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Monetary policy shocks in the euro area
and global liquidity spillovers
by João Sousa and Andrea Zaghini
Number 629 - June 2007
The purpose of the Temi di discussione series is to promote the circulation of working
papers prepared within the Bank of Italy or presented in Bank seminars by outside
economists with the aim of stimulating comments and suggestions.
The views expressed in the articles are those of the authors and do not involve the
responsibility of the Bank.
Editorial Board: D OMENICO J. M ARCHETTI , M ARCELLO B OFONDI , M ICHELE C AIVANO , S TEFANO
I EZZI , ANDREA LAMORGESE, FRANCESCA LOTTI, MARCELLO PERICOLI, MASSIMO SBRACIA, ALESSANDRO
SECCHI, PIETRO TOMMASINO.
Editorial Assistants: ROBERTO MARANO, ALESSANDRA PICCININI.
MONETARY POLICY SHOCKS IN THE EURO AREA AND
GLOBAL LIQUIDITY SPILLOVERS
by João Sousa* and Andrea Zaghini**
Abstract
We analyse the international transmission of monetary policy shocks with a focus on
the effects of foreign liquidity on the euro area. We estimate two domestic structural VAR
models for the euro area and then we introduce a global liquidity aggregate. The impulse
responses show that a positive shock to foreign liquidity leads for the euro area to a
permanent increase in M3 and in the price level, a temporary rise in real output and a
temporary appreciation of the euro real effective exchange rate. Moreover, we find that
innovations in global liquidity play an important role in explaining price and output
fluctuations.
JEL Classification: E52; F01.
Keywords: monetary policy, structural VAR, international spillovers.
Contents
1. Introduction.......................................................................................................................... 3
2. Two benchmark models for the euro area ........................................................................... 4
3. Global liquidity spillovers ................................................................................................. 12
4. Conclusion ......................................................................................................................... 19
Appendix ................................................................................................................................ 21
References .............................................................................................................................. 24
_______________________________________
* Banco de Portugal, Research Department
* Bank of Italy, Economic Research Department.
1
Introduction1
Recently, there has been an increasing interest in the sources of international
business ‡uctuations on the one hand, and in the role played by international
spillovers of monetary policy shocks on the other hand. Mounting evidence
suggest that the cross-country transmission of shocks plays an important
role in international business ‡uctuations, but so far only a limited number of studies have examined the role of shocks to monetary aggregates in
driving business ‡uctuations or, more generally, in in‡uencing the behaviour
of macroeconomic and …nancial variables in other countries.2 The weak performance of money demand models in many countries in terms of stability
and the generally low explanatory power of monetary models of exchange
rate determination partly explain this circumstance. This contrasts with a
recent research strand, which focuses on the role of money as an indicator of
macroeconomic development in closed-economy models (Trecroci and Vega;
2000, Amato and Swansson; 2001, Dotsey and Hornstein; 2003). This paper
provides an attempt to …ll this gap by studying the international transmission of monetary shocks with a special focus on the e¤ects of foreign money
(“global liquidity”) on the euro area economy.
There are several reasons why monetary developments abroad should
be taken into account by an open economy. Given the high level of integration attained in …nancial markets, cross-country capital ‡ows may have
non-negligible e¤ects on domestic asset prices or monetary aggregates. In a
…rst channel of transmission (the “push”channel), high monetary growth in
one area may lead to capital ‡ows into foreign countries, thus resulting in
stronger monetary growth and higher asset returns abroad; while according
to the “pull”channel, high domestic monetary growth may lead to domestic
asset price in‡ation and, as a result, attract foreign capital, thereby depressing asset prices in the countries where the capital ‡ows originated (Baks and
1
We are grateful to Alessandro Calza, José Luis Escrivá, Michael Ehrmann, Leonardo
Gambacorta, Hans-Joachim Klöckers, Livio Stracca and Larry D. Wall for useful comments and helpful discussions, as well as to participants to the XII International Tor
Vergata Conference on Banking and Finance and the XIX European Economic Association Congress. This paper does not necessarily re‡ect the views of Banco de Portugal and
Banca d’Italia.
2
After an early attempt by McKinnon (1982), only recently the literature has recorded
contributions in this …eld (Kim and Roubini; 2000, Kim; 2001, Holman and Neumann;
2002, Canova; 2005, Ehrmann and Fratzscher 2006).
3
Kramer; 1999). These e¤ects operate not only at times of stress in …nancial markets (as witnessed for instance by the quick spreading of the Asian
crisis in many countries of the South-East Asia and other emerging markets
economies in 1997), but also in “normal times”.
Furthermore, in the absence of capital ‡ows between regions, the very existence of common international exogenous shocks may lead to co-movements
of monetary aggregates in di¤erent countries. From a single country perspective, such co-movements can be exploited to reveal information about
the source of the shocks hitting the domestic economy. For instance, shocks
associated to international stock price volatility may lead to increases in both
domestic and foreign monetary aggregates due to a worldwide increased preference for liquid assets. In this case information on foreign monetary developments may help to con…rm that such liquidity preference shock was the
likely cause of the observed ‡uctuation in domestic monetary aggregates.
The aim of this paper is thus to study the international spillover e¤ects
on the euro area economy due to changes in foreign monetary aggregates.
We choose to do so within the context of structural vector autoregressions
(SVARs). We …rst propose two models that are taken as a benchmark for
the euro area using only domestic variables (i.e. prices, output, money, the
short-term interest rates and the exchange rate). From these models it is
possible to identify the “true” exogenous monetary policy shocks over the
period 1980-2001. Then, following a marginal approach (Kim; 2001), we add
to the block of endogenous variables a global liquidity aggregate (i.e. an
aggregation of broad monetary aggregates of major economies expressed in
the same currency) and analyse how euro area variables respond to shocks
to foreign money.
The paper is organised as follows: Section 2 proposes two benchmark
models for the euro area, Section 3 introduces a cumulative monetary aggregate and studies the impact on euro area macroeconomic variables of a
global liquidity shock, Section 5 concludes.
2
Two benchmark models for the euro area
In this section we propose two benchmark models to analyze the monetary
policy transmission mechanism within the euro area. We rely on the following
Structural VAR scheme:
4
Kut = et ;
(1)
where K is the matrix of the contemporaneous relations among variables, ut
is the vector of the reduced form residuals and et is the vector of the “true”
structural shocks.3 The SVAR methodology allows us to impose restrictions
only on the contemporaneous structural parameters, so that reasonable economic structures might be derived. The standard Cholesky decomposition
imposes a lower triangular K matrix, while a more general set of relations
might be introduced given that the same number of restrictions are maintained.
Thus we have to rely on a set of assumptions on the structural model of
the economy. In particular, the reaction function of the monetary authority
has to be speci…ed. This feedback rule explains the endogenous response
of the monetary authority to changes in a given set of variables and thus
relates policy-makers’ actions to the state of the economy. This in turn
implies making assumptions about which variables the monetary authority
looks at when setting its operational instrument. However, the basic idea
underlying the model is that not all changes in the central bank policy stance
re‡ect the systematic response to variations in the state of the economy:
the unaccounted alteration is formalized with the notion of monetary policy
shock. The most common interpretation of a policy shock is an exogenous
change in the preferences of the monetary authority, due, for instance, to a
shift in the relative weight given to in‡ation and unemployment.4
The …rst scheme (Model 1) we propose to identify monetary policy shocks
derives from Kim (1999). Kim’s model is an ideal starting point for euro
area aggregate analysis, since it is based on a common set of identifying
restrictions that worked well for the G7 countries. It shows the following
3
This speci…cation scheme is probably the most used in the monetary policy analysis:
see among others Gordon and Leeper (1994), Sims and Zha (1998), Leeper and Roush
(2003) for the US and Kim and Roubini (2000), Dedola and Lippi (2005), Mojon and
Peersman (2003) for other countries. For a comprehensive text-book reference see Amisano
and Giannini (1997).
4
See Christiano, Eichenbaum and Evans (1999) for possible alternative explanations.
5
non-recursive structure:
2
1
0
0
0
0
6 a21 1
0
0
0
6
6 a31 a32 1 a34 0
6
4 0
0 a43 1 a45
a51 a52 a53 a54 1
32
76
76
76
76
54
uYt R
uPt I
3
uM
t
SR
ut
uER
t
3
2
7 6
7 6
7=6
7 6
5 4
eYt R
ePt I
3
eM
t
SR
et
eER
t
3
7
7
7
7
5
(2)
where Y R is the real GDP, P I is the consumer price index, M 3 is the broad
monetary aggregate, SR is the short-term rate, which we assume the monetary authority can freely adjust, and ER is the real e¤ective exchange rate.
Both the reduced form and the structural residuals are assumed to follow a
standard normal distribution and have a zero mean and a constant variance.
The model is estimated by maximum likelihood.
The …rst two equations indicate that the real sector reacts sluggishly to
shocks in the …nancial variables. The general assumption is that GDP and
prices respond to …nancial signals (money, interest rate and exchange rate)
only with a lag. For instance, within the quarter …rms do not change their
output and prices in response to unexpected changes in …nancial variables
or monetary policy due to adjustment costs. The third equation is a money
demand function. The demand for money balances depends on real income,
the price index and the short-term interest rate, so that only the exchange
rate does not enter contemporaneously in the money demand equation. The
fourth relationship models the reaction function of the monetary authority,
which sets the interest rate after observing the current value of money and
the exchange rate. As in Sims and Zha (1998), the choice of this monetary
policy feedback rule is based on the assumption of information delays that
do not allow the monetary policy to respond within the same period to price
level and output developments. That is: published data on money and the
exchange rate are available within the period but reliable data on output and
prices are not. Finally, in the …fth equation the exchange rate, being an asset
price, reacts immediately to changes in all the other variables.
The second speci…cation (Model 2) is based on a recursive identi…cation
scheme based on the Cholesky decomposition:
6
2
6
6
6
6
4
1
0
0
0
a21 1
0
0
a31 a32 1
0
a41 a42 a43 1
a51 a52 a53 a54
0
0
0
0
1
32
76
76
76
76
54
uYt R
uPt I
3
uM
t
SR
ut
uER
t
3
2
7 6
7 6
7=6
7 6
5 4
eYt R
ePt I
3
eM
t
SR
et
eER
t
3
7
7
7:
7
5
(3)
The Cholesky scheme (3) implies, in particular, that monetary policy
shocks have no contemporaneous e¤ect not only on output and prices as in
model (2), but also on money. They a¤ect the exchange rate within the same
quarter, but the policy interest rate does not respond to contemporaneous
changes in the exchange rate.5
Estimations are based on quarterly data for the euro area from 1980 Q1
to 2001 Q4 (see the data annex for the details about de…nitions and sources).
Data are expressed in logarithmic form and are seasonally adjusted, except
the interest rates which are in levels. A constant and a linear trend are added
to both models. Standard information tests suggest to adopt a 4-lag length
for both VARs. As in the reference studies, in this paper we do not perform
an explicit analysis of the long run behaviour of the economy. Nevertheless,
the speci…cation in levels allows for implicit cointegrating relationships in
the data (Sims, 1990), i.e. we are implicitly assuming that the variables are
jointly covariance stationary.6
Figures (1) and (2) display the estimated impulse responses to an unexpected temporary monetary policy shock in both models.7 A 1-time standard
deviation increase in the short-term rates is followed by a real appreciation
of the exchange rate and a temporary fall in the real GDP. The e¤ect on
output reaches the peak after 4 to 6 quarters and returns to baseline afterwards. Prices respond much more sluggishly, and the e¤ect of the shock is
only signi…cant in the case of Model 2. Within the …rst year the impact on
5
The Cholesky approach has been followed by Peersman and Smets (2003) in their
analysis of euro area monetary transmission mechanism. The main di¤erence with the
VAR model used in this study is that they include also a vector of exogenous variables
containing a commodities price index and the real GDP and short-term nominal interest
rate of the US.
6
In fact, the examination of the residuals of the VARs reveals no evidence of nonstationarities. The same applies for the other VAR models including global liquidity used
in this study. For an explicit modelling of the euro area money demand in the long-run
see Coenen and Vega (2001) and Calza et al. (2001).
7
The con…dence bands are obtained through a standard bootstrap procedure with 1000
draws.
7
Figure 1: Impulse responses from Model 1 (including 90% con…dence bands)
8
Figure 2: Impulse responses from Model 2 (including 90% con…dence bands)
M3 is negative, even though it becomes signi…cant only from the end of the
second/beginning of the third year.
A typical monetary policy shock is 30 basis points in both models. The
maximum impact of the shock on GDP is just above 0.2%, slightly larger than
in Peersman and Smets (2003), which estimated a drop in GDP of 0.15%, but
smaller than in Monticelli and Tristani (1999), for which the decline was 0.4%.
All in all, the estimated responses are very close to expected movements of
macro-variables in a monetary policy tightening setting. Thus, the results
support the validity of the identifying assumptions for both models.
9
Table 1. Forecast error variance decomposition
1 year
Model 1
2 year 3 year
4 year
1 year
Model 2
2 year 3 year
4 year
Variability of M3
YR
PI
M3
SR
ER
9.5
0.2
89.7
0.5
0.1
33.6
1.9
62.6
1.3
0.5
53.1
4.4
37.5
3.3
1.7
57.7
4.6
30.0
4.6
3.2
9.5
0.2
89.8
0.2
0.3
33.6
1.9
62.8
0.5
1.1
53.1
4.5
37.3
4.5
0.7
57.6
4.6
29.6
7.4
0.8
2.7
75.5
16.0
0.4
5.4
10.7
62.9
22.6
0.4
3.4
23.4
48.9
23.8
1.4
2.4
76.1
2.2
6.0
15.1
0.5
74.3
2.0
6.3
14.3
3.1
73.3
1.9
5.9
14.4
4.4
Variability of prices
YR
PI
M3
SR
ER
0.2
86.0
6.3
0.4
7.1
2.7
75.5
16.0
0.8
5.0
10.7
62.9
22.6
0.6
3.1
23.5
48.8
24.0
0.9
2.7
0.2
86.0
6.4
0.9
6.5
Variability of real GDP
YR
PI
M3
SR
ER
88.0
3.2
2.1
5.5
1.3
76.2
2.1
6.6
11.0
4.0
74.4
1.9
6.8
9.6
7.2
73.4
1.9
6.4
9.2
9.1
87.9
3.2
1.9
6.5
0.5
The forecast error variance decompositions of the monetary aggregate,
prices and real GDP are reported in Table 1. Focusing on the e¤ects of a
shock to the short-term interest rate (the policy instrument) we can see that,
as in most of the VAR literature, the contribution to the volatility of money,
prices and output is rather limited.8 For instance, reading by row the lower
panel, Table 1 reports that for both models the contribution of an innovation
in interest rates to output ‡uctuation is at most 15% at any horizon. This
result is close to that reported by Peersman and Smets (2003) and consistent
with the …ndings of Kim (1999) for single G7 countries. Almost negligible
8
See Canova and De Nicoló (2002) for the opposite result.
10
Figure 3: Historical decomposition of SR from Model 1 (including 1-time
standard deviation con…dence band)
is the contribution to price dynamics both in the short- and long-run (see
middle panel). As for the impact of a shock to M3, its e¤ect on prices is
somehow stronger: after 4 years the relative contribution to price ‡uctuation
is 24% for both models (see upper panel).
Figures (3) and (4) depict the historical contribution of the monetary
policy shocks to the short-term interest rate in the euro area as identi…ed by
the two models. Even though the magnitude of the swings are sometimes
di¤erent, the overall picture provided by the recursive and the structural
approach is indeed similar.9
Over the period from 1981 to 2001 the two models signal contemporaneously a “tight” stance of the euro area monetary policy on three occasions.
In fact, the contribution of monetary policy is above one time the standard
deviation for at least two consecutive quarters in the episode of 1987, 1989-90
and 1992-93.10 There are as well four episodes of “easy” monetary policy:
9
The correlation coe¢ cient between the two series is 0.89; the standard deviation is
slightly larger for Model 2: 0.59 versus 0.56.
10
Only the recursive approach signals a breaching of the 1-time standard deviation
threshold in 1983 Q3-Q4 and in 1998 Q1-Q2.
11
Figure 4: Historical decomposition of SR from Model 2 (including 1-time
standard deviation con…dence band)
1984-85, 1991, 1993-94 and 1999.11 Again, the …nding is consistent with the
results from Peersman and Smets (2003): they report positive and negative
contribution to the short-term rates in the same periods as in this study even
though the oscillations seems to be less pronounced in the second half of the
1990s.
3
Global liquidity spillovers
In order to investigate the possible e¤ect of foreign liquidity on euro area variables, we follow the “marginal”approach (Kim; 2001) and introduce a sixth
variable taking into account the development of extra-euro area monetary
aggregates in both benchmark speci…cations of the previous section. This
exercise, to our knowledge, is new in the empirical literature on monetary
policy.12
11
In this case the non-recursive approach signals an additional episode in 1988 Q1-Q2.
Instead, the use of cross-country aggregated data in the econometric analysis of international spillovers is not new in the literature. For recent applications see Kwark (1999),
Kim (2001) and Lumsdaine and Prasad (2003).
12
12
The variable used (GL4Y ) is a measure of liquidity outside the euro
area corrected for the e¤ect of foreign output (assuming therefore a unit
elasticity of money demand with respect to real output in these countries).
It is obtained by subtracting the logarithm of real GDP of the remaining four
G7 non-euro area countries (US, Japan, UK and Canada) from the logarithm
of the weighted sum of monetary aggregates of the same countries (GL4 ).
In particular, the global liquidity aggregate is constructed as the simple sum
of the reference monetary aggregates using exchange rates vis-à-vis the euro
based on purchasing power parities to convert them into a common currency
(see the data annex for further details).
The choice of the marginal approach instead of a full VAR including
other relevant foreign variables (foreign output, interest rates and prices) was
dictated by the relatively small size of the sample used (84 observations).13 .
By using money per output, we assume that only the part of global liquidity
not linked to foreign output can potentially generate spillover e¤ects on the
euro area.
We order the extra variable GL4Y in the models of the previous section
as the most exogenous variable in the system. Under this assumption, we
are implicitly assuming that developments in the euro area do not have a
contemporaneous e¤ect on global monetary developments but only a delayed
one.14
13
See Sousa and Zaghini (2007) for a SVAR model based on aggregate G5 data including
the euro area.
14
The choice of including global liquidity in the euro area benchmark VARs as a fully
exogenous variable could also be considered. However, the results of exclusion tests in the
extended six-variable VAR show that it is possible to reject the null that global liquidity
is exogenous to euro area variables. In addition, it is also possible to reject the hypothesis
that the euro area block is exogenous to global liquidity. Therefore, we have opted to keep
global liquidity endogenous. On the other hand, we have added a total commodities cost
variable as an exogenous variable, to take account of movements in global commodities
prices. The inclusion of this variable therefore controls for a further source of external
shocks that may distort the link between global liquidity and in‡ation and output in the
euro area.
13
When Model 1 is used, the identi…cation scheme (Model 1a)
2
3 2 GL4Y 3 2 GL4Y
1
0
0
0
0
0
ut
et
6 a21 1
7 6 uYt R 7 6 eYt R
0
0
0
0
6
7 6 PI 7 6 PI
6 a31 a32 1
6 t
7 6 et
0
0
0 7
6
7 6 uM
7 6
3
6 a41 a42 a43 1 a45 0 7 6 ut 3 7 = 6 eM
6
7 6 SR 7 6 tSR
4 a51 0
5 4 et
0 a54 1 a56 5 4 ut
ER
a61 a62 a63 a64 a65 1
ut
eER
t
becomes:
3
7
7
7
7:
7
7
5
(4)
The plot of the impulse responses is shown in Figure 5. A positive shock
to global liquidity results in a permanent rise in the levels of both euro area
M3 and prices. As regards the e¤ect on real GDP, there is a temporary
upward e¤ect of a positive shock to global liquidity on the output level of the
euro area, with GDP returning to baseline after a period of about …ve years.
Therefore, shocks to global liquidity seem to have only nominal e¤ects in the
long-run.
Also for the identi…cations based on the Cholesky decomposition, we introduce GL4Y as the most exogenous variable in the system, following the
chain GL4Y ! Y R ! P I ! M 3 ! SR ! ER (Model 2a). The impulse
response functions are shown in Figure 6. The dynamics are indeed similar
to those from Model 1a: a positive shock to global liquidity leads to a signi…cant rise in euro area M3 and to an upward e¤ect on prices, suggesting
that there is a transmission from global monetary developments to the euro
area over time. In particular, these developments point to a positive spillover
e¤ect into the euro area as predicted by the “push”channel. An unexpected
increase in money abroad gives rise to capital ‡ows into the euro area in the
mid-term determining an upward pressure on M3, which in turn leads to an
increasing price pressure.
As regards output, an exogenous increase in global liquidity leads to a
signi…cant upward e¤ect on euro area output after two quarters. The e¤ect
peaks at around two years and then declines becoming insigni…cant in the
longer-run. The short-term interest rate does not appear to react much
in the short-run but it rises signi…cantly after a period of about one year.
One possible interpretation is that the upward movement of the interest
rate re‡ects a monetary policy reaction to the increase in the price index
associated to the positive spillover of global liquidity. Finally, a positive
shock to global liquidity leads to a temporary upward e¤ect on the euro
exchange rate.15
15
As a robustness check we looked at the impulse responses when global liquidity is
14
Figure 5: Impulse responses from Model 1a (including 90% con…dence bands)
15
Figure 6: Impulse responses from Model 2a (including 90% con…dence bands)
16
Overall, these …ndings are consistent with the existence of a push channel,
through which high monetary growth abroad determines an increase in the
demand for assets in domestic markets and leads to stronger M3 growth and
higher returns.
Next we analyze the forecast error variance decomposition of M3, prices
and real GDP. Starting with Model 1a the variance decomposition for M3
suggests that, besides shocks to M3 itself, shocks to the short-term interest
rate are the most important source of ‡uctuations in the monetary aggregate
over the 1-year horizon. However, their importance declines over time (see the
Table 2, upper panel). By contrast, global liquidity has a small contribution
to the variability of M3 in the short-run but it gradually increases over time
becoming the most important variable in the longer-run, after shocks to M3
itself. As regards Model 2a, the results are somewhat di¤erent as in this
case innovations to the short-term interest rate do not play an important
role in explaining M3 ‡uctuations. Instead, global liquidity plays a strong
role also in the short-run, being the most important variable in explaining
the variability of M3 at any horizon, again excluding M3 itself.
The middle panel of Table 2 shows the forecast error decomposition for
prices. M3 plays an important role in explaining the forecast error variance
in both models, particularly in Model 1a. Again, the main di¤erence between
the two models concerns the importance of shocks to the short-term interest
rate. In fact, in Model 1a shocks to short-term rates are important in explaining the variability of the price level, even in the longer-run. In addition,
their contribution is always larger than that of shocks to global liquidity.
By contrast, in Model 2a global liquidity appears to be the most important
contributor to the variability of price level in the long-run, with a share of
36.6% at a horizon of four years.
introduced in the model as the most endogenous variable. The shape and the size of
the responses do not change signi…cantly, both in the non-recursive and in the Cholesky
identi…cation scheme.
17
Table 2. Forecast error variance decomposition
1 year
Model 1a
2 year 3 year
4 year
1 year
Model 2a
2 year 3 year
4 year
Variability of M3
GL4Y
YR
PI
M3
SR
ER
2.4
0.6
4.9
79.5
10.4
2.1
15.4
1.3
3.1
67.1
9.8
3.4
26.8
11.0
2.4
49.7
6.3
3.9
33.7
16.6
1.9
36.2
4.4
7.3
14.4
1.0
4.8
79.1
0.5
0.2
44.6
1.4
5.4
47.3
1.0
0.3
58.8
5.9
4.8
26.8
2.8
0.8
64.8
6.5
3.8
17.2
5.6
2.1
8.2
1.2
61.4
17.2
0.8
11.2
20.0
1.2
48.9
19.7
0.5
9.7
36.6
2.6
35.1
16.8
1.0
7.8
40.6
36.8
2.7
1.1
17.2
1.6
55.7
26.8
2.1
1.1
13.2
1.1
57.9
24.3
1.9
1.2
13.0
1.8
Variability of prices
GL4Y
YR
PI
M3
SR
ER
6.0
0.2
52.6
14.3
18.3
8.5
4.6
0.2
32.8
31.6
24.1
6.8
4.3
2.4
23.6
40.5
23.1
6.1
12.4
8.4
17.0
38.7
16.6
6.9
4.1
1.6
73.4
8.6
0.7
11.5
Variability of real GDP
GL4Y
YR
PI
M3
SR
ER
5.7
74.0
7.6
0.6
4.8
7.2
19.2
54.6
5.1
3.8
8.2
9.1
38.4
41.0
3.9
3.4
6.5
6.7
40.8
37.8
3.4
3.2
6.2
8.6
18.1
65.2
4.4
0.4
9.9
2.0
Finally, the decomposition of the forecast error variance for output is
shown in the lower panel of Table 2. As in the case of the euro area models,
in Model 1a the contribution of the short-term rate to the variability of real
output is relatively limited. In Model 2a shocks to the short-term rate explain
a share of 17.2% of GDP variability after two years and remain well above
10% thereafter. As for international money, both models suggest that while
in the short-run shocks in global liquidity play a small role in in‡uencing
18
output ‡uctuations in the euro area, the importance increases over time with
global liquidity becoming relevant in the long-run.16
Overall, the analysis suggests that the impulse responses to shocks to
global liquidity are quite robust to the type of speci…cation that is chosen.
The results highlights that a positive shock to global liquidity leads to a
rise in euro area M3 and in the price level in the euro area. The e¤ect on
euro area real GDP is found to be positive and temporary, with a return to
baseline four years after a shock to global liquidity. As regards the forecast
error variance decompositions, the results suggest that global liquidity plays
an important role in explaining ‡uctuations in M3, prices and output in the
euro area. However, as regards prices, the evidence is not conclusive on the
relative importance of global liquidity and interest rates. In particular, while
in Model 1a global liquidity plays a limited role, it is quite important in
Model 2a.
4
Conclusion
The paper relied on the SVAR approach to construct two benchmark models
of the euro area that seem to properly identify exogenous monetary policy
shocks. The behaviour of GDP, prices, money and the exchange rate derived
from the impulse response functions is consistent with the transmission of a
monetary policy impulse. By introducing in the models a further endogenous
variable, we could check the e¤ects of a global liquidity aggregate on euro
area macroeconomic developments. Our results show that a positive shock
to extra-euro area global liquidity leads to a permanent rise in M3 and the
price level and determines temporary increases in euro area output and a
temporary appreciation of the real e¤ective exchange rate of the euro.
The relevance of the inclusion of foreign variables in the empirical models analysed here relates to the broad economic integration across-countries
already achieved and to the speed at which capital markets are currently
16
The result that global liquidity per output is the main cause of the euro area output
volatility in the longer-run is somewhat above what would be expected. One possible
explanation for this …nding is that shocks to global liquidity per output may capture also
shocks to global demand. However, when other international variables are introduced
in the benchmark models (global GDP and global interest rate outside the euro area),
the contributions to the variance are always rather limited, thus suggesting that global
liquidity is indeed an important source of variability for some euro area macroeconomic
variables.
19
able to move funds worldwide. The literature on international business cycle
shows that the cross-country transmission of shocks is an important element
in explaining domestic output ‡uctuations. This paper suggests that a similar channel is at work when dealing with monetary aggregates. In fact, our
contribution to the current empirical literature hinges also on the …nding
that shocks to global liquidity play an important role in explaining price and
output ‡uctuations in the euro area.
The size of the impact of the development in the extra-euro area monetary
aggregate is to some extent sensitive to the speci…cation implemented. When
a recursive scheme is used, both M3 and the foreign liquidity have important
explanatory power for the variability of euro area prices. In addition, in the
longer-run shocks to global liquidity seem to have a higher importance for
the variability of prices than shocks to M3 itself. On the other hand, when
a non recursive scheme is at work, global liquidity plays a somewhat smaller
role in the short-run in explaining price ‡uctuations. Nevertheless, also in
this model the global monetary aggregate still contributes signi…cantly to
price variability at longer horizons.
As for GDP ‡uctuations, the contribution of global liquidity shocks is
increasing over time and soon becomes the most important source of GDP
variability. In particular, when the recursive approach is implemented, the
portion of output variability explained by foreign money shocks is very large.
However, comparing our results with those of Canova and De Nicoló (2002)
we can note that the share of the output ‡uctuation after two years attributable to a global liquidity shock (20-40%) is even smaller than what they
report for some European G7 countries due to a “standard”monetary policy
innovation.
Concluding, the main contribution of this work is that the evolution of
foreign variables and in particular of monetary aggregates is relevant for the
economic policy management of a country. The evidence reported suggest a
possible channel of transmission of global liquidity shocks: robust monetary
growth abroad may lead to capital ‡ows into the domestic economy due to the
search of di¤erent sources of investment, thus resulting in stronger monetary
growth and higher asset returns in the recipient country.
20
A
Data annex
The table below provides an overview of the series used in this study. All
series are seasonally adjusted except interest rates and the real e¤ective exchange rate of the euro.
Variable
Broad
monetary
aggregates
Real GDP,
GDP de‡ator
and CPI
Short-term
interest rates
Total commodity
prices
Real
(CPI-based)
e¤ective exchange
rate of the euro
De…nition
Sources
Euro area: M3
US: M2
ECB.
US Federal Reserve
Board (press release H6).
Bank of Japan.
Japan: M2 plus
certi…cates of deposit
UK: M4
Canada: M2+
Euro area (HICP)
US
Japan
Canada
UK
Euro area: three-month
interbank rate (until 29
December 1998); three-month
EURIBOR (thereafter)
US
Japan
Canada
UK
Commodity Price Index
Aggregation of the
bilateral exchange
rate of the euro
against 12 partner countries.
Bank of England.
Bank of Canada.
Eurostat.
OECD Main Econ.
OECD Main Econ.
OECD Main Econ.
OECD Main Econ.
ECB.
OECD Main
OECD Main
OECD Main
OECD Main
HWWA.
Econ.
Econ.
Econ.
Econ.
Indicators.
Indicators.
Indicators.
Indicators.
Indicators.
Indicators.
Indicators.
Indicators.
ECB.
The criterion used for the selection of the broad aggregates for each country was that are the key broad monetary aggregates in the di¤erent countries
from a monetary policy point of view. The global monetary aggregate (GL4)
21
is constructed by converting each national aggregate into euros using PPP
exchange rates. The formula used is the following:
GL4 =
4
X
i;eur
Mi Eppp
i=1
i;eur
where Mi represents each national monetary aggregate and Eppp
is the corresponding country’s PPP exchange rate vis-à-vis the euro. The PPP exchange
rate is based on relative PPP. It is computed by taking the nominal exchange
i;eur
) as the
rate of January 1999 of the several countries against the euro (E1999
basis and using the consumer price indices of the several countries to construct the PPP exchange rate for the other periods. More precisely, the PPP
i;eur
exchange rate for country i (Eppp
) at time t is constructed as:
i;eur
i;eur
Eppp;t
= E1999
eur
Pti
P1999
i
Pteur P1999
eur
i
where P1999
, P1999
are the consumer price indices in the euro area and in
country i in January 1999, respectively, and Pteur , Pti are the corresponding
consumer price indices at time t. Thus, this procedure does not guarantee
that absolute PPP holds. However, for the purpose of this study, the level
of the exchange rate used to construct the global liquidity is relatively not
important as only the changes over time of the global liquidity aggregate will
matter in the estimation of the model.
One possible limitation in the construction of the global liquidity aggregate as done above, is that the resulting aggregate will be rather sensitive to
the de…nition of the monetary aggregate used to construct it. As there are
problems of comparability between the aggregates used for the di¤erent countries, given the di¤erent de…nitions of monetary aggregates, the weights may
not re‡ect appropriately the di¤erences in the importance of each country.
This is particularly the case for Japan and the US, with the former country
having over same periods a larger share in the global liquidity aggregate than
the latter. Therefore, we have also constructed a di¤erent measure of global
liquidity using GDP weights. The formula is the following:
GL4 =
4
X
M Indexi
i=1
22
GDPi i;eur
E
eur ppp
GDPall
Figure 7: Global liquidity computed with di¤erent weights
where GDPi represents nominal GDP of country i expressed in national
eur
currency and GDPall
is the aggregate GDP of the whole set of countries
obtained as the sum of each country’s GDP converted into euros with PPP
exchange rates. M Indexi is the index of the monetary aggregate in country
i. For each country this index equals 100 in January 1999 and grows at the
same rate as the monetary aggregates denominated in national currency used
for each country.
Figure 7 shows the di¤erence between the two series. As can be seen in
the chart, most of the time they are quite limited.
In the case of the other variables used, namely the short-term interest
rate, real GDP and the GDP de‡ator, the computation of global aggregates
was done by relying on GDP weights obtained using PPP exchange rates to
convert each national nominal GDP into euro.
23
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FORTHCOMING
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