCFF policy rate forecasts remain. The contract priceof federal funds futures represents the market opinion of the average daily federal funds
effective rate as calculated and reported by the Federal Reserve Bank of New York for agiven calendar month. It is designed to capture the market’s need for an instrument thatreflects the Federal Reserve’s monetary policy. Federal funds futures and options have long
been regarded as an effective means of tracking market expectations of monetary actions of
the FOMC. Futures for the FFR started trading in the late 1980s (December 1988) but only
up to a six-months-ahead horizon. Meaningful trading volumes of up to twenty-four-monthsahead begin only in 2004 (up to thirty-six-months-ahead in 2011). We use six-months-ahead
federal funds futures as an alternative measure for expectations of future monetary policy.
One disadvantage of working with market-based expectations measures such as futures isthat they contain a risk premium, which is increasing with contract horizon. We follow
Kuttner (2001) and use the difference between the future price before and after FOMC
announcements dates to obtain a risk-premia-free measure.17
As described earlier, we use the surprises, that is, the daily changes in the futures’ pricearound FOMC announcements. Figure 13 plots the federal funds future surprise at the sixmonths-ahead horizon. Since the surprises are defined in a one-day window, their magnitudeis naturally smaller than the magnitude of the survey-based forecast errors. Nevertheless, asimilar pattern as before occurs; there are generally more negative surprises during easing
episodes and more positive surprises during tightening. As expected, surprises are small
during the ZLB period. Moreover, similar to the survey-based forecast errors, the lift-off
period seems to exhibit mainly negative surprises, an exception to the behavior observed
before the ZLB. Table 3 confirms this (see columns labeled as “Surprise”), as surprises are on
average negative during easing cycles and positive during tightening. Further, the average
17

The data on federal funds futures are obtained from Bloomberg, while the FOMC announcements dates
are obtained from the Federal Reserve’s website, see https://www.federalreserve.gov/monetarypolicy/
fomc_historical_year.htm.

20

surprise appears to be smaller in tightening episodes than in easing episodes. Recall thatthe same pattern can be observed with the survey-based forecast errors. Further, Figure
14 shows the left- and right-tail risk for the conditional distribution based on surprises at
the FOMC-meeting frequency. We observe asymmetric tail behavior over time (abstractingfrom the ZLB period). While the right tail somewhat tightens, the left tail actually expands.
Figure 15 plots the real-time uncertainty index obtained from federal funds futures
surprises at the six-month horizon. Note that the surprises are at the FOMC announcement frequency. Therefore, we calculate the uncertainty index at FOMC announcement
frequency. With futures starting in 1988:12, we use FOMC announcement observations
occurring between 1988:12 and 1993:06 to approximate the conditional distribution of surprises in the beginning of the sample period. To plot the index at a monthly frequency,
we assume that uncertainty is zero in months with no meeting. Using the market measureof FFR expectations, a similar pattern for the monetary policy uncertainty index arises,
although less pronounced. In particular, we can observe blue (light) bars mainly during
episodes of easing and red (dark) bars during episodes of tightening. Note, however, thatthe clustering is less clear than is the case for the uncertainty index based on the BCFF
survey forecasts. While the easing cycles of the early and late 2000s are mainly associatedwith upside uncertainty and the tightening cycle of the mid-2000s can be mostly related todownside uncertainty, the clustering along the monetary policy cycle is less clear throughout
the 1990s. As is the case with the survey-based uncertainty index, market-based uncertainty
is low during the ZLB and starts to rise again in late 2014. Similarly, the period surrounding
the lift-off of the FFR is mostly associated with upside uncertainty implying that the policy
rate was lower than expected, while the more recent hikes were accompanied by downside
uncertainty.
The relation between the monetary policy cycle and FFR expectations—that is, easing(tightening) can be associated with upside (downside) uncertainty–seems robust to themeasure of expectations (either survey- or market-based). Note also that the uncertainty
index based on the BCFF survey is an ex-post measure of uncertainty since it is basedon forecast errors. However, the uncertainty index based on the market-based surprise
does not relying on any ex-post realization since we consider the one-day-window forecast
revision (surprise) occurring around FOMC announcements. In that sense the surprises are21

revisions to expectations. It is interesting that though these two measures approach thequestion from different angles, they have comparable results.

4

Macroeconomic Effects of Monetary Policy Uncertainty

As discussed in the former sections, our uncertainty index for the FFR exhibits substantiallydifferent dynamics across the monetary policy cycle. This raises the question of whether thishas any implications for macroeconomic transmission of monetary policy uncertainty. In
what follows we assess whether there is non-linearity in the macroeconomic propagation ofmonetary policy uncertainty. To do so, we estimate a vector autoregression containing key
macroeconomic variables and the monetary policy uncertainty index (both the original andpurged versions) conditional on the monetary policy cycle. For the sake of transparency
and clarity, we simply split our sample according to easing and tightening subsamples.
An observation at time t falls into the tightening (easing) sample whenever the change inthe FFR is positive (negative) at time t − 1.18 We then study the dynamic responses toinnovations of monetary policy uncertainty in each subsample.19
The VAR is in the following variables:0yt = log(S&P 500)t , Ut∗ , ∆2 (log(W ages)t ), Hourst , log(Employment)t , log(IndP rod)t .
As standard in the literature, we identify the structural innovations of uncertainty using a
Cholesky scheme for each subsample (see, e.g., Bloom, 2009, and Jurado et al., 2015). Notethat though the uncertainty index was depicted and discussed as of the forecast origin dateearlier, in the VAR it enters as of the realization date, which mitigates any endogeneity
concerns.
Figure 16 shows the impulse responses to a one-unit monetary policy uncertainty shockusing the original index (based on six-months-ahead expectations) during easing (blue line)
and tightening periods (red dashed line). Dotted lines represent the 68% confidence intervals. Indeed, there seem to be differences in the impulse responses of macroeconomic
18
We use changes in the effective federal funds (as opposed to the federal funds target) rate here in orderto have sufficiently large subsample sizes. Note that this choice, if anything, makes it harder to identify
asymmetric effects since the association with monetary easing or tightening regimes becomes noisier.
19
If we had to analyze the effects of the upside and downside uncertainties separately, the VAR framework
would not be appropriate as the corresponding uncertainty indices would be capturing either left- or righttail developments of the forecast error distributions, which would make the symmetry assumption embeddedin the linear VAR framework inappropriate. A similar argument would apply to tail risk measures.

22

variables and stock prices. Industrial production, employment, hours, and the S&P 500
respond more to an uncertainty shock in periods of easing than they do in periods of tightening.
In particular, a monetary policy uncertainty shock decreases industrial production significantly, reaching its maximum effect of about 1.3 percentage points (pp) (annualized)
after a year during easing episodes. Employment also decreases significantly up to 0.6 pp(annualized) and stock prices fall by about 3.8 pp (annualized) following a monetary policy
uncertainty shock in the easing regime. However, a same-sized shock has no significant
effects on either macroeconomic variables or stock prices in the tightening subsample.
The contractionary effects of monetary policy uncertainty during easing could be simply
an artifact of easing episodes overlapping partly with recessions and, thus, periods of highmacroeconomic uncertainty. To shed light on whether this is the case, we replace the original
monetary policy uncertainty index with its purged version in the VAR. As explained earlier,the purged version controls the “pure” macroeconomic uncertainty by controlling for theuncertainty associated with macroeconomic fundamentals.
Figure 17 presents the corresponding impulse responses. In the case of the purged monetary policy uncertainty shock, the responses of stock prices, employment, and industrial
production remain insignificant in the tightening subsample. In the easing subsample, stock
prices, hours, employment, and industrial production decrease significantly following a monetary policy uncertainty shock when controlling for macroeconomic uncertainty, although
the negative responses are less pronounced relative to those of the original index. In particular, the peak effects on industrial production, employment, and stock prices of monetary
policy uncertainty get dampened to -0.4 pp, -0.3 pp, and -1.2 pp, respectively.
To see whether these results depend on the forecast horizon used to construct monetary
policy uncertainty, we also estimate the same (conditional) VARs for the uncertainty measures (both original and purged versions) based on the three-months-ahead, nine-monthsahead, and twelve-months-ahead forecast error distributions.
Figure 18 shows the impulse responses following a one-unit monetary policy uncertainty
shock (original version) based on nine-months-ahead expectations. Asymmetries in transmission across subsamples remain, and we observe similar responses during easing periods.
However, in the tightening sample, responses of macroeconomic variables and stock prices

23are now significantly negative. That is, a monetary policy uncertainty shock decreases employment by about 0.6 pp (annualized), industrial production by 0.7 pp (annualized), andstock prices by 3 pp (annualized) after about a year during times of tightening. Results
remain when we use the purged index based on the nine-months-ahead forecast errors.
Figures 20 and 21 show the responses (now without bands) for the original as well as
purged monetary policy uncertainty indices at all alternative horizons, and confirm thepatterns observed for the indices based on the six-months- and nine-months-ahead expectations. To sum up, first, monetary policy uncertainty has contractionary and significant
effects during easing regimes for all horizons. Effects get dampened when purged of uncertainty surrounding macroeconomic fundamentals, but remain significantly negative acrossall horizons. Also, effects of monetary policy uncertainty seem to be stronger when the index is constructed based on the longer horizon forecast error distributions.20 Second, monetary policy uncertainty has insignificant economic effects during tightening episodes whenshort-horizon-based expectations (three- to six-months-ahead) are used. However, once
we include uncertainty measures obtained from longer horizon forecasts (nine- to twelvemonths-ahead), we observe that economic effects become significantly negative; althoughstill lower when compared with those in easing. Purged versions of the uncertainty index
generate similar results.
To summarize: First, during easing episodes some negative macroeconomic effects may
be linked to the Fed’s conduct of monetary policy (such as communication) given thatmacroeconomic uncertainty is not the only source of recessionary macroeconomic effects.
This is also the case during tightening episodes at longer horizons. Second, as discussed in
Section 2.3, tightening episodes seem to be associated with clearer communication. Thus,
clearer communication in tightening, associated with better predictability of monetary policy decisions, could be linked to better anchored expectations, which in turn makes monetary
policy uncertainty less relevant for macroeconomic dynamics at the short horizons. However,expectations are less anchored at the longer horizons given that we observe contractionary
macroeconomic effects at those horizons during tightening, suggesting that monetary policy
communication might pin down shorter-term expectations better.
20
This observation partly vanishes once we use the purged version of the monetary policy uncertainty
indices.

24

5Further Considerations: Monetary Policy Uncertainty at
the ZLB

By construction, monetary policy uncertainty is historically low at the ZLB period since
we use up to twelve-months-ahead FFR forecasts.21 To shed some light on monetary policy
uncertainty related to unconventional policy measures such as quantitative easing (QE) andforward guidance at the ZLB, we construct uncertainty based on survey forecasts for the
10-year Treasury bond yield.
The literature has shown that these unconventional monetary policies affect interest
rates and, in particular, influenced the 10-year Treasury yield (see Gagnon et al., 2011;
Krishnamurthy and Vissing-Jorgensen, 2011; Neely, 2015; and Hamilton and Wu, 2012,
among many others). Thus, we use the 10-year Treasury bond yield to provide a measureof unconventional monetary policy uncertainty at the ZLB. Away from the ZLB, longer
maturities are usually beyond the direct control of the central banks and thus less indicativeof monetary policy uncertainty.
Figure 22 shows the uncertainty index obtained from the 10-year Treasury bond yield
forecast errors. For illustrative purposes, we present only the ZLB period. As is the case withthe uncertainty index for the FFR, upside and downside uncertainty occur in clusters. Once
the ZLB is binding, the uncertainty index for the 10-year Treasury yield peaks around QErelated announcements. We identify high levels of uncertainty when the Fed first announced
sterilized purchases of $600 billion in MBS, around the QE1 and QE2 announcement, aroundthe Operation Twist announcement, around the Bernanke testimony in May 2013, and at
the end of Fed’s large scale asset purchases. The uncertainty index takes on relatively
low values at the QE3 announcement and the start of Fed tapering. Interestingly, a clear
pattern of whether upside or downside uncertainty links to these key QE policy interventions
does not arise. For example, while the QE1 and QE2 announcements are associated withdownside uncertainty, Operation Twist is associated with upside uncertainty. We also
identify a period of high downside uncertainty at the Bernanke testimony, where the former
Fed chairman announced the possibility of tapering the Fed’s asset purchases in the future.
Uncertainty seems to be generally lower at forward-guidance–related announcements.
21

FFR expectations with horizons up to one to two years remained relatively flat following unconventionalmonetary policy announcements at the ZLB.

25

6

Conclusion

This paper characterizes monetary policy uncertainty and its impact on the macroeconomy.
We start from the premise that monetary policy is more uncertain whenever there is lesspredictability in the FFR, measured by the accuracy of the BCFF for the FFR. We also
consider a measure based on the “surprises,” i.e., revisions of the FFR future’s price on the
FOMC announcement dates.
We document asymmetries in both the forecast errors and the implied uncertainty index dynamics. Forecast errors are, in general, positive when the monetary policy tightens
and negative when the monetary policy is expansionary. Both forecast errors and uncertainty have been decreasing over time in the tightening episodes, suggesting an increasingly
better communication strategy on behalf of the Fed when implementing a contractionary
monetary policy. These dynamics are fairly robust and remain even when the monetary
policy uncertainty is “purged” of uncertainty surrounding macroeconomic conditions, suchas output growth and inflation.
In addition to the asymmetric behavior over the easing and tightening cycles, monetary
policy uncertainty also has non-linear propagation effects. It is, in general, recessionary, yet
the effects are stronger in easing relative to tightening. While the non-linearities remain,
effects become smaller when we consider monetary policy uncertainty purged of macroeconomic uncertainty. In addition, longer horizon uncertainty appears to have stronger effects
relative to shorter horizon uncertainty.
The results, overall, suggest that in tightening, the Fed not only well anchors the FFR
expectations, but also anchors the propagation of the monetary policy shocks reasonably
well, decreasing the macroeconomic relevance of monetary policy uncertainty. Our analysis
suggests that expectations can be better anchored in monetary easing cycles. There isalso room to further improve the propagation of the monetary policy uncertainty sincemonetary policy uncertainty appears to be recessionary in easings, thus counteracting the
expansionary effects of the monetary easing.

26

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29

Tables and Figures
Table 1: Sample Moments of BCFF Forecast Errors of the Federal Funds Rate
Conditional on the Monetary Policy Cycle
3-monthNr.
MeanMedianStd.
Skewness

Easing6-month
9-month

62
-0.41
-0.26
0.58
-1.73

62
-0.64
-0.51
0.87
-0.31

12-month3-month

62
-1.22
-1.56
1.45
0.50

69
0.13
0.22
0.53
-1.60

62
-0.93
-0.93
1.20
-0.01

Tightening
6-month
9-month
67
0.15
0.27
0.84
-2.13

12-month

67
0.17
0.33
1.10
-1.40

Notes: Descriptive statistics for the federal funds rate forecasts at the three-months-, sixmonths-, nine-months-, and twelve-months-ahead horizons. Observations at time t are associated with the easing (tightening) subsample whenever there was a negative (positive) change inthe federal funds target rate in the previous period t − 1.

Table 2: Unbiasedness and Rationality of BCFF Forecast Errors of the Federal
Funds Rate Conditional on the Monetary Policy Cycle
Overall
Wald-stats
p-value
3-month
6-month
9-month12-month

2.26
4.58
7.91
13.48

0.13
0.03
0.00
0.00

EasingWald-stats
p-value
Unbiasedness
19.57
0.00
16.47
0.00
17.16
0.00
17.34
0.00

Tightening
Wald-stats
p-value
2.78
1.11
0.61
0.60

0.10
0.29
0.43
0.44

Rationality
3-month
3.35
0.19
24.71
0.00
14.65
0.00
6-month
5.37
0.07
16.75
0.00
8.53
0.01
8.66
0.01
18.83
0.00
6.12
0.05
9-month12-month
14.20
0.00
20.00
0.00
6.27
0.04
Notes: Unbiasedness and rationality tests (joint test of unbiasedness and efficiency) for thefederal funds rate forecasts at the three-months-, six-months-, nine-months-, and twelve-monthsahead horizons. Observations at time t are associated with the easing (tightening) subsample
whenever there was a negative (positive) change in the federal funds target rate in the previous
period t − 1. Wald test is implemented based on a Newey and West (1987) covariance estimatorwith a truncation parameter of P 1/4 , where P is the effective sample size.

30

65
0.20
0.45
1.37
-1.06

Table 3: Sample Moments of Taylor-rule Residuals and Surprises Conditional on
the Monetary Policy Cycle.

EasingResiduals Surprise
Nr.
MeanMedianStd.
Skewness

54
-0.42
-0.24
0.68
-0.12

Tightening
Residuals Surprise

41
-0.05
-0.05
0.12
-0.45

55
0.33
0.21
0.46
0.42

41
0.02
0.02
0.06
0.71

Note: Taylor-rule residuals are the residuals obtained from a regression of federal funds forecast
errors (six-months-ahead) on GDP forecast errors (six-months-ahead) and price forecast errors(six-months-ahead). Surprises are the daily changes in federal funds futures at the six-monthsahead horizon around FOMC announcements dates.

Table 4: Correlations with Alternative IndicesRealized
Volatility

BBD
MPU 1

BBD
MPU 2

HRS
MPU

Disagreement

Original Index
Full Sample
EasingTightening

0.28
0.17
0.05

0.20
0.13
0.10

0.00
0.29
-0.08

0.14
0.13
-0.09

0.19
-0.30
-0.20

0.09
-0.05
0.12

0.20
-0.05
-0.05

Purged Index
Full Sample
EasingTightening

0.18
0.03
-0.02

0.10
-0.06
-0.06

0.01
0.08
0.00

31

Figure 1: BCFF Forecast Errors for the Federal Funds Rate
14.0

4.0
3.0

12.0

2.0

10.0

1.0
0.0

8.0

-1.0

6.0

-2.0

4.0

-3.0
2.0

-4.0

0.0
198304
198404
198504
198604
198704
198804
198904199004
199104
199204
199304
199404
199504
199604
199704
199804
199904200004
200104
200204
200304
200404
200504
200604200704
200804
200904
201004
201104
201204
201304
201404
201504
201604
201704
201804-5.0

3-months-ahead

6-months-ahead

12-months-ahead

FFR Target (rhs)

9-months-ahead

Note: This figure plots forecast errors based on the BCFF federal funds rate forecasts at thethree-months-, six-months-, nine-months-, and twelve-months-ahead horizons (left axis) andfederal funds target rate (right axis).

Figure 2: Forecast Error Distributions Over Time32

Figure 3: Tail Risks of Forecast Errors Over Time3-months-ahead

0.17

6-months-ahead

0.22
Right
Left

0.16

Right
Left

0.2

0.15
0.18

Probability

Probability

0.14
0.13
0.12

0.16

0.14

0.11
0.12
0.1
0.1

0.09
0.08

0.08
1990

1995

2000

2005

2010

2015

1990

9-months-ahead

0.21

2000

2005

2010

2015

12-months-ahead

0.2
Right
Left

0.2

Right
Left

0.19

0.19

0.18

0.18

0.17

0.17

Probability

Probability

1995

0.16
0.15

0.16
0.15
0.14

0.14

0.13

0.13

0.12

0.12
0.11

0.11
1990

1995

2000

2005

2010

2015

1990

1995

2000

2005

2010

2015

Note: This figure plots the probabilities associated with BCFF forecast errors one standard
deviation below (blue line) and above the mean (red line).

Figure 4: Illustrative Example for the Uncertainty Index
0.045

PDF of 6-months-ahead FFR Forecast Errors

1

0.04
0.8

0.035

Uncertainty Index based on 6-months-ahead FFR Forecast Errors

CDF of 6-months-ahead FFR Forecast Errors

1

2006:02
2008:08
Distribution - 2006:02
Distribution - 2008:08

U-t+h - 2006:02
U+t+h - 2008:08

0.9

0.03

0.02

0.6

0.8

0.4

0.7

0.2

0.6

F(x)

2006:02
2008:08
Distribution - 2006:02
Distribution - 2008:08

0.025

0.015
0.01
0.005
0
-4

-3

-2

-1

0

1

2

3

4

0
-4

-3

-2

-1

0

x

33

1

2

3

4

0.5
-4

-3

-2

-1

0

1

2

3

4

Figure 5: Monetary Policy Uncertainty Index
12.0

1

10.0

0.9

8.0

0.8

6.0

0.7

4.0

0.6

2.0

0.5

0.0
198807
198905199003
199101
199111
199209199307
199405199503
199601
199611
199709199807
199905200003200101
200111
200209200307200405200503
200601
200611200709200807
200905201003
201101
201111
201209201307
201405201503
201601
201611
201709

1.1

Downside uncertainty

Upside uncertainty

FFR Target (rhs)Note: This figure plots upside and downside uncertainty obtained from the six-months-ahead
federal funds rate forecast errors from the BCFF survey.

Figure 6: Output Growth Uncertainty Index
1.00

10.00

0.95
0.90

8.00

0.85
0.80

6.00

0.75
0.70

4.00

0.65
0.60

2.00

0.55
0.50
198912199010199108
199206
199304
199402
199412
199510199608199706199804
199902
199912200010200108200206200304
200402
200412
200510200608200706200804
200902
200912
201010201108201206201304
201402
201412
2015102016082017060.00

NBER Recessions

UpsideDownside

FFR Target (rhs)Note: This figure plots upside and downside uncertainty obtained from the six-months-ahead
BCFF GDP growth forecast errors. Shaded bars represent NBER recession dates.

34

Figure 7: Inflation Uncertainty Index
10.00

0.95

9.00

0.90

8.00

0.85

7.00

0.80

6.00

0.75

5.00

0.70

4.00

0.65

3.00

0.60

2.00

0.55

1.00

0.50

0.00
199010199108
199206
199304
199402
199412
199510199608199706199804
199902
199912200010200108200206200304
200402
200412
200510200608200706200804
200902
200912
201010201108201206201304
201402
201412
201510201608201706

1.00

NBER Recessions

UpsideDownside

FFR Target (rhs)Note: This figure plots upside and downside uncertainty obtained from the six-months-ahead
BCFF GDP deflator-based inflation forecast errors. Shaded bars represent NBER recession
dates.

Figure 8: Monetary Policy Uncertainty Index Purged of Macroeconomic Uncertainty
1.20

Correlation=0.73

1.10

10.00
9.00
8.00

1.00

7.00

0.90

6.00

0.80

5.00

0.70

4.00

0.60

3.00
2.00

0.40

1.00

0.30

0.00
199009199107
199205
199303
199401
199411
199509199607
199705199803
199901
199911200009200107
200205200303
200401
200411
200509200607200705
200803
200901
200911
201009201107
201205201303
201401
201411
201509201607
2017050.50

NBER recessions

Original Index

Purged Index

FFR Target (rhs)Note: This figure plots the purged uncertainty index: the normalized residuals of a regressionof the federal funds rate uncertainty index obtained from the six-months-ahead BCFF forecast
errors on the uncertainty indexes obtained from the six-months-ahead BCFF forecasts errors of
output and prices, respectively.

35

Figure 9: Taylor-rule Residuals: Purged Federal Funds Rate Forecast Errors
2.0

12.0

1.5

10.0

1.0
0.5

8.0

0.0

6.0

-0.5

4.0

-1.0
-1.5

2.0

-2.0

0.0
198601
198612
198711
198810198909199008199107
199206
199305199404
199503
199602
199701
199712
199811
199910200009200108200207
200306200405200504
200603200702
200801
200812
200911
201010201109201208
201307
201406201505
201604
201703-2.5

Original FFR errors (6-months-ahead)

Taylor-rule residuals

FFR Target (rhs)Note: This figure plots the Taylor-rule residuals obtained from a regression of federal funds rate
forecast errors (six-months-ahead) on output growth and inflation (six-months-ahead) forecast
errors.

Figure 10: Taylor-rule Residuals: Tail Risk Over Time

6-months-ahead

0.24

Right
Left

0.22

Probability

0.2

0.18

0.16

0.14

0.12
1995

2000

2005

2010

2015

Note: This figure plots the probabilities associated with the Taylor-rule residuals one standard
deviation below (blue line) and above the mean (red line).

36

450

400

350

300

250

200

150

100

50

0

BBD MPU (Access World News)BBD MPU (10 Major Papers)

8

Realized volatility (6-months-ahead FFR futures)
198901199002
199103
199204
199305199406199507
199608199709199810199911
200012
200201
200302
200403
200504
200605200706200807
200908201009201110201211
201312
201501
201602
201703

198901199002
199103
199204
199305199406199507
199608199709199810199911
200012
200201
200302
200403
200504
200605200706200807
200908201009201110201211
201312
201501
201602
201703

Downside uncertainty

12
12.00

10
10.00

6

6.00

4

4.00

2

2.00

0

0.00

198901199002
199103
199204
199305199406199507
199608199709199810199911
200012
200201
200302
200403
200504
200605200706200807
200908201009201110201211
201312
201501
201602198901199002
199103
199204
199305199406199507
199608199709199810199911
200012
200201
200302
200403
200504
200605200706200807
200908201009201110201211
201312
201501
201602
201703
201804

199011
199109199207
199305199403
199501
199511
199609199707199805199903200001
200011
200109200207
200305200403
200501
200511
200609200707
200805200903
201001
201011
201109201207
201305201403
201501
201511
201609201707

Figure 11: Monetary Policy Uncertainty Based on Taylor-rule Residuals
1.1
9

1
8

7

0.9
6

0.8
5

4

0.7
3

0.6
2

1

0.5
0

Upside uncertainty

BBD MPU
12.00

10.00

8.00

6.00

4.00
150

2.00
100

0.00

FFR Target (rhs)

Realized Volatility

8.00

FFR Target (rhs)37
FFR Target (rhs)Note: This figure plots the uncertainty index obtained from the Taylor-rule residuals.

Figure 12: Alternative Uncertainty Indices

400

HRS MPU

350
12.00

300
10.00

250
8.00

200
6.00

4.00

50

0
2.00

0.00

HRS MPUFFR Disagreement (6-months-ahead)
FFR Target (rhs)

2.00

Disagreement

1.80
12.00

1.60

1.40
10.00

1.20
8.00

1.00

0.80

6.00

0.60

0.40

4.00

0.20

0.00

2.00

0.00

FFR Target (rhs)Figure 13: Federal Funds Future Surprises
12

0.3
0.2

10

0.1
8

0

6

-0.1
-0.2

4

-0.3
2

-0.4

0
198812
198910199008199106
199204
199302
199312
199410199508199606199704
199802
199812
199910200008200106200204
200302
200312
200410200508200606200704
200802
200812
200910201008201106201204
201302
201312
201410201508
201606201704
201802-0.5

FFR target (rhs)

Future surprise (6-months-ahead)

Note: This figure plots the FFR future surprises at the 6-month horizon (daily changes of sixmonth future prices around FOMC announcement dates).

Figure 14: Federal Funds Future Surprises: Tail Risk Over Time

6-months-ahead

0.17

Right
Left

0.16
0.15

Probability

0.14
0.13
0.12
0.11
0.1
0.09
0.08
0.07
1995

2000

2005

2010

2015

Note: This figure plots the probabilities associated with the Taylor-rule residuals one standard
deviation below (blue line) and above the mean (red line).

38

Figure 15: Market-based Monetary Policy Uncertainty Index
1.1

8
7

1

6
0.9

5

0.8

4
3

0.7

2
0.6

1

0.5
199307
199403
199411
199507
199603
199611
199707199803
199811
199907200003
200011
200107
200203
200211
200307200403
200411
200507200603
200611200707
200803
200811
200907
201003
201011
201107
201203
201211
201307
201403
201411
201507
201603
201611
201707
2018030

Downside uncertainty

Upside uncertainty

FFR Target (rhs)Note: This figure plots upside and downside uncertainty obtained from the six-months-ahead
Federal funds future surprises (daily changes of six-month future prices around FOMC announcement dates).

39

Figure 16: Impulse Responses Conditional on the Monetary Policy Cycle: OriginalIndex

Monetary Policy Uncertainty (6-months)

S&P 500

1

1.2

0

Wages

0.1

Tightening
Easing

1

-1

0.8

0.05

-2
0.6
-3
0.4
-4

0

0.2

-5

0

-6
-7

-0.2
0

5

10

15

20

-0.05
0

5

Horizon

15

20

0

5

Horizon

Hours

10-3

5

10

Employment

0.2

15

20

Industrial Production

0.5

0
0

10

Horizon

0

-0.2

-0.5

-0.4
-5

-1
-0.6
-1.5

-0.8

-10

-2

-1
-15

-1.2
0

5

10

Horizon

15

20

-2.5
0

5

10

Horizon

15

20

0

5

10

15

20

HorizonNote: This figure plots the impulse responses of the S&P 500, monetary policy uncertainty (basedon six-months-ahead expectations), wages, hours, employment and industrial production to a
one-unit monetary policy uncertainty shock. Red dashed (blue solid) lines are the point estimatesobtained from the tightening (easing) subsample. Dotted lines represent the 68% confidence
bands.

40

Figure 17: Impulse Responses Conditional on Monetary Policy Cycle: Purged Index

S&P 500

2

Monetary Policy Uncertainty (6-months)
1.2

0.08

1

0.06

0.8

0.04

0.6

0.02

0.4

0

0.2

-0.02

0

-0.04

Wages
Tightening
Easing

1

0

-1

-2

-3

-0.2
0

10

20

-0.06
0

Horizon
10-3

6

10

0

20

Horizon

Hours

Employment

0.2
0.1

0.2

2

0

0

0

-0.1

-0.2

-2

-0.2

-0.4

-4

-0.3

-0.6

-0.4
0

10

Horizon20

20

Industrial Production

0.4

4

-6

10

Horizon

-0.8
0

10

Horizon

20

0

10

20

HorizonNote: This figure plots the impulse responses of the S&P 500, purged monetary policy uncertainty (based on six-months-ahead expectations), wages, hours, employment, and industrial
production to a one-unit monetary policy uncertainty shock. Red dashed (blue solid) lines are
the point estimates obtained from the tightening (easing) subsample. Dotted lines represent the
68% confidence bands.

41

Figure 18: Impulse Responses Conditional on the Monetary Policy Cycle: OriginalIndex (nine-months-ahead)

S&P 500

0

Monetary Policy Uncertainty (9-months)
1.2

0.1

Wages
Tightening
Easing

1
-2

0.05

0.8
-4

0.6
0
0.4

-6

0.2

-0.05

-8
0
-10

-0.2
0

10

20

-0.1
0

Horizon
10-3

5

10

20

0

Horizon

Hours

Employment

0.5

10

20

HorizonIndustrial Production

0.5
0

0

0

-0.5

-5

-1
-0.5
-1.5

-10

-2

-1

-15

-2.5
-20

-1.5
0

10

Horizon

20

-3
0

10

Horizon

20

0

10

20

HorizonNote: This figure plots the impulse responses of the S&P 500, monetary policy uncertainty (basedon nine-months-ahead expectations), wages, hours, employment, and industrial production to a
one-unit monetary policy uncertainty shock. Red dashed (blue solid) lines are the point estimatesobtained from the tightening (easing) subsample. Dotted lines represent the 68% confidence
bands.

42

Figure 19: Impulse Responses Conditional on Monetary Policy Cycle: Purged Index (nine-months-ahead)

S&P 500

1

Monetary Policy Uncertainty (9-months)
1.2

0.08

1

0.06

0.8

0.04

0.6

0.02

0.4

0

0.2

-0.02

0

-0.04

Wages
Tightening
Easing

0

-1

-2

-3

-4

-0.2
0

10

20

-0.06
0

5

Horizon
10-3

6

10

15

0

20

5

Horizon

Hours

Employment

0.1

4
2

10

15

20

HorizonIndustrial Production

0.4

0

0.2

-0.1

0

-0.2

-0.2

-0.3

-0.4

-0.4

-0.6

-0.5

-0.8

-0.6

-1

0
-2
-4
-6
-8

-0.7
0

10

Horizon

20

-1.2
0

5

10

Horizon

15

20

0

5

10

15

20

HorizonNote: This figure plots the impulse responses of the S&P 500, purged monetary policy uncertainty (based on six-months-ahead expectations), wages, hours, employment, and industrial
production to a one-unit monetary policy uncertainty shock. Red dashed (blue solid) lines are
the point estimates obtained from the tightening (easing) subsample. Dotted lines represent the
68% confidence bands.

43

Figure 20: Impulse Responses Conditional on Monetary Policy Cycle: OriginalIndex at Alternative Horizons
Monetary Policy Uncertainty

S&P 500

1.2

0.06

-1

1

0.04

-2

0.8

-3

0.6

-4

0.4

-5

0.2

0

Wages

0.02
0

-6

-0.02

0

-7

-0.2
0

5

10

15

20

-0.06
0

Horizon

10

0

20

Horizon

Hours

10-3

5

3-month
6-month
9-month12-month-0.04

Employment

0.4

10

20

HorizonIndustrial Production

0.5

0.2
0

0

0
-0.2

-0.5

-0.4

-1

-5

-0.6

-10

-1.5
-0.8

-15

-1
0

5

10

Horizon

15

20

-2
0

10

Horizon

20

0

10

20

HorizonNote: This figure plots the impulse responses of the S&P 500, monetary policy uncertainty,
wages, hours, employment, and industrial production to a one-unit monetary policy uncertainty
shock for several forecast horizons (three-, six-, nine-, and twelve-months-ahead). Dashed (solid)lines are the point estimates obtained from the tightening (easing) subsample.

44

Figure 21: Impulse Responses Conditional on Monetary Policy Cycle: Purged Index at Alternative Horizons

Monetary Policy Uncertainty

S&P 500

1.2

0.06

0

1

0.04

-0.5

0.8

-1

0.6

-1.5

0.4

-2

0.2

-2.5

0

0.5

Wages

0.02
0

-3

-0.02
-0.04

-0.2
0

5

10

15

20

-0.06
0

Horizon

10

20

0

Horizon

Hours

10-3

4

3-month
6-month
9-month12-month

Employment

0.1

2

0

0

-0.1

-2

-0.2

10

20

HorizonIndustrial Production

0.4
0.2
0
-0.2
-0.4

-4

-0.3

-6

-0.6

-0.4
0

5

10

Horizon

15

20

-0.8
0

10

Horizon

20

0

10

20

HorizonNote: Note: This figure plots the impulse responses of the S&P 500, purged monetary policy
uncertainty, wages, hours, employment, and industrial production to a one-unit monetary policy
uncertainty shock for several forecast horizons (three-, six-, nine-, and twelve-months-ahead).
Dashed (solid) lines are the point estimates obtained from the tightening (easing) subsample.

45

Figure 22: Monetary Policy Uncertainty Index at the ZLB"low to at "low to atleast 2013" least 2014""low forsome time"

1.1"low forextended
period"$600B inMBS

1.0

"low as long as
unemployment...

"low toat least
2015"

QE2

$45B/
month

1.1

End of purchases

1.0

QE3

QE1

Tapering
start"Be patient innormalizing
rates"

Bernanketestimony

Operation
Twist

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5
2008

2009

2010

2011

2012

Downside uncertainty
Forward-guidance–related announcements

2013

2014

2015

2016

Upside uncertainty
QE-related announcements

Note: This figure plots upside and downside uncertainty obtained from the six-months-ahead
10-year Treasury bond yield forecast errors from the BCFF survey over the ZLB period.

46

Appendix
This section presents additional figures for completeness and to provide further evidence onrobustness.
Figure 23: Conditional Empirical Distributions of BCFF Forecast Errors of theFederal Funds Rate
3-months-ahead

1.5

6-months-ahead

0.8
0.7
0.6

1
0.5
0.4
0.3
0.5
0.2
0.1
0
-4

-3

-2

-1

0

1

0
-4

2

9-months-ahead

0.5

-3

-2

0

1

2

3

12-months-ahead

0.4

0.45

-1

0.35

0.4
0.3
0.35
0.25

0.3
0.25

0.2

0.2

0.15

0.15
0.1
0.1
0.05

0.05
0
-6

-4

-2

0

2

0
-6

4

-4

-2

0

2

4

Notes: The blue (red) line shows the empirical distribution of federal funds rate forecast errors
at the six-months-, nine-months-, and twelve-months-ahead horizons for easing (tightening) subsamples. Observations at time t are associated with the easing (tightening) subsample whenever
there is a negative (positive) change in the federal funds target rate in the previous period t − 1.

47

Figure 24: Monetary Policy Uncertainty Index (based on three-months-ahead expectations)
12.0

1

10.0

0.9

8.0

0.8

6.0

0.7

4.0

0.6

2.0

0.5

0.0
198808
198906199004
199102
199112
199210199308199406199504
199602
199612
199710199808199906200004
200102
200112
200210200308200406200504
200602
200612
200710200808200906201004
201102
201112
201210201308
201406201504
201602
201612
201710

1.1

Downside uncertainty

Upside uncertainty

FFR Target (rhs)Note: This figure plots upside and downside uncertainty obtained from the three-months-ahead
federal funds rate forecast errors from the BCFF survey.

Figure 25: Monetary Policy Uncertainty Index (based on nine-months-ahead expectations)
12.0

1

10.0

0.9

8.0

0.8

6.0

0.7

4.0

0.6

2.0

0.5

0.0
198807
198905199003
199101
199111
199209199307
199405199503
199601
199611
199709199807
199905200003200101
200111
200209200307200405200503
200601
200611200709200807
200905201003
201101
201111
201209201307
201405201503
201601
201611

1.1

Downside uncertainty

Upside uncertainty

FFR Target (rhs)Note: This figure plots upside and downside uncertainty obtained from the nine-months-ahead
federal funds rate forecast errors from the BCFF survey.

48

Figure 26: Monetary Policy Uncertainty Index (based on twelve-months-ahead expectations)
12.0

1

10.0

0.9

8.0

0.8

6.0

0.7

4.0

0.6

2.0

0.5

0.0
198807
198905199003
199101
199111
199209199307
199405199503
199601
199611
199709199807
199905200003200101
200111
200209200307200405200503
200601
200611200709200807
200905201003
201101
201111
201209201307
201405201503
201601
201611

1.1

Downside uncertainty

Upside uncertainty

FFR Target (rhs)Note: This figure plots upside and downside uncertainty obtained from the twelve-months-ahead
federal funds rate forecast errors from the BCFF survey.

Figure 27: Monetary Policy Uncertainty Index Purged of Macroeconomic Uncertainty (based on three-months-ahead expectations)
1.20

Correlation=0.74

1.10

10.00
9.00
8.00

1.00

7.00

0.90

6.00

0.80

5.00

0.70

4.00

0.60

3.00
2.00

0.40

1.00

0.30

0.00
199011
199109199207
199305199403
199501
199511
199609199707199805199903200001
200011
200109200207
200305200403
200501
200511
200609200707
200805200903
201001
201011
201109201207
201305201403
201501
201511
2016092017070.50

NBER recessions

Original Index

Purged Index

FFR Target (rhs)Note: This figure plots the purged uncertainty index: the normalized residuals of a regressionof the federal funds rate uncertainty index obtained from the three-months-ahead BCFF forecast
errors on the uncertainty indexes obtained from the three-months-ahead BCFF forecasts errorsof output growth and inflation, respectively.

49

Figure 28: Monetary Policy Uncertainty Index Purged of Macroeconomic Uncertainty (based on nine-months-ahead expectations)
1.20

Correlation=0.70

1.10

10.00
9.00
8.00

1.00

7.00

0.90

6.00

0.80

5.00

0.70

4.00

0.60

3.00
2.00

0.40

1.00

0.30

0.00
199010199107
199204
199301
199310199407
199504
199601
199610199707199804
199901
199910200007
200104
200201
200210200307200404
200501
200510200607200704
200801
200810200907
201004
201101
201110201207
201304
201401
201410201507
201604
201701

0.50

NBER recessions

Original Index

Purged Index

FFR Target (rhs)Note: This figure plots the purged uncertainty index: the normalized residuals of a regressionof the federal funds rate uncertainty index obtained from the nine-months-ahead BCFF forecast
errors on the uncertainty indexes obtained from the nine-months-ahead BCFF forecasts errorsof output growth and inflation, respectively.

Figure 29: Monetary Policy Uncertainty Index Purged of Macroeconomic Uncertainty (based on twelve-months-ahead expectations)
1.20

Correlation=0.74

1.10

10.00
9.00
8.00

1.00

7.00

0.90

6.00

0.80

5.00

0.70

4.00

0.60

3.00
2.00

0.40

1.00

0.30

0.00
199009199106
199203
199212
199309199406199503
199512
199609199706199803
199812
199909200006200103
200112
200209200306200403
200412
200509200606200703200712
200809200906201003
201012
201109201206201303201312
201409201506201603
2016120.50

NBER recessions

Original Index

Purged Index

FFR Target (rhs)Note: This figure plots the purged uncertainty index: the normalized residuals of a regression of
the federal funds rate uncertainty index obtained from the twelve-months-ahead BCFF forecast
errors on the uncertainty indexes obtained from the twelve-months-ahead BCFF forecasts errorsof output growth and inflation, respectively.

50