#  International Spillovers of ECB Interest Rates
Monetary Policy & Information Effects

Santiago Camara111Contact email<EMAIL_ADDRESS>.
Northwestern University

###### Abstract

This paper shows that disregarding the information effects around the European
Central Bank monetary policy decision announcements biases its international
spillovers. Using data from 23 economies, both Emerging and Advanced, I show
that following an identification strategy that disentangles pure monetary
policy shocks from information effects lead to international spillovers on
industrial production, exchange rates and equity indexes which are between 2
to 3 times larger in magnitude than those arising from following the standard
high frequency identification strategy. This bias is driven by pure monetary
policy and information effects having intuitively opposite international
spillovers. Results are present for a battery of robustness checks: for a sub-
sample of “close” and “further away” countries, for both Emerging and Advanced
economies, using local projection techniques and for alternative methods that
control for “information effects”. I argue that this biases may have led a
previous literature to disregard or find little international spillovers of
ECB rates.

Keywords: ECB monetary policy; Information Effects; International Spillovers;
Emerging Markets; Advanced Economies.

JEL: F1, F4, G32.

This version: September 1, 2024. For the latest version Click here!

##  1 Introduction

High-frequency changes in interest rates around the Federal Reserve’s (Fed)
and the European Central Bank’s (ECB) policy announcements are a standard
method of measuring monetary policy shocks (standard HFI strategy). However,
there is a substantial literature which has found puzzling effects of these
shocks in domestic variables, such as GDP, inflation, and interest rates. This
evidence has been supportive of a “information effects” or an “information
channel” of monetary policy, whereby a Fed or ECB tightening may communicate
that the economy is stronger than the private sector expected. While there is
evidence that information effects may cause puzzling results when estimating
the international spillovers of the Fed’s interest rates, there is little to
no evidence on the role informational effects have on shaping the ECB’s
international spillovers. This paper shows that disregarding the information
effects around ECB policy announcements biases the estimates of the
international spillovers of ECB’s interest rates. Following an identification
strategy that disentangles pure monetary policy shocks from information
disclosure shocks lead to an impact in industrial production and nominal
exchange rates which is between 2 to 3 times larger in magnitude than
following the standard high frequency identification strategy. Furthermore,
disregarding the information effect would lead to the conclusion that an ECB
tightening causes an expansion of equity indexes abroad. This bias is driven
by pure monetary policy and information disclosure shocks having intuitively
opposite international spillovers.

I show this by estimating a panel SVAR model using an identification strategy
scheme that allows me to separate two ECB shocks: a pure monetary policy shock
(MP shock) and an information disclosure shock (ID shock). I disentangle these
two shocks using the methodology introduced by Jarociński and Karadi [2020]
which imposes sign restrictions on the co-movement of the high-frequency
surprises of interest rates and the stock market around ECB meetings. This co-
movement is informative as standard theory unambiguously predicts that a
monetary policy tightening shock should lead to lower stock market valuation.
This is because a pure monetary policy tightening decreases the present value
of future dividends by increasing the discount rate and by deteriorating
present and future firm’s profits and dividends. Thus, MP shocks are
identified as those innovations that produce a negative co-movement between
these high-frequency financial variables. On the contrary, innovations
generating a positive co-movement between the interest rates and the stock
market correspond to ID shocks. This is a shock that occurs systematically at
the time of the ECB policy announcements, but that is different from the
standard monetary policy shock. By separately identifying these two structural
shocks and introducing them into a panel SVAR, I find that MP shocks produce a
recession, an exchange rate depreciation and a drop in equity indexes. On the
contrary, ID shocks produce an economic expansion, an exchange rate
appreciation and an expansion of equity indexes.

I show that results are robust across model and sample specifications. First,
I find that the biases arising from disregarding the information effects of
ECB interest rates are greater for countries further away from the Euro Area.
This piece of evidence suggests that following the standard HFI strategy may
lead to the wrong conclusion that ECB interest rates have little to no
quantitative international spillovers. Second, I show results are present for
both Emerging and Advanced economies. Third, I show that results also arise
when estimating the impulse response functions using local projection
regressions. Lastly, I show that results also arise when using alternative
identification strategies that control for “information effects”.

Related literature. This paper relates to two main strands of literature.
First, this paper contributes to an increasingly larger literature which has
studied the role of information effects around the Fed’s and the ECB’s policy
meetings such as Romer and Romer [2000], Nakamura and Steinsson [2018] and
Jarociński et al. [2018], which argue that these central banks have
considerable better information about aggregate variables than
commercial/professional forecasters, and that policy actions provide
information signals that lead to private sector agents revising their beliefs
and expectations over aggregate dynamics. More recently, Jarociński and Karadi
[2020] introduces an identification strategy disentangles these monetary
policy and the information components of monetary policy by studying high-
frequency movements in interest rates and stock prices around policy
announcements. The authors show that pure monetary policy and information
disclosure shocks have intuitive and very different effects on the US economy.
Additionally, the authors argue that ignoring the central bank information
shocks biases the inference on monetary policy non-neutrality within the US
economy. The contribution of this paper to this literature is two fold. First,
providing evidence of significant information effects around ECB policy
announcements which cause substantial spillovers the rest of the world.
Second, this paper focuses on showing how disregarding the systematic
disclosure of information around policy meetings consistently biases the
international spillovers of ECB interest rates and may have lead previous
research to conclude the lack of any significant international
transmission.222For a discussion on the small or lack of international
transmission of ECB interest rates see Ca’Zorzi et al. [2020].

Additionally, this paper contributes to a long strand of literature which has
focused on quantifying the international spillovers of foreign interest rate
shocks into open economies, such as Eichenbaum and Evans [1995] and Uribe and
Yue [2006] for the Fed, and Von Hagen and Fratianni [1990] and Kobrak [2008]
for the ECB. More recently, Jarociński [2022] studies the impact of Fed and
ECB MP and ID shocks in the US and Euro Area economies, finding that while Fed
shocks have spillovers the Euro Area, ECB MP shocks do not have significant
effects in the US economy. Additionally, Miranda-Agrippino and Nenova [2022]
shows that “quantitative easing” shocks by the ECB have significant global
spillovers, though relatively smaller than those by the Fed. In this paper, I
show that while ECB MP shocks may not have a significant impact on the US
economy, both MP and ID shocks have quantitatively large impacts for both
Emerging and Advanced economies. Closely related to this paper, Camara [2021]
shows that following the standard HFI leads to puzzling international
spillovers, such as an economic expansion, an exchange rate appreciation and
equity index booms, which disappear once one controls for the information
disclosed around ECB meetings.333Also related to this paper, Camara and
Ramirez-Venegas [2022] shows that Fed and ECB shocks affect the Chilean
economy through different transmission channels by exploiting firm level
balance sheet data. The contribution of the this paper to this literature are
two-fold. To begin with, this paper shows that ECB interest rates have
quantitatively meaningful international spillovers in open economies,
highlighting the global role of the ECB and the Euro Area in financial and
international trade markets. Unlike Jarociński [2022] and Miranda-Agrippino
and Nenova [2022], this paper uses detailed data from a panel of 23 countries,
both Emerging Markets and Advanced Economies, instead of relying on US, Euro
Area or global variables.444It is noteworthy to mention that Miranda-Agrippino
and Nenova [2022] that estimates the impact of a “Path Factor” of ECB shocks
to measure the informational impact of ECB’s quantitative easing. In that
paper, the authors follow the identification strategy proposed by Swanson
[2021] which exploits different interest rate maturities such that the “Path
Factor” proxies for shocks that affect markets expectations of future medium-
term interest rates which combines the effects of forward guidance with those
of this “signalling channe” of the QE transmission mechanism. On the contrary,
in this paper I use the identification strategy proposed by Jarociński and
Karadi [2020] which exploits the co-movement of both interest rates and the
stock market to isolate a pure information effect. Secondly, this paper shows
that failing to take into account the information effects around ECB meetings
leads to downward-biased estimates of the impact of ECB interest rates into
the rest of the world. Thus, I argue that the biases introduced by information
effects may explain why a previous research found little to no international
spillovers of ECB interest rates.555For a discussion on the lack of
international spillovers of ECB interest rates see Ca’Zorzi et al. [2020].

##  2 Data, Methodology & Identification

In this section, I describe the construction of my dataset, describe the panel
SVAR methodology used and delineate the identification strategy used across
the paper.

Data. First, I describe the sample of Emerging Economies (EM) and Advanced
Economies (AE) countries, the different datasets used across the paper to
construct out sample of macroeconomic and financial variables, and the source
of the high-frequency surprises and ECB shocks. The benchmark specification
analyzes the international transmission of ECB interest rate spillovers on 23
countries, 16 Emerging Markets and 7 Advanced Economies at the monthly
frequency for the period January 2004 to December 2016. The Emerging Market
Economies are: Brazil, Bulgaria, Chile, Colombia, Hungary, India, Indonesia,
Malaysia, Mexico, Peru, Philippines, Poland, Russia, South Africa, Turkey,
Uruguay. The Advanced Economies are: Australia, Canada, Iceland, Japan,
Singapore, South Korea and Sweden.666 A big constraint in the construction of
a rich but balanced panel dataset is the lack of data availability for
Emerging Market economies in the late 1990s and early 2000s. It is noteworthy
to mention that during the late 1990s and early 2000s several EM economies
experienced significant monetary and fiscal policy changes (for instance the
implementation of inflation targetting regimes and fiscal policy rules). An
example of these policy changes is that around a third of emerging and
developing countries shifted from pro-cyclical to counter-cyclical fiscal
policies between the late 1990s to the early 2000s.

The benchmark model specification includes four macroeconomic and financial
variables: (i) nominal exchange rate with respect to the Euro, (ii) industrial
production index, (iii) CPI index, (iv) equity index.777In order to construct
a harmonized dataset of macroeconomic and financial variables for both AE and
EM economies, I source all of the datasets from the IMF, which guarantees that
the variables in the dataset are constructed following closely related
methodologies. Variables “industrial production”, “consumer price index”,
“nominal exchange rate with respect to the Euro”, “equity index” are sourced
from the IMF’s International Financial Statistics dataset. For additional
details on the construction of the dataset see Appendix A. I source the high
frequency surprises in interest rates and the two ECB shocks from the dataset
constructed by Jarociński [2022].,888Both the time series and the the
structural shocks and the replication codes to compute shocks can be directly
downloaded from the authors’ website. See https://marekjarocinski.github.io/.

Methodology. Next, I describe the panel SVAR model methodology estimated
across the paper. The model specification is a pooled panel SVAR as presented
by Canova and Ciccarelli [2013], following Camara [2021]. This type of model
considers the dynamics of several countries simultaneously, but assuming that
the dynamic coefficients are homogeneous across units, and coefficients are
time-invariant. In this framework, this implies that country $i$’s variables
only depend on structural shocks and the lagged values of country $i$’s
variables.

Identification strategy. The identification strategy combines the two
structural ECB shocks recovered by using the identification methodology
developed by Jarociński and Karadi [2020] and Jarociński [2022] with a
standard Choleski ordering identification strategy. The identification
strategy introduced by Jarociński and Karadi [2020] exploit the high-frequency
surprises of multiple financial instruments to recover two distinct ECB
shocks: a pure monetary policy (MP) shock and an information disclosure (ID)
shock. This deconstruction faces a typical set identification problem.999See
Jarociński and Karadi [2020] and Jarociński [2022] to see a detailed
discussion of this topic.. For my benchmark identification strategy I follow
Jarociński [2022] and use the shock deconstruction that arises from using the
median rotational angle that satisfies the sign restriction condition.101010In
Section 3.2 I show that the benchmark results are robust to using different
rotational angles. I order the vector of identified structural shocks first
with the vector of country $i$ specific macroeconomic and financial variables
second. I introduce the high-frequency surprise in interest rates and the two
ECB shocks one at a time and compute the resulting impulse response functions
for each shock separately. In Section 3.2 I show that an alternative method to
control for information effects around ECB meetings yields similar results.

##  3 Spillovers of Monetary Policy & Information Effects

This section presents the main results of this paper. In Section 3.1 I
estimate and quantify the impact of the two ECB shocks: a pure monetary policy
(MP) shock and an information disclosure (ID) shock; and following the
standard HFI. I show that the two ECB shocks have completely opposite
international spillovers. I argue that the presence of information disclosure
shocks biases the results arising from the standard HFI strategy. In Section
3.2 I show that my results are robust across different sub-samples, to
estimating the impulse response functions using local projection regressions,
and to alternative identification strategies that control for “information
effects” around ECB meetings.

###  3.1 Benchmark Results

I start by showing that deconstructing interest rate movements into the two
distinct structural shocks matter for quantifying the international spillovers
of ECB interest rates. The first and second columns of Figure 1 present the
impulse response functions of the macroeconomic and financial variables to a
MP and ID shock, respectively.

Figure 1: Impulse Response to One-Standard-Deviation Shock
Benchmark Specification

Note: The black solid line represents the median impulse response function.
The dark shaded area represents the 68 confidence intervals. The light shaded
area represents 95 confidence intervals. The figure is comprised of 12 sub-
figures ordered in four rows and three columns. Every row represents a
different variable: (i) nominal exchange rate, (ii) industrial production
index, (iii) consumer price index, (iv) equity index. The first column
presents the results for the MP or “Pure Monetary Policy” shock, the middle
column presents the results for the ID or “Information Disclosure” shock, and
the last column presents the results for the interest rate composite high
frequency surprise or “Standard HFI”. In the text, when referring to Panel
$(i,j)$, $i$ refers to the row and $j$ to the column of the figure.

Comparing the figures across these two columns leads to a first important
conclusion. This is that the identification scheme based on sign restriction
of the policy rates and the stock market financial separately identifies two
distinct economic shocks. If the co-movement between the high frequency
surprises of the policy interest rate and the stock market was uninformative,
the impulse response functions presented in the first and second columns of
Figure 1 should exhibit the same results. Comparing the results presented in
these figures, it is straightforward to conclude that this is not the case.
For instance, the behavior of the industrial production and equity indexes are
completely opposite across figures, with a MP shock leading to a persistent
decline in both indexes and a ID shock leading to a persistent increase in
them. Hence, correctly identifying the different EBC shocks is crucial to
accurately quantify the spillovers of ECB monetary policy shocks.

I now turn to describing the impulse response functions in greater detail. The
first column of Figure 1 shows the impulse responses to a MP shock. First,
Panel (1.1) shows that a one-standard-deviation MP shock leads to a 50 basis
point depreciation on impact of the euro nominal exchange rate, remaining
significantly above pre-shock levels for at least 35 months after the shock.
Panel (2,1) shows that a MP shock leads to hump shaped reduction in industrial
production. After a 25 basis point decrease on impact, industrial production
continues decreasing and reaches a trough 80 basis points below pre-shock
level 3 months after the initial shock. The decrease in industrial production
is persistent, remaining 50 basis points below pre-shock levels 35 months
after the initial shock. Panel (3,1) shows that a MP shock does not lead to a
significant response of the consumer price index. This lack of exchange rate
pass-through may be explained by the increasing evidence dollar currency
pricing, i.e., international trade invoiced in US dollars, which could reduce
the influence of euro exchange rates on domestic pricing. Lastly, Panel (4.1)
shows that a ECB MP shock leads to a hump shaped drop in the equity index,
close to 50 basis points on impact and reaching a trough 100 basis points
below pre-shock levels.

The international spillovers of an ECB ID shock are completely opposite to
those of a MP shock. The second column of Figure 1 presents the impulse
response functions of a one-standard-deviation ID shock. Panel (1.2) shows
that the nominal euro exchange rate shows an appreciation in response to an ID
shock. However, this appreciation is short-lived, only significantly different
from zero during the first 4 months after the initial shock. Panel (2.2) shows
that industrial production exhibits a mild yet persistent increase in response
to a ID shock. The increase becomes significantly different from zero 4 months
after the initial shock and remains 25 basis points above pre-level shocks for
35 months. Panel (3.2) shows that a ID shock leads to a reduction in the
consumer price index, however this is only significantly different from zero
when compared to a 68% confidence interval, but not compared to a 95%
confidence interval. Lastly, Panel (4.2) shows that a ID shocks leads to a on
impact jump in the equity index of 100 basis points. Additionally, the index
continues increasing and reaching a peak of 150 basis points above the pre-
shock level 2 months after the initial shock. In summary, the first and second
columns of Figure 1 show that the two EBC shocks lead to almost completely
opposite impulse response functions for the full sample of countries.

Next, I argue that following the “Standard HFI” strategy, i.e., using only the
high-frequency surprise of the policy interest rate composite, leads to biased
estimates of the ECB’s international spillovers. The third column of Figure 1
exhibits the impulse response functions under this identification strategy.
Across the different variables, the impulse responses are an average of the
responses presented for the MP and ID shocks in the first and middle columns.
Comparing the results for the nominal euro exchange rate, Panel (1.3) shows
that following the standard HFI approach would lead to the conclusion that an
ECB interest rate hike has no significant impact on nominal exchange rates. In
terms of impact on industrial production, following the standard HFI approach,
Panel (2.3), leads to, on average, a 40% smaller impact than when purging the
information effects, see Panel (2.1). For instance, following the MP shock
(Standard HFI shock) the drop in industrial production on impact is 21 basis
points (13 basis points), reaches a trough of 68 basis points (38 basis
points, and converges to a value 50 basis points (30 basis points) below pre
shock levels. Finally, the impact on the equity index, on the fourth row,
provides the sharpest qualitative difference. Following the standard HFI
approach, Panel (4.3), yield a mild expansion in the equity index. However,
once controlling for the information effects around policy announcements shows
that an ECB interest rate hike caused by a monetary policy shock, Panel (4.1)
leads to a significant and persistent drop in equity indexes.

Discussion. Overall, the results presented above show that disregarding the
systematic disclosure of information around policy announcements biases the
international spillovers of ECB interest rates. This is because an interest
rate hike caused by the disclosure of positive information about the state of
the Euro Area economy around ECB policy announcements, i.e. a ID shock, leads
to opposite international spillovers than those generated by a pure monetary
policy shock, i.e. MP shock. Thus, following the standard HFI approach could
lead to the erroneous conclusion that ECB interest rates do not have
significant international spillovers.

The fact that information effects around ECB policy announcements biases
international spillovers of its interest rates is line with what Camara [2021]
found for Fed interest rates. Yet, the biases introduced by information
effects are quantitatively smaller for the case of ECB than for Fed interest
rates.111111For instance, Ilzetzki and Jin [2021] and Camara [2021] show that
following the standard HFI approach lead to an expansion of industrial
production and looser financial conditions in the rest of the world. This is
expected as, for instance, Nakamura and Steinsson [2018] showed for the US
that following the standard HFI approach leads to an increase in real interest
rates, expected inflation, and expected output growth simultaneously, which
the authors attribute to changes in private sector beliefs about both monetary
policy but also about other economic fundamental variables. Additionally, this
relatively smaller role of information effects around ECB policy announcements
is in line with evidence presented by Jarociński [2022], which presented
evidence that information effects bias the degree of monetary non-neutrality
within the Euro Area, but do not change the sign of impact, as presented for
the US by Nakamura and Steinsson [2018].

###  3.2 Additional Results & Robustness Checks

In this section, I present additional results and robustness checks that
complement the benchmark results presented above. For convenience of
presentation, all figures are presented in Appendix C.

Results across different sub-samples. Given the large number of countries and
their different underlying characteristics, a natural robustness check
exercise is to test whether the results presented above hold across different
country sub-samples. I carry out two sub-sample exercises: (i) partitioning
the sample across the geographical distance into a sub-sample of “close” and a
sub-sample of “further-away” countries to the Euro Area; and (ii) Emerging vs
Advanced economies. The main results presented in Section 3.1 are robust
across all two sub-sample exercises. Additionally, I discuss any minor
quantitative differences across sub-samples.

Figure 2 in Appendix C presents impulse response functions estimated for a
sub-sample of economies geographically “close” to the Euro Area (Figure 2(a)),
and a sub-sample of economies “further-away” from the Euro Area (Figure
2(b)).121212The sub-sample of economies “close” to the Euro Area is: Bulgaria,
Hungary, Iceland, Poland, Russia, Sweden and Turkey. The sub-sample of
economies “further-away” from the Euro Area is: Australia, Brazil, Canada,
Chile, Colombia, India, Indonesia, Japan, Korea, Malaysia, Mexico, Peru,
Philippines, Singapore, South Africa, Uruguay. The first conclusion from this
exercise is that our benchmark results are present in both sub-samples, i.e.,
pure monetary policy and information disclosure shocks have qualitatively
different international spillovers and following the standard HFI approach
would predict significantly lower impact in the exchange rate, industrial
production and the equity index. Nevertheless, the impact in industrial
production of ECB shocks is quantitatively larger for the sub-sample of
countries relatively “closer” to the Euro Area than for the sub-sample of
countries “further-away”. This is intuitive as geographically closer economies
tend to trade relatively more with the Euro Area, use the euro relatively more
for invoicing and as a financial/savings instrument. Still, the potential
biases from disregarding the information effects around ECB meetings may be
particularly larger for the sub-sample of countries “further-away” from the
Euro Area. To see this, note that following the standard HFI approach leads to
a persistent increase in the equity markets in this sub-sample, which is
completely explained by the information disclosure shock.

Figure 3 in Appendix C presents the separately estimated impulse response
functions for Emerging Market (see Figure 3(a)) and Advanced Economies (see
Figure 3(b)). Once again, the main results are robust to this sub-sample
exercise, with only quantitative differences across sub-samples. First, the
biases or loses of disregarding information effects around ECB meetings is
significantly larger for Emerging Markets. To see this, note that following
the standard HFI predicts almost no impact at all in Emerging Markets’
industrial production, see Panel (2,3) in Figure 3(a). However, a MP shock
leads to a persistent drop in industrial production that is significantly
below pre-shock levels 36 months after the initial shock, see Panel (2,1) in
Figure 3(a). Note that for Advanced Economies, both the standard HFI and the
MP shock predict a persistent drop in industrial production, with the MP shock
impulse response function being two and three times greater in magnitude than
the one computed following the standard HFI approach. In consequence, once
again, disregarding the systematic disclosure of information around ECB
meetings may be the cause behind

Results using local projection regressions. Next, I show that the main results
that arise when estimating impulse response functions from a panel SVAR
results also arise when using local projection regressions. In particular, I
estimate the following regression given by Equation 1.

$\displaystyle
y_{i,t+h}=\beta^{Shock}_{h}i^{\text{Shock}}_{t}+\sum^{J_{y}}_{j=1}\delta^{j}_{i}y_{i,t-j}+\sum^{J_{x}}_{j=1}\alpha^{j}_{i}x_{i,t-j}+\sum^{J_{i}}_{j=1}\left(\phi^{j}_{i}i^{\text{Shock}}_{t-j}\right)+\Gamma_{i}+\Gamma_{t}+\epsilon_{i,t}$
(1)

where $y_{i,t+h}$ is the outcome variable of interest of country $i$ in period
$t+h$, $i^{\text{Shock}}_{t}$ is either the high-frequency surprise in the
interest rate, or one of the two ECB shocks; $y_{i,t-j}$, $x_{i,t-j}$ and
$i^{Shock}_{t-j}$ represent lagged valued of the variable of interest, other
macro-finance control variables and the specific shock, respectively;
$\Gamma_{i}$ represents a country fixed effect and $\Gamma_{t}$ represents a
linear trend. Parameter $\beta^{Shock}_{h}$ measures the impact that shock
$i^{\text{Shock}}_{t}$ has on variable $y_{i,t+h}$ in period $t+h$. Standard
errors are clustered at the “time” dimension given that the ECB shocks hit all
countries in our sample at the same time.

Figure 4 in Appendix C presents the estimated coefficients $\beta_{h}$ for the
full sample of countries for the two ECB shocks and when following the
Standard HFI approach. The local projection estimated impulse response
functions are in line with the benchmark results. On the one hand, the ECB
pure monetary policy leads to a depreciation of the domestic currency with
respect to the Euro which lasts slightly more than a year; and a persistent
drop in the industrial production and equity indexes. On the other hand, an
ECB information disclosure shock leads to a short-lived exchange rate
appreciation, a mild expansion in industrial production and a significant
increase in equity indexes. Additionally, when following the Standard HFI
approach, the estimated coefficients for the nominal exchange rate and the
industrial production index are, on average, 35% and 48% smaller in magnitude
than those estimated when using the ECB pure monetary policy shock. Moreover,
as it was the case in Section 3.1, following the Standard HFI approach
predicts an ECB interest hike predicts either a positive or no significant
impact in equity indexes, while a pure monetary policy shock predicts a
significant decline. These results are robust to removing the country fixed
effects (see Figure 5) and the time trend (see Figure 6). Additionally,
Figures 7 to 10 show that all the sub-sample exercises are also robust to
estimating impulse response functions using local projection techniques.

Results using alternative recovered ECB shocks. Sign restriction
identification strategies face a non-uniqueness problem, as mentioned in
Section 2. To provide robustness to our benchmark exercise, which uses the
median rotational angle which satisfies the sign-restrictions, I show all of
our results are robust to using three alternative rotational angles: the 68th
percentile angles (following Jarociński [2022] poor man strategy), and the
10th and 90th percentile angles which test upper and lower bounds. Figures 11
to 13 that the main benchmark results in Section 3.1, are robust to using
these alternative approaches to the non-uniqueness problem in sign
identification strategies.

Results using alternative controls for information effects. The literature on
the identification of monetary policy shocks have proposed several theories on
the “information effects” and different methods to control for
them.131313There is still no consensus on the literature about which is the
correct or appropriate method to control for these “information effects”. For
instance, Bauer and Swanson [2022] argues that what Jarociński and Karadi
[2020] label as “information disclosure” shocks can be explained by the “Fed
responding to News Channel”. However, Jarociński [2022] responds to this view
and presents additional evidence favoring the results presented by Jarociński
and Karadi [2020]. While this paper does not aim to contribute to this
discussion, I show that all the benchmark results presented in Section 3.1 are
still present when using an alternative identification strategy to that
proposed by Jarociński and Karadi [2020]. To do so, I follow Miranda-Agrippino
and Nenova [2022]’s approach on “poor man’s sign restriction” using the
identification strategy constructed by Swanson [2021].141414It is noteworthy
to point out that Miranda-Agrippino and Nenova [2022]’s main focus point are
ECB’s quantitative easing policy shocks and measuring their impact on measures
of global risk aversion. This approach implies the construction of shocks to
the ECB’s target policy rate for all policy meetings (which I label in Figures
as the Swanson [2021] method), for the set policy meetings in which there is a
negative co-movement of the policy rate and the stock market (which I label as
“cleansed monetary policy” shock), for the set of policy meetings in which the
co-movement is positive (which I label as “information content” shock).

Figure 14 in Appendix C presents the results of this robustness check.
Comparing the resulting impulse response functions lead to the same three
conclusions presented in the previous section: (i) the drop in industrial
production and equity indexes is, on average, 15% and 40% greater once
“information content” is cleansed; (ii) disregarding “information contents”
would predict no depreciation of the nominal exchange rate in response to an
ECB hike; (iii) “information content” shocks lead to intuitive opposite
responses than “cleansed monetary policy” shocks, such as an exchange rate
appreciation, and a mild expansion of industrial production and equity
indexes. Figure 15 show that these results are robust to estimating the
impulse response functions using local projection regressions. Even more,
estimating a “rate race” impulse response functions between the “cleansed” and
“uncleansed” shocks, by introducing them jointly into Equation 1, only lead to
intuitive and statistically significant dynamics for the “cleansed” one (see
Figure 16 in Appendix C). In summary, I interpret these results as further and
supporting evidence that “information effects” around policy meetings bias the
international spillovers of ECB interest rates.

##  4 Conclusion

This paper studies the international spillovers of ECB’s interest rates,
highlighting potential biases that arise from disregarding the systematic
disclosure of information around policy meetings. To do so, I use an
identification strategy that uses multiple asset high-frequency surprises
around ECB meetings to recover two distinct interest rate shocks, a pure
monetary policy shock and information disclosure shock. Introducing these
shocks into a panel SVAR model with data from 23 countries, these two interest
rate shocks have qualitatively and intuitively different international
spillovers. A pure monetary policy shock leads to an exchange rate
depreciation with respect to the Euro, and a persistent drop in industrial
production and equity indexes. Oppositely, an information disclosure shock
leads to an exchange rate appreciation and expansions of industrial
production. Lastly, I show that disregarding the information effects and
following the standard high-frequency identification strategy leads to
international spillovers that are between 35% and 65% smaller in magnitude.
This finding may explain why previous research found little to no
international spillovers of ECB interest rates.

Results are robust across a battery of tests, such as across several sub-
samples, both for SVAR and local projection regressions, and alternative
identification strategies that cleanse for “information effects” around policy
meetings. Going forward, it is crucial to further study whether Fed and ECB
interest rates’ international spillovers are transmitted differently or
through similar channels.

## References

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##  Appendix A Data Details

In this section of the appendix I provide additional details on the
construction of the sample used across the paper. The source of the
macroeconomic and financial data used for the construction of the variables in
the benchmark variable specification is the IMF’s “International Financial
Statistics”.151515To access the IMF’s IFS datasets go to
https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b.

First, the benchmark specification is comprised of five variables:

  1. 1.

Nominal Exchange Rate

  2. 2.

Industrial Production index

  3. 3.

Consumer Price Index

  4. 4.

Equity Index

Next, I present additional details for the construction of each of the
variables

  * •

Nominal Exchange Rate: The variable’s full name at the IMF IFS data set is
“Exchange Rates, National Currency Per U.S. Dollar, Period Average, Rate”.

  * •

Industrial Production Index: In order to construct countries’ “Industrial
Production Index” I rely on three variables of the IMF IFS’ dataset:

    * –

Economic Activity, Industrial Production, Index

    * –

Economic Activity, Industrial Production, Seasonally Adjusted, Index

    * –

Economic Activity, Industrial Production, Manufacturing, Index

Ideally, I would construct the variable “Industrial Production Index” by
choosing only one of the variables mentioned above. However, this is
impossible as countries do not report to the IMF all three of these variables
for our time sample, rey 2004 to December 2016. For instance, Peru provides
neither the “Economic Activity, Industrial Production, Index” nor the
“Economic Activity, Industrial Production, Seasonally Adjusted, Index”, but
does provide the “Economic Activity, Industrial Production, Manufacturing,
Index”. Visiting Peru’s Central Bank statistics website, there is no
“Industrial Production Index”, but there is an “Industrial Production,
Manufacturing Index”, which coincides with the variable reported as “Economic
Activity, Industrial Production, Manufacturing, Index” to the IMF.

In order to deal with this, I establish the following priority between the
three IMF IFS variables: (i) “Economic Activity, Industrial Production,
Seasonally Adjusted, Index” (ii) “Economic Activity, Industrial Production,
Index”, (iii) “Economic Activity, Industrial Production, Manufacturing,
Index”.

  * •

Consumer Price Index: Data for all countries except Australia is constructed
using the variable “Prices, Consumer Price Index, All items, Index” from IMF
IFS data set. Australia does not report a monthly CPI series to the IMF-IFS
data set. Furthermore, the Australian Bureau of Statistics provides only
quarterly data on their consumer price index.161616See
https://www.abs.gov.au/statistics/economy/price-indexes-and-
inflation/consumer-price-index-australia/jun-2022. Thus, for the case of
Australia I proxy the monthly consumer price index by using the “Prices,
Producer Price Index, All Commodities, Index”. Once again, given that the
paper’s main results are robust to the different exercises that partition the
sample, I believe that using this proxy variable does not guide any of the of
the results presented in the paper.

  * •

Equity Index: In order to construct countries’ “Equity Index” I rely on two
variables of the IMF IFS’ dataset:

    * –

Monetary and Financial Accounts, Financial Market Prices, Equities, Index

    * –

Monetary and Financial Accounts, Financial Market Prices, Equities, End of
Period, Index

I establish a priority: (i) “Monetary and Financial Accounts, Financial Market
Prices, Equities, Index” (ii) “Monetary and Financial Accounts, Financial
Market Prices, Equities, End of Period, Index”. Again, the data coverage is
not complete for all countries for the full sample period of January 2004 to
December 2016

##  Appendix B Model Details

In this section of the appendix I provide additional details on the panel SVAR
model and the identification strategy described in Section 2. Section B.1
presents details on the panel SVAR model while Section B.2 presents additional
technical details on the recovery of structural shocks.

###  B.1 Panel SVAR Model

In this section of the appendix I provide additional details on the estimation
of the Structural VAR model presented in Section 2. In its most general form,
a panel SVAR model comprises of N countries or units, $n$ endogenous
variables, $p$ lagged values and $T$ time periods. The pooled panel SVAR model
can be written as

$\displaystyle\begin{pmatrix}y_{1,t}\\\ y_{2,t}\\\ \vdots\\\
y_{N,t}\end{pmatrix}$ $\displaystyle=C+\begin{pmatrix}A^{1}\quad
0\quad\cdots\quad 0\\\ 0\quad A^{1}\quad\cdots\quad 0\\\
\vdots\quad\vdots\quad\ddots\quad\vdots\\\ 0\quad 0\quad\cdots\quad
A^{1}\end{pmatrix}\begin{pmatrix}y_{1,t-1}\\\ y_{2,t-1}\\\ \vdots\\\
y_{N,t-1}\end{pmatrix}+\cdots$ $\displaystyle+\begin{pmatrix}A^{p}\quad
0\quad\cdots\quad 0\\\ 0\quad A^{p}\quad\cdots\quad 0\\\
\vdots\quad\vdots\quad\ddots\quad\vdots\\\ 0\quad 0\quad\cdots\quad
A^{p}\end{pmatrix}\begin{pmatrix}y_{1,t-p}\\\ y_{2,t-p}\\\ \vdots\\\
y_{N,t-p}\end{pmatrix}+\begin{pmatrix}\epsilon_{1,t}\\\ \epsilon_{2,t}\\\
\vdots\\\ \epsilon_{N,t}\end{pmatrix}$

where $y_{i,t}$ denotes an $n\times 1$ vector of $n$ endogenous variables of
country $i$ at time $t$ and $A^{j}$ is an $n\times n$ matrix of coefficients
providing the response of country $i$ to the $j^{th}$ lag at period $t$. Note
that by assuming that $A^{j}_{1}=A^{j}_{n}=A^{j}$ for $j=1,\ldots,n$ implies
the assumption that the estimated coefficients are common across countries.
$C$ is a $Nn\times 1$ vector of constant terms which are also assumed to be
common across countries. Lastly, $\epsilon_{i,t}$ is an $n\times 1$ vector of
residuals for the variables of country $i$, such that

$\displaystyle\epsilon_{i,t}\sim\mathcal{N}\left(0,\Sigma_{ii,t}\right)$

with

$\displaystyle\epsilon_{ii,t}$
$\displaystyle=\mathbb{E}\left(\epsilon_{i,t}\epsilon_{i,t}^{\prime}\right)=\Sigma_{c}\quad\forall
i$ $\displaystyle\epsilon_{ij,t}$
$\displaystyle=\mathbb{E}\left(\epsilon_{i,t}\epsilon_{j,t}^{\prime}\right)=0\quad\text{for
}i\neq j$

The last two equations imply that, as for the model’s auto-regressive
coefficients, the innovation’s variance is equal across countries.

The model described by Equation B.1 is estimated using Bayesian methods. In
order to carry out the estimation of this model I first re-write the model. In
particular, the model can be reformulated in compact form as

$\displaystyle\underbrace{\begin{pmatrix}y_{1,t}^{\prime}\\\
y_{2,t}^{\prime}\\\ \vdots\\\ y_{N,t}^{\prime}\end{pmatrix}}_{Y_{t},\quad
N\times n}$ $\displaystyle=\underbrace{\begin{pmatrix}y_{1,t-1}^{\prime}\ldots
y_{1,t-p}^{\prime}\\\ y_{2,t-1}^{\prime}\ldots y_{2,t-p}^{\prime}\\\
\vdots\ddots\vdots\\\ y_{N,t-1}^{\prime}\ldots
y_{N,t-p}^{\prime}\end{pmatrix}}_{\mathcal{B},\quad N\times
np}\underbrace{\begin{pmatrix}\left(A^{1}\right)^{\prime}\\\
\left(A^{2}\right)^{\prime}\\\ \vdots\\\
\left(A^{N}\right)^{\prime}\end{pmatrix}}_{X_{t},\quad np\times
n}+\underbrace{\begin{pmatrix}\epsilon_{1,t}^{\prime}\\\
\epsilon_{2,t}^{\prime}\\\ \vdots\\\
\epsilon_{N,t}^{\prime}\end{pmatrix}}_{\mathcal{E}_{t},\quad N\times n}$ (3)

or

$\displaystyle Y_{t}=X_{t}\mathcal{B}+\mathcal{E}_{t}$ (4)

Even more, the model can be written in vectorised form by stacking over the
$T$ time periods

$\displaystyle\underbrace{vec\left(Y\right)}_{NnT\times
1}=\underbrace{\left(I_{n}\otimes X\right)}_{NnT\times
nnp}\quad\underbrace{vec\left(\mathcal{B}\right)}_{nnp\times
1}\quad+\quad\underbrace{vec\left(\mathcal{E}\right)}_{NnT\times 1}$ (5)

or

$\displaystyle y=\bar{X}\beta+\epsilon$ (6)

where $\epsilon\sim\mathcal{N}\left(0,\bar{\Sigma}\right)$, with
$\bar{\Sigma}=\Sigma_{c}\otimes I_{NT}$.

The model described above is just a conventional VAR model. Thus, the
traditional Normal-Wishart identification strategy is carried out to estimate
it. The likelihood function is given by

$\displaystyle
f\left(y|\bar{X}\right)\propto|\bar{\Sigma}|^{-\frac{1}{2}}\exp\left(-\frac{1}{2}\left(y-\bar{X}\beta\right)^{\prime}\bar{\Sigma}^{-1}\left(y-\bar{X}\beta\right)\right)$
(7)

As for the Normal-Wishart, the prior of $\beta$ is assumed to be multivariate
normal and the prior for $\Sigma_{c}$ is inverse Wishart. For further details,
see Dieppe et al. [2016]. All of the panel SVAR model computations are carried
out using the BEAR Toolbox version 5.1.

###  B.2 Identification Strategy & Shock Recovery

In this section of the appendix, I present additional details on the sign-
restriction identification strategy presented in Section 2 which recovers the
two ECB structural shocks. As stated in Section 2, this identification
strategy follows Jarociński and Karadi [2020] and Jarociński [2022].

The identification strategy introduced by Jarociński [2022] exploit the high-
frequency surprises of multiple financial instruments to recover two distinct
ECB shocks: a pure monetary policy (MP) shock and information disclosure (ID)
shock. In particular, the authors impose sign restrictions conditions on the
co-movement of the high-frequency surprises of interest rates and the Euro
Stoxx 50 around ECB meetings. This co-movement is informative as standard
theory unambiguously predicts that a monetary policy tightening shock should
lead to lower stock market valuation. This is because a monetary policy
tightening decreases the present value of future dividends by increasing the
discount rate and by deteriorating present and future firm’s profits and
dividends. Thus, MP shocks are identified as those innovations that produce a
negative co-movement between these high-frequency financial variables. On the
contrary, innovations generating a positive co-movement between interest rates
and the Euro Stoxx 50 correspond to ID shocks.

The high frequency surprise in the policy interest rate, $i^{Total}$, can be
decomposed as

$\displaystyle i^{\text{Total}}=i^{\text{MP}}+i^{\text{ID}}$ (8)

where $i^{\text{MP}}$ is negatively correlated with the high frequency
surprise of the $EuroStoxx50$ “$s$”, and $i^{\text{ID}}$ is positively
correlated with the “$s$”. As shown by Jarociński [2022], the sign restriction
recovery of the structural shocks must satisfy the following decomposition

$\displaystyle M=UC$ (9)

where $U^{\prime}U$ is a diagonal matrix, $C$ takes the form of

$\displaystyle C=\begin{pmatrix}1&c^{MP}<0\\\ 1&c^{ID}>0\\\ \end{pmatrix}$
(10)

where $M=(i^{Total},s)$ is a $T\times 2$ matrix with $i^{Total}$ in the first
column, $s$ in the second; $U=(i^{\text{MP}},i^{\text{ID}})$ is a $T\times 2$
matrix with $i^{\text{MP}}$ in the first column and $i^{\text{ID}}$ in the
second column; and $T$ denoting the time length of the sample. By
construction, $i^{\text{MP}}$ and $i^{\text{ID}}$ are mutually orthogonal.
Matrix $C$ captures how $i^{\text{MP}}$ and $i^{\text{ID}}$ translates into
financial market surprises.

The decomposition in 10 is not unique. In terms of Jarociński [2022] there is
a range of rotations of matrices $U$ and $C$ that satisfy the sign
restrictions $c^{MP}<0$ and $c^{ID}>0$.

The matrices $U$ and $C$ are computed as

$\displaystyle U$ $\displaystyle=QPD$ (11) $\displaystyle C$
$\displaystyle=D^{-1}P^{\prime}R$ (12)

where the matrices $Q,P,D,R$ are obtained in three steps.

  * 1.

Decompose matrix $M=UC$ into two orthogonal components using a QR
decomposition such that

$\displaystyle M$ $\displaystyle=QR$ (13) $\displaystyle Q^{\prime}Q$
$\displaystyle=\begin{pmatrix}1&0\\\ 0&1\\\ \end{pmatrix}$ (14) $\displaystyle
R$ $\displaystyle=\begin{pmatrix}r_{1,1}>0&r_{1,2}\\\ 0&r_{2,2}>0\\\
\end{pmatrix}$ (15)

  * 2.

Rotate these orthogonal components using the rotation matrix

$\displaystyle P$
$\displaystyle=\begin{pmatrix}\cos\left(\alpha\right)&\sin\left(\alpha\right)\\\
-\sin\left(\alpha\right)&\cos\left(\alpha\right)\end{pmatrix}$ (16)

To satisfy the sign restrictions use any angle $\alpha$ in the following range

$\displaystyle\alpha\in\left(\left(1-w\right)\times\arctan\frac{r_{1,2}}{r_{2,2}},\frac{w\times\pi}{2}\right)\qquad\text{if
}r_{1,2}>0$
$\displaystyle\alpha\in\left(0,w\times\arctan\frac{-r_{2,2}}{r_{1,2}}\right)\qquad\text{if
}r_{1,2}\leq 0$

where $w$ is weight, between 0 and 1, scaling the rotation angle. Setting
$w=0.5$ implies the median rotation angle, assumption used under the benchmark
specification

  * 3.

Re-scale the resulting orthogonal components with a diagonal matrix $D$ to
ensure that they add up to the interest rate surprises $i^{\text{Total}}$. It
is straightforward to show that

$\displaystyle D=\begin{pmatrix}r_{1,1}\cos\left(\alpha\right)&0\\\
0&r_{1,1}\sin\left(\alpha\right)\end{pmatrix}$ (17)

The first robustness check in Section 3.2 which proposes an alternative
approach to deal with the non-uniqueness problem of sign-restriction
identification follows the approach of Jarociński [2022]. In Jarociński
[2022], the angle of rotation $\alpha$ of matrix $P$ is pinned down following
these steps

  * 1.

Construct “poor man’s sign restrictions” shocks such that

$\displaystyle i^{\text{Total}}$ $\displaystyle=i^{MP}\quad\&\quad
i^{\text{ID}}=0\qquad\text{if }i^{Total}\times s\leq 0$ $\displaystyle
i^{\text{Total}}$ $\displaystyle=i^{ID}\quad\&\quad
i^{\text{MP}}=0\qquad\text{if }i^{Total}\times s>0$

In the data, Jarociński [2022] finds that the poor man’s monetary policy
shocks account for 68% of the variance of the ECB’s total interest rate
surprises, i.e,

$\displaystyle\frac{var\left(i^{\text{MP}}\right)}{var\left(i^{\text{Total}}\right)}=0.68$

To pin down the decomposition, Jarociński [2022] impose that, as in the “poor
man’s sign restrictions” case,
$var\left(i^{\text{MP}}\right)/var\left(i^{\text{MP}}\right)=0.68$. In Section
3.2 I carry out several robustness checks that considering different
rotational angles.

##  Appendix C Additional Results Figures

In this Appendix of the paper I present the figures referred to in Section 3.2
which refer to robustness checks and additional estimations which complement
the main results presented in Section 3.1.

Figure 2: Impulse Response to One-Standard-Deviation Shock
Separate Samples for Geographically Close & Further Away Countries

(a) Closer Economies

(b) Further Away Economies

Note: The black solid line represents the median impulse response function.
The dark shaded area represents the 16 and 84 percentiles. The light shaded
are represents the 5 and 95 percentiles. Figure 2(a) on the left presents the
results for the panel of “Close Economies” comprised of: Bulgaria, Hungary,
Iceland, Poland, Russia, Sweden, Turkey. Figure 2(b) presents the results for
the panel of “Further Away Economies” comprised of: Australia, Brazil, Canada,
Chile, Colombia, India, Indonesia, Japan, Korea, Malaysia, Mexico, Peru,
Philippines, Singapore, South Africa, Uruguay. Each figure is comprised of 12
sub-figures ordered in four rows and three columns. Every row represents a
different variable: (i) nominal exchange rate, (ii) industrial production
index, (iii) consumer price index, (iv) equity index. The first column
presents the results for the MP or “Pure Monetary Policy” shock, the middle
column presents the results for the ID or “Information Disclosure” shock, and
the last column presents the results for the interest rate composite high
frequency surprise or “Standard HFI”. In the text, when referring to Panel
$(i,j)$, $i$ refers to the row and $j$ to the column of the figure.

Figure 3: Impulse Response to One-Standard-Deviation Shock
Emerging vs Advanced Economies

(a) Emerging Economies

(b) Advanced Economies

Note: The black solid line represents the median impulse response function.
The dark shaded area represents the 16 and 84 percentiles. The light shaded
are represents the 5 and 95 percentiles. Figure 3(a) on the left presents the
results for the panel of “Emerging Economies” comprised of: Brazil, Bulgaria,
Chile, Colombia, Hungary, India, Indonesia, Malaysia, Mexico, Peru,
Philippines, Poland, Russia, South Africa, Turkey, Uruguay. Figure 3(b)
presents the results for the panel of “Advanced Economies” comprised of:
Australia, Canada, Iceland, Japan, Singapore, South Korea, Sweden. Each figure
is comprised of 12 sub-figures ordered in four rows and three columns. Every
row represents a different variable: (i) nominal exchange rate, (ii)
industrial production index, (iii) consumer price index, (iv) equity index.
The first column presents the results for the MP or “Pure Monetary Policy”
shock, the middle column presents the results for the ID or “Information
Disclosure” shock, and the last column presents the results for the interest
rate composite high frequency surprise or “Standard HFI”. In the text, when
referring to Panel $(i,j)$, $i$ refers to the row and $j$ to the column of the
figure.

Figure 4: Local Projection Regression - Parameter $\beta^{Shock}_{h}$
Full Sample

Note: The black solid line represents the point estimate of parameter
$\beta^{Shock}_{h}$ from estimating Equation 1. The dark shaded area
represents the 68 confidence intervals. The light shaded area represents 90
confidence intervals. The figure is comprised of 12 sub-figures ordered in
four rows and three columns. Every row represents a different variable: (i)
nominal exchange rate, (ii) industrial production index, (iii) consumer price
index, (iv) equity index. The first column presents the results for the MP or
“Pure Monetary Policy” shock, the middle column presents the results for the
ID or “Information Disclosure” shock, and the last column presents the results
for the interest rate composite high frequency surprise or “Standard HFI”. In
the text, when referring to Panel $(i,j)$, $i$ refers to the row and $j$ to
the column of the figure. Standard errors are clustered at the “time” or
“date” level.

Figure 5: Local Projection Regression - Parameter $\beta^{Shock}_{h}$
Full Sample - No Country Fixed Effects

Note: The black solid line represents the point estimate of parameter
$\beta^{Shock}_{h}$ from estimating Equation 1 without the country fixed-
effects $\Gamma_{i}$. The dark shaded area represents the 68 confidence
intervals. The light shaded area represents 90 confidence intervals. The
figure is comprised of 12 sub-figures ordered in four rows and three columns.
Every row represents a different variable: (i) nominal exchange rate, (ii)
industrial production index, (iii) consumer price index, (iv) equity index.
The first column presents the results for the MP or “Pure Monetary Policy”
shock, the middle column presents the results for the ID or “Information
Disclosure” shock, and the last column presents the results for the interest
rate composite high frequency surprise or “Standard HFI”. In the text, when
referring to Panel $(i,j)$, $i$ refers to the row and $j$ to the column of the
figure. Standard errors are clustered at the “time” or “date” level.

Figure 6: Local Projection Regression - Parameter $\beta^{Shock}_{h}$
Full Sample - No FE - No Time Trend

Note: The black solid line represents the point estimate of parameter
$\beta^{Shock}_{h}$ from estimating Equation 1 without the country fixed-
effects $\Gamma_{i}$ and the time trend $\Gamma_{i,t}$. The dark shaded area
represents the 68 confidence intervals. The light shaded area represents 90
confidence intervals. The figure is comprised of 12 sub-figures ordered in
four rows and three columns. Every row represents a different variable: (i)
nominal exchange rate, (ii) industrial production index, (iii) consumer price
index, (iv) equity index. The first column presents the results for the MP or
“Pure Monetary Policy” shock, the middle column presents the results for the
ID or “Information Disclosure” shock, and the last column presents the results
for the interest rate composite high frequency surprise or “Standard HFI”. In
the text, when referring to Panel $(i,j)$, $i$ refers to the row and $j$ to
the column of the figure. Standard errors are clustered at the “time” or
“date” level.

Figure 7: Local Projection Regression - Parameter $\beta^{Shock}_{h}$
“Close” Sub-Sample

Note: The black solid line represents the point estimate of parameter
$\beta^{Shock}_{h}$ from estimating Equation 1. The dark shaded area
represents the 68 confidence intervals. The light shaded area represents 90
confidence intervals. The figure is comprised of 12 sub-figures ordered in
four rows and three columns. Every row represents a different variable: (i)
nominal exchange rate, (ii) industrial production index, (iii) consumer price
index, (iv) equity index. The first column presents the results for the MP or
“Pure Monetary Policy” shock, the middle column presents the results for the
ID or “Information Disclosure” shock, and the last column presents the results
for the interest rate composite high frequency surprise or “Standard HFI”. In
the text, when referring to Panel $(i,j)$, $i$ refers to the row and $j$ to
the column of the figure. Standard errors are clustered at the “time” or
“date” level. This figure is computed for the sub-sample of “Close Economies”
comprised of: Bulgaria, Hungary, Iceland, Poland, Russia, Sweden, Turkey.

Figure 8: Local Projection Regression - Parameter $\beta^{Shock}_{h}$
“Further Away” Sub-Sample

Note: The black solid line represents the point estimate of parameter
$\beta^{Shock}_{h}$ from estimating Equation 1. The dark shaded area
represents the 68 confidence intervals. The light shaded area represents 90
confidence intervals. The figure is comprised of 12 sub-figures ordered in
four rows and three columns. Every row represents a different variable: (i)
nominal exchange rate, (ii) industrial production index, (iii) consumer price
index, (iv) equity index. The first column presents the results for the MP or
“Pure Monetary Policy” shock, the middle column presents the results for the
ID or “Information Disclosure” shock, and the last column presents the results
for the interest rate composite high frequency surprise or “Standard HFI”. In
the text, when referring to Panel $(i,j)$, $i$ refers to the row and $j$ to
the column of the figure. Standard errors are clustered at the “time” or
“date” level. This figure is computed for the sub-sample of “Further Away
Economies” comprised of: Australia, Brazil, Canada, Chile, Colombia, India,
Indonesia, Japan, Korea, Malaysia, Mexico, Peru, Philippines, Singapore, South
Africa, Uruguay.

Figure 9: Local Projection Regression - Parameter $\beta^{Shock}_{h}$
Emerging Economies Sub-Sample

Note: The black solid line represents the point estimate of parameter
$\beta^{Shock}_{h}$ from estimating Equation 1. The dark shaded area
represents the 68 confidence intervals. The light shaded area represents 90
confidence intervals. The figure is comprised of 12 sub-figures ordered in
four rows and three columns. Every row represents a different variable: (i)
nominal exchange rate, (ii) industrial production index, (iii) consumer price
index, (iv) equity index. The first column presents the results for the MP or
“Pure Monetary Policy” shock, the middle column presents the results for the
ID or “Information Disclosure” shock, and the last column presents the results
for the interest rate composite high frequency surprise or “Standard HFI”. In
the text, when referring to Panel $(i,j)$, $i$ refers to the row and $j$ to
the column of the figure. Standard errors are clustered at the “time” or
“date” level. This figure is computed for the sub-sample of “Emerging Market
Economies” comprised of: Brazil, Bulgaria, Chile, Colombia, Hungary, India,
Indonesia, Malaysia, Mexico, Peru, Philippines, Poland, Russia, South Africa,
Turkey, Uruguay.

Figure 10: Local Projection Regression - Parameter $\beta^{Shock}_{h}$
Advanced Economies Sub-Sample

Note: The black solid line represents the point estimate of parameter
$\beta^{Shock}_{h}$ from estimating Equation 1. The dark shaded area
represents the 68 confidence intervals. The light shaded area represents 90
confidence intervals. The figure is comprised of 12 sub-figures ordered in
four rows and three columns. Every row represents a different variable: (i)
nominal exchange rate, (ii) industrial production index, (iii) consumer price
index, (iv) equity index. The first column presents the results for the MP or
“Pure Monetary Policy” shock, the middle column presents the results for the
ID or “Information Disclosure” shock, and the last column presents the results
for the interest rate composite high frequency surprise or “Standard HFI”. In
the text, when referring to Panel $(i,j)$, $i$ refers to the row and $j$ to
the column of the figure. Standard errors are clustered at the “time” or
“date” level. This figure is computed for the sub-sample of “Emerging Market
Economies” comprised of: Australia, Canada, Iceland, Japan, Singapore, South
Korea, Sweden.

Figure 11: Impulse Response to One-Standard-Deviation Shock
Poor Man Rotational Angle

Note: The black solid line represents the median impulse response function.
The dark shaded area represents the 68 confidence intervals. The light shaded
area represents 95 confidence intervals. The figure is comprised of 12 sub-
figures ordered in four rows and three columns. Every row represents a
different variable: (i) nominal exchange rate, (ii) industrial production
index, (iii) consumer price index, (iv) equity index. The first column
presents the results for the MP or “Pure Monetary Policy” shock, the middle
column presents the results for the ID or “Information Disclosure” shock, and
the last column presents the results for the interest rate composite high
frequency surprise or “Standard HFI”. In the text, when referring to Panel
$(i,j)$, $i$ refers to the row and $j$ to the column of the figure.

Figure 12: Impulse Response to One-Standard-Deviation Shock
$10^{th}$ Percentile Rotational Angle

Note: The black solid line represents the median impulse response function.
The dark shaded area represents the 68 confidence intervals. The light shaded
area represents 95 confidence intervals. The figure is comprised of 12 sub-
figures ordered in four rows and three columns. Every row represents a
different variable: (i) nominal exchange rate, (ii) industrial production
index, (iii) consumer price index, (iv) equity index. The first column
presents the results for the MP or “Pure Monetary Policy” shock, the middle
column presents the results for the ID or “Information Disclosure” shock, and
the last column presents the results for the interest rate composite high
frequency surprise or “Standard HFI”. In the text, when referring to Panel
$(i,j)$, $i$ refers to the row and $j$ to the column of the figure.

Figure 13: Impulse Response to One-Standard-Deviation Shock
$10^{th}$ Percentile Rotational Angle

Note: The black solid line represents the median impulse response function.
The dark shaded area represents the 68 confidence intervals. The light shaded
area represents 95 confidence intervals. The figure is comprised of 12 sub-
figures ordered in four rows and three columns. Every row represents a
different variable: (i) nominal exchange rate, (ii) industrial production
index, (iii) consumer price index, (iv) equity index. The first column
presents the results for the MP or “Pure Monetary Policy” shock, the middle
column presents the results for the ID or “Information Disclosure” shock, and
the last column presents the results for the interest rate composite high
frequency surprise or “Standard HFI”. In the text, when referring to Panel
$(i,j)$, $i$ refers to the row and $j$ to the column of the figure.

Figure 14: Impulse Response to One-Standard-Deviation Shock
Alternative Identification Strategy

Note: The black solid line represents the median impulse response function.
The dark shaded area represents the 68 confidence intervals. The light shaded
area represents 95 confidence intervals. The figure is comprised of 12 sub-
figures ordered in four rows and three columns. Every row represents a
different variable: (i) nominal exchange rate, (ii) industrial production
index, (iii) consumer price index, (iv) equity index. The first column
presents the results for the “Cleansed Monetary Policy” shock, the middle
column presents the results for the ID or “Information Content” shock, and the
last column presents the results for the interest rate composite high
frequency surprise constructed by Swanson [2021]. In the text, when referring
to Panel $(i,j)$, $i$ refers to the row and $j$ to the column of the figure.

Figure 15: Local Projection Regression - Parameter $\beta^{Shock}_{h}$
Alt. Identification Strategy

Note: The black solid line represents the point estimate of parameter
$\beta^{Shock}_{h}$ from estimating Equation 1. The dark shaded area
represents the 68 confidence intervals. The light shaded area represents 90
confidence intervals. The figure is comprised of 12 sub-figures ordered in
four rows and three columns. Every row represents a different variable: (i)
nominal exchange rate, (ii) industrial production index, (iii) consumer price
index, (iv) equity index. The first column presents the results for the
“Cleansed Monetary Policy” shock, the middle column presents the results for
the ID or “Information Content” shock, and the last column presents the
results for the interest rate composite high frequency surprise constructed by
Swanson [2021]. In the text, when referring to Panel $(i,j)$, $i$ refers to
the row and $j$ to the column of the figure.

Figure 16: Local Projection Regression - Parameter $\beta^{Shock}_{h}$
Rate Race

Note: The black solid line represents the point estimate of parameter
$\beta^{Shock}_{h}$ from estimating Equation 1 with two shocks simultaneously.
The dark shaded area represents the 68 confidence intervals. The light shaded
area represents 90 confidence intervals. The figure is comprised of 4 sub-
figures ordered in two rows and two columns. Every row represents a different
variable: (i) industrial production index, (ii) equity index. The first column
presents the results for the “Cleansed Monetary Policy” shock, the second
column presents the results for the ID or “Uncleansed” or Swanson [2021]
shock. In the text, when referring to Panel $(i,j)$, $i$ refers to the row and
$j$ to the column of the figure.