Fiscal and Monetary Interactions in the Eurozone with
Real Time Data
This version Sept 08:
Preliminary and Incomplete
Do not cite
John Lewis
De Nederlandsche Bank
Andrew Hughes Hallett
George Mason University & CEPR
Abstract
Using real time data, we estimate reaction fiscal and monetary reaction functions for
the eurozone both individually and as a system. We find that monetary policy does
respond to fiscal stance, but that the reverse does not hold. A loosening of the
cyclically adjusted budget balance by one percentage point prompts a monetary
tightening of 37 basis points. Estimating equivalent reaction functions based on ex
post data yields a different story, and disguises the central bank’s reaction to fiscal
policy.
JEL Codes: E63 (Comparative or Joint Analysis of Monetary and Fiscal Policy)
E61 (Policy Designs and Consistency, Policy Coordination)
Keywords:
Policy co-ordination, Fiscal Policy, Monetary Policy, Real Time Data
The authors would like to thank…. for useful comments. Part of this paper was written
during John Lewis visit to the Robert Schuman Centre for Advanced Studies(European
University Insitutute) under the auspices of the Pierre Warner Chair programme, which is
gratefully acknowledged. The empirical section benefited from the helpful comments of
Kerstin Bernoth and Steven Poelhekke. The views expressed are those of the authors and not
necessarily those of the institutions they are affiliated to.

Economics and Research Department, De Nederlandsche Bank, PO Box 98, 1000AB, Amsterdam, The
Netherlands, [email protected]

School of Public Policy, George Mason University, George Mason School of Public Policy, 4400 University
Drive, MS 3C6, Fairfax, VA 22030 [email protected]
1. Introduction
In recent years, there has been a growing appreciation that analyses of policymakers
behaviour need to consider the data the policymaker had at the time (real time data), as
opposed to the revised data available many years hence (ex post data). As Orphanides (2001)
points out, any policy rule based on ex post data could not have been implemented by the
policymaker since it relies of information the policymaker did not have. Accordingly, it
cannot be interpreted a description of what the policymaker was trying to do. By the same
token, that suggests empirical estimates of policymakers “reaction functions”, should be
formulated in terms of the real time data that the policymaker could have reacted to.
Since Orphanides’ groundbreaking article there has been a proliferation of papers examining
the behaviour of monetary authorities based on real time data. In recent years, a similar
literature has sprung up in the field of fiscal policy. However, to our knowledge, there are no
papers which have examined the interactions betweens fiscal and monetary policy based on
real time data.
Clearly Orphanides real-time critique applies equally to empirical characterisations of
monetary-fiscal interactions. Any descriptive account of policymaking which aims to capture
what a policymaker was trying to do, must be conditioned on data available at the time. Aside
from the well known problems of real-time output gap measurement, several papers have
chronicled similar real-time data issues with budget deficits and their cyclically adjusted
counterparts (Jonung & Larch, 2003; Hughes Hallett et al, 2007).
The goal of this paper is to analyse fiscal-monetary interactions in the eurozone, using real
time data. We do this by estimating reaction functions, where each policymaker is permitted
(but not required) to respond to the other policymakers instrument, as well as economically
relevant target variables such as the output gap, inflation and the debt ratio.
There exists a relatively large theoretical literature on the optimal interaction of fiscal and
monetary policy, and an empirical literature (based on ex post data), which seeks to describe
these interactions in practice. A natural extension to these theoretical literatures is to examine
how fiscal and monetary policymakers have interacted with each other based on real time
data.
On the monetary side, the literature suggests that the distinction between real time and ex post
data can have important implications for characterisations of policymaker’s behaviour.
Orphanides (2001) finds that a taylor rule fitted with real time data yields a better to fit to
observed Fed behaviour than its ex post counterpart. In a similar paper, Orphanides (2002)
also shows that during the great inflation, it was persistent mismeasurement of the output gap,
rather than a change in policymakers’ preferences that was the cause of looser monetary
policy. Geberding et al (2003), are able to resolve to puzzling finding that Bundesbank
reaction functions appeared not to have a significant response to monetary growth. By reestimating the same reaction functions with real time data, a strong reaction to monetary
variables is uncovered. A number of papers have also attempted to estimate reaction
functions for the ECB1. Several have also attempted to do this using real time data
(Gerdesmeier and Roffia, 2004; Sauer and Sturm, 2007; Gerlach, 2007; Gorter et al 2007:
Castelnuovo, 2006). A general finding of such papers is that the relatively good fit of the
simple Taylor rule also carries over to eurozone data.
Papers which explicitly contrast the performance of real time versus ex post data in fitting
Taylor rules find that the real time based rules typically tend to yield more “plausible” coefficients. Shifting from ex post to real-time data is often crucial in pushing the coefficient on
inflation above unity- and thus satisfying the “Taylor principle”.2
On the fiscal side, the emerging literature also finds an important distinction between the
accounts of behaviour derived from real data as compared to those using ex post data. As
well as mismeasurement of the state variable (the output gap), the policy instrument (budget
balance, possibly cyclically adjusted) is also subject to measurement error in real time, which
adds an additional dimension to the problem. Hughes Hallett et al (2007) show that cyclically
adjusted budget balances are subject to considerable revisions over time.
Momigliano and Forni (2006), Cimadomo (2007) and Bernoth et al (2008) all find that in real
time, governments seek to use discretionary fiscal policy in a counter-cyclical way, but that
when the same reaction functions are estimated with ex post data, the result is acyclical. Von
1
Studies using only ex post data include: Gerlach-Kristen (2003), Surico (2003), Carstensen and Colavecchio
(2004), Fourçans and Vranceau (2004)
2
Taylor (1999) notes that a k% rise in inflation requires more than a k% rise in the nominal interest rate, in order
to generate a rise in the real interest rate and hence a tightening of the monetary stance. If the inflation
coefficient is less than one, then a rise in inflation induces a looser monetary policy (leading to yet higher
inflation), and hence inflation expectations are not well anchored.
Kalckreuth and Wolff (2007) find that governments seek to adjust spending in a countercyclical way, and are able to do so within the space of a quarter. Beetsma and Giuliodori
(2008), find evidence of eurozone fiscal authorities reacting to each others plans based on real
time data, but do not test explicitly if this result also shows up in ex post data.
Balboni et al (2007) present a model of fiscal monetary interactions in real time when the
policymakers themselves disagree about the output gap. However, this does not attempt an
empirical characterisation of the observed interactions. For the US, Claeys (2003) estimates
fiscal and monetary reaction functions which include the other policymakers instrument as an
argument. The present paper is complementary to this paper, in the sense that it performs a
similar exercise for the Eurozone (rather than the US), and on the basis of real time (as
opposed to ex post) data.
This paper makes a number of contributions to the literature. First, it extends the existing
empirical work on fiscal monetary interactions by making use of real time data. In this way, it
get closer to “what policymakers were trying to do” than the work which utilises ex post data.
Second, it tests whether some of the previous results on fiscal and monetary policymakers
behaviour are robust to the inclusion of the “other” policymakers behaviour in the reaction
function. Third, it provides specific evidence on the eurozone, in contrast to most of the other
literature which tends to focus on the US.
2. Dataset
There is no single eurozone dataset available for all our relevant variables. The Euro Area
Real Time Database is the single most complete dataset, but the vintages only begin in 2001
and some only run up to 2006 . For that reason, to obtain data for a longer time period it was
necessary to compile out dataset independently, using data from several sources. In all cases
our data is at the quarterly frequency.
The bulk of the real time time data is taken from successive editions of the OECD’s economic
outlook from December 1994 (No 56) onwards. It is similar to that used by Hughes Hallett et
al (2007) and Bernoth et al (2008). The variables from this source are the primary balance,
total balance (and their cyclically adjusted counterparts), government debt and the output gap.
Economic Outlook is published twice per year- one edition in June and one in edition
December. The published values of the variables are all on a yearly basis3. To derive our
quarterly data, we take the latest available vintage at the start of a given quarter, and then
perform the Lisman method4 to interpolate quarterly values for the whole time series. This
procedure generates the property that the average of the four quarterly figures equals the
annual figure from the official data.
Economic Outlook does not report eurozone figures for the whole period. Therefore, we
construct our own euerozone data, based on a weighted average of national data. Weights are
determined by the nominal GDP (in millions of euro) of each country5. In each case, we use a
vintage of GDP which matches the vintage of the variable being measured- e.g real time
budget deficits are weighted according to real time GDP, ex post budget deficits are weighted
using ex post GDP and so on. Economic Outlook does not report figures across the whole
period for Luxembourg, Slovenia, Malta and Cyprus and therefore, these countries are
effectively assigned a weight of zero in our analysis. However, the bias from excluding these
countries from the construction of our eurozone data is extremely small, since they account
for around 1% of Eurozone GDP (and for most of the sample, only Luxembourg was an EMU
member).
For monetary policy, the policy instrument is the ECB repo rate. Data is taken from Eurostat..
There is no distinction between real time and ex post data here, since the observation of the
discount rate in real time is not subject to any measurement error.
Inflation expectations data is taken from Consensus Forecasts. This is a monthly survey of
over 200 forecasters, who report inflation expectations for around 20 countries. Participants
are asked to forecast year end inflation for the current year and the next year- i.e. in December
of each year. To generate a forecast for inflation in the intermediate months, we follow a
number of authors6 in performing linear interpolation. This of course only provides a proxy
for “true” inflation expectations, but nevertheless respects the “real-time principle” of
3
The December 2000 and the December 2004 issues of economic outlook (68 and 74 respectively) do not report
a figure for the Greek primary balance.. In these cases, this “missing” data was filled in using the figures
reported in the previous editions (67 and 73 respectively).
4
See Lisman (1967)
5
Greece is assigned a weight of zero prior to 2001.
6
Gorter et al (2007), Sauer and Sturm (2007), Gerlach (2007), Begg et al (1998) and Alesina et al (2001)
restricting our information set to information known to policymakers at the time. The
eurozone figure is obtained by taking a weighted average of the national figures using
eurostat’s yearly HICP country weights7. Consensus Forecasts do not collect data on
Luxembourg, Slovenia, Malta and Cyprus, therefore our “eurozone” figure exclude these
countries. However, since they have a combined weight of around 1% in the HICP, our bias
from excluding them is likely to be very small.
Data on inflation itself was taken from from Eurostat, using the year on year change in the
HICP. Given that initial releases are seldom revised (Coenen et al 2003), the real time data
and ex post data for current inflation are largely the same8, although there is typically a lag of
around 2 months in the reporting of inflation figures. In any case, in the bulk of the analysis,
we assume monetary policy is set on a forward looking basis, and hence the inflation data that
we use are forecasts, rather than contemporaneous inflation data.
Figure 1 compares the real time, ex post and 1 year forecast of the output gap. The forecast
variable is lagged by one year, so that the figure reported for year X quarter Q is the forecast
made at X-1:Q, for the variable at time X:Q.
Figure 1: Data Across Vintages (percentage points)
Cyclically Adjusted Balance
Output Gap
0.5
4
Real Time
1year Forecast
3
Ex Post
2
0
1999Q1
-0.5
2001Q1
2003Q1
2005Q1
2007Q1
-1
1
-1.5
0
1999Q1
-1
2001Q1
2003Q1
2005Q1
2007Q1
-2
-2.5
Real Time
1year Forecast
-2
-3
-3
Ex Post
-3.5
7
These weights are determined at the beginning of each year, and are not subsequently revised. Therefore the
“real time” and the “ex post” HICP weights are identical.
8
“In contrast, the consumer price data are typically not revised at all”, (Coenen et al, 2003, p980)
Looking at the output gap (left hand panel), it is evident that compared to the ex post data the
real time figures (and the 1 year forecast) underestimated the extent of the boom in the first
half of the sample, and were overly pessimistic during the recovery in the latter years of the
sample. Similarly, the real time CAB figures failed to pick up the substantial fiscal loosening
in the early part of the sample, and were sluggish in picking up the improvement in public
finances later on. Taken together, these graphs provide prima facie evidence that empirical
characterisation of monetary and fiscal policy using ex post data may differ substantially from
those which use only the data available to the policymaker at the time.
3. Empirical Estimates of Reaction Functions
Monetary Policy
To capture the behaviour of the ECB we estimate a canonical Taylor rule of the form:
it  it 1  (1   )[0    t  k   y yt  k  zt ]
(1)
where it is the policy rate, t is the rate of inflation9, y is the output gap and z is a vector of
additional variables. k captures the policy horizon of the central bank: k=0 means the
authorities respond to contemporaneous data, k>0 implies forward looking behaviour. The
parameter, , captures the degree of “gradualism” or “inertia” in monetary policy.
Fiscal Policy
In generic form, the aggregate fiscal policy of the eurozone is of the form:
balt  balt 1  (1   )[0   y yt m   DEBT bt  zt ]
(2)
where bal is a measure of the fiscal balance. The precise measures of fiscal balance used
varies across the literature: cyclically adjusted primary balance, capb, the (unadjusted)
In some representations the inflation term is written in terms of a deviation from some target value *. In the
case where * is constant across the sample period, the estimation of the two reaction functions yields identical
results.
9
primary balance, pb, the actual budget balance b and the cyclically adjusted balance b. The
time horizon of the fiscal authorities is captured by m and their inertia by .
Alternatively, one may analyse the fiscal policy of individual countries within the eurozone:
balit  balit1  (1   )[0   y yitm  DEBT Btzt ]
(3)
3.1 Econometric Considerations
We begin by considering the persistence properties of individual variables (for a tabulation of
results, see table A1 of the appendix). An Augmented Dickey- Fuller (ADF) test cannot reject
non-stationarity in any of the variables. However, given the low power of the ADF test when
the autoregressive parameter is close to but below one (as may be the case with these
variables), we follow Claeys (2003), Österholm (2003) and others , by also undertaking a
KPSS test (Kwiatwoski et al,1992). The KPSS test was carried out with a Bartlett kernel
where the bandwidth parameter was automatically selected following the Newey-West
procedure (Newey and West, 1994). The test was run both with and without a trend term.
For the output gap and inflation, the KPSS test cannot reject stationarity, and for the interest
rate the KPSS tests rejects at the 1 and 5% levels. For the fiscal variables the picture varies
across vintages- some vintages show some signs of a unit root, but other vintages of the same
variable do not. But in no case do all three tests suggest non-stationarity for a given time
series, and for no variable are the results consistent across the three vintages. Taken together,
the results do not provide clear cut evidence of non-stationarity in the time series.
As a further check, a visual plot of all series suggests non-stationarity. Furthermore, sound
theoretical arguments can be presented as to why these variables should be stationary.
Therefore, we proceed to model these variables as persistent but stationary.
In common with the bulk of the literature, the reaction function equations were estimated
using the (two stage) Generalised Method of Moments. This overcomes the problem of
potential correlation between explanatory variables and the residual term. We reported
Newey-West Heteroscedasticity and Autocorrelation corrected (HAC) standard errors. All
regressions use a Bartlett kernel with a bandwidth of 3. This is consistent with the literature10
and is also motivated by the suggestion of Green (2003) of using T1/4. In any event, the
results are robust to changes in the bandwidth parameter.
As instruments, we employ one to four lags of the (real time) inflation and output gap series,
and one to four lags of the year on year percentage change in the euro-dollar exchange rate.
The j-statistic is reported for each regression, and in each case exogeneity is strongly
supported.
Favourable results for tests of the exogeneity of instruments are a necessary condition, but it
is also important that instruments are “relevant”- i.e. that the correlation between the
instruments and explanatory variables is high. Stock and Yogo (2002) argue that many
applications of GMM and IV suffer from the problem of weak (but nevertheless exogenous)
instruments. If instruments are of low relevance, then not only do standard aysmptotics fail,
but the asymptotic standard errors are increased and hence the power of hypothesis tests is
reduced.
As Staiger and Stock (1997) and others have demonstrated, the weak instrument problem can
be present even when first stage F-tests are significant at conventional levels.
Whilst the Hansen test can detect exogeneity, it cannot say anything about the relevance of
instruments. Moreover, the Hansen test is a somewhat “permissive” criterion in the sense that
it permits quite a wide range of instruments, which may yield contradictory estimation results.
To ensure that our selected instruments are relevant, we employ several criteria. We start
with the weak instruments test of Stock and Yogo (2002). This is based on two definitions of
weak instruments, and yields two formal tests. The first tests for the bias of the IV estimator
relative to OLS; the second tests whether conventional Wald test on IV statistics has a size
that could exceed a certain threshold. For the sake of brevity we report the only the outcome
of the first of these tests for each regression.11
10
See for example Castelnuovo (2003)
In practice the two tests provide very similar conclusions. A full set of results is available from the authors
upon request.
11
We also use the partial R2 measure of Shea (1997) to measure the relevance of our
instruments. This test allows for possible collinearity of instruments, which may be an issue
when a number of lags of the same variable are used as instruments.12 For our instruments to
be relevant, the partial R2 should be also be large. For results of this, see appendix
3.2 Estimations of Taylor Rules
Table 1: Standard Taylor Rules
I
II
III
IV
V
β0
0.47
(0.19)**
1.13
(0.39)***
0.29
(0.43)
1.25
(0.67)*
0.75
(0.29)**

1.52
(0.05)***
1.44
(0.27)***
1.58
(0.27)***
1.45
(0.45)***
0.93
(0.35)***
y
0.98
(0.08)***
1.04
(0.28)***
0.94
(0.05)***
1.10
(0.08)***
0.68
(0.05)***

0.43
(0.03)***
0.61
(0.05)***
0.42
(0.06)***
0.64
(0.07)***
0.63
(0.00)***
-0.35
(0.09)***
-0.28
(0.105)**
CAPB1YR
-0.37
(0.08)***
CAPBRT
0.05
(0.12)
-0.10
(0.19)
-0.01
(0.06)
CAPBREV
J-stat
2
R
0.17
0.16
0.14
0.16
0.15
0.86
0.89
0.86
0.89
0.89
Instruments used in are: to 4 lags each of the real time output gap, inflation and zero to four lags of the percentage change in the
exchange rate and oil prices.
Instruments used in IV are as for I to III plus the one year forecast of the cyclically adjusted primary balance
Standard errors shown in parantheses based on Newey-West serial correlation and heteroscedasticity robust estimator
*,**,*** denote significance at 10, 5 and 1% levels
Table 1 shows the results of our estimations of Taylor rules. Regression I is the standard
taylor rule with terms in inflation and output gaps. The authorities react to both, but do so
more strongly to inflation than to output. Regression II augments the standard specification
12
In multivariate panels, simply regressing each explanatory variable on the instrument vector may yield
misleading results. Multicollinearity in the instruments may lead to a high R 2 even though each individual
instrument is only weakly correlated with a given explanatory variable.
with the one year ahead forecast for the CAPB. This enters with a negative sign, indicating
that the ECB tries to “undo” the impact of fiscal policy: specifically, for every percentage
point loosening of CAPB, monetary policy tightens by 37 basis points.
To check that our specification of the ECB’s time horizon was correct, regression III replaces
the one year ahead forecast of the CAPB with the real time value of (the current years) CAPB.
The coefficient on this turns out to be insignificant. As a further check, regression IV
includes both CAPB variables- but only the one year ahead forecast is significant. Lastly
regression V includes the variable CAPB_REV, defined as the differences between the real
time CAPB and the forecast of the same variable four quarters ago. Again, this is
insignificant. Taken together, this implies that the ECB is fully forward looking in its
treatment of fiscal policy- it responds to the forecast value of the CAPB at the same time
horizon as it responds to inflation and output, and it does not seek to correct revisions to the
CAPB that occur between the planning and implementation stage. Note however, that in our
dataset, these revisions are mean zero, and hence there is no systematic “slippage” between
planning and implementation. Regressions II to V are robust to the inclusion of the relevant
fiscal variable as an instrument (equivalent to treating the variable as exogenous).
To see the difference between real time and ex post data, we re-run the same regressions, but
using ex post data. In these regressions, all instruments were converted to their ex post
analogs. The columns headed EP1 refer to the case where the authorities are assumed to be
forward looking by four quarters (as in the real time data case) when setting policy. Since it is
common in the literature to estimate taylor rules using ex post data assuming no forward
looking behaviour, we also estimate the equations under this specification, denoted EP2.
Table 2: Real Time vs Ex Post Data: Coefficients Compared
Regression
I
II
III
Data
RT
EP1
EP2
RT
EP1
EP2
RT
Horizon
+4
+4
0
+4
+4
0
+4

1.52***
1.70***
1.14***
1.44***
1.10***
1.58***
0.11
y
0.98***
0.09
0.65***
1.04***
0.23
0.94***
0.50***
0.43***
0.87***
0.75***
0.61***
0.81***
0.42***
0.77***
0.05
1.01***

CAPB1YR
CAPBRT
-0.37***
EP1
EP2
+4
0
-0.25
Instruments used in I to III are: to 4 lags each of the real time output gap, inflation and zero to four lags of the percentage change
in the exchange rate and oil prices.
*,**,*** denote significance at 10, 5 and 1% levels
Table 2 demonstrates how the results differ depending on the data vintage (and horizon used)
for all regressions. Each of the three regressions was re-run, but with ex post data. EP1
denotes the case where the policymaker is assumed to be forward looking by four quarters,
EP2 is the case where the policymaker is assumed to respond to contemporaneous variables.
In the case of regression I, the ex post data suggest much larger gradualism on the part of the
central bank, and in the forward looking case, significantly understates the central bank’s
response to the output gap. In the case of regression II, the ex post data again overstates the
extent of gradualism, and it understates the response to both output and inflation. Lastly, for
regression III, the weight on inflation does not differ significantly from zero, and the central
bank seems to move strongly to follow the fiscal policymaker. These regressions make it
clear that the choice of data vintage is crucial in correctly capturing the monetary
policymakers response. Reading across the rows which depict the response to the output gap
and inflation, it’s clear that only in the real time case does a consistent picture emerge.
3.3 Estimations of Fiscal Policy Reaction Functions
Table 3: Fiscal Reaction Functions- CAPB
i
debt
I
II
III
IV
m=4
m=4
m=0
m=0
t
t
t-4
t-4
real time
real time(t-4)
real time t-4
real time
B
y
0.58
(0.11)***
1.30
(0.32)***
0.386
(0.10)***
-0.05
(0.17)
DEBT
0.05
(0.02)**
0.17
(0.06)**
0.09
(0.16)***
0.07
(0.05)

0.45
(0.03)***
0.73
(0.03)***
0.71
(0.00)***
0.91
(0.07)***
i
0.11
(0.35)
-0.09
(0.16)
J-stat
0.14
0.18
021
0.03
R2
0.66
0.77
0.87
0.88
Instruments used in I , II I and IV are: 1 to 4 lags each of the real time output gap and inflation, 0 to 4 lags of the annual
percentage change in the euro dollar exchange rate and a lagged dependent variable and the debt variable
Instruments in IV are as per III but with 1 to 8 lags of the output gap (1 to 4 lags does not converge)
For abbreviations to the table, see notes below table 1.
Table 3 shows the results for the fiscal reaction function. Since the literature takes different
views on the time horizon of the fiscal policymaker, we estimate reaction functions for both
the case where the fiscal policy responds to contemporaneous variables, and where the fiscal
policymaker is “forward looking” by four quarters.
Regression I, our forward-looking benchmark, shows a a significant attempt to use fiscal
policy in a countercyclical way, and has a positive co-efficient on the debt ratio, implying that
higher debt ratios lead to a higher primary surplus. When the policy rate, i, is added,
(regression II) it enters with an insignificant co-efficient. The contemporaneous regressions
tell a very similar story- in regression III the co-efficient on the output gap is significant and
positive (though slightly smaller than in the forward looking case), and the co-efficient on
debt is also significant. Adding in the policy rate (regression IV) results in an insignificant
co-efficient. Taken together, these regressions imply that at both the planning stage, and in
the implementation stage discretionary fiscal policy is countercyclical, and does not appear to
respond to monetary policy.
Re-estimating these equations using ex post data yields quite different results. Regression I
records a highly persistent and strongly pro-cyclical fiscal policy. Regressions II and III have
an autoregressive parameter of greater than one, and hence one cannot compute long runcoefficients. 13
3.4 System Estimation of Reaction Functions
We now estimate the equations as a system, to take into account possible correlations in the
errors across the two reaction functions. We retain a similar specification to the individual
estimation case- but with the difference that debt is excluded from the fiscal policymaker’s
reaction function14. The results are shown below:
Table 4: Real Time Reaction Functions as a System
Monetary
Fiscal
β0
1.94
(0.45)***
0
1.07
(0.505)**

1.13
(0.05)***
y
1.05
(0.35)***
y
0.64
(0.13)***

0.72
(0.03)***

0.85
(0.07)***
CAPB1YR
-0.42
(0.06)***
i
0.14
(0.14)
R2
0.90
0.86
0.89
Instruments used in monetary reaction function are: 0 to 4 lags each of the real time output gap and 1 to 4 lags of inflation, 0 to 4
lags of the annual percentage change in the euro dollar exchange rate and a lagged dependent variable and the debt variable
Instrurments used in the fiscal reaction function are as for the monetary one, but without the oil price variable.
13
14
For this reason, these ex post regression results are not tabulated here.
When debt is included, the reaction is positive, but insignificant.
Table 4 makes it clear that estimating the results as a system yields similar results to the
individual estimation. Monetary policy responds more to inflation than to output, and
responds negatively to the CAPB, with similar co-efficients. For fiscal policy, the response to
output is more moderate, (but nevertheless stabilising), and there is no response to monetary
policy.
4. Conclusions
Estimating the reaction function of the central bank and the fiscal authority using real time
data yields significantly different characterisations of the policymakers behaviour than when
ex post data is used. Moreover, we uncover evidence that the central bank does take fiscal
stance into account when setting monetary policy, and that the standard taylor rule can be
augmented with an additional term in the cyclically adjusted primary balance.
The fiscal authorities do not appear to respond to monetary policy when setting fiscal policy.
That could reflect the fact that budgets are typically set at an annual frequency, and are
revised only in response to specific macro events, and hence fiscal policy cannot easily be
adjusted at a comparable frequency to offset monetary policy.
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Appendix A: Results Tables
Table A1: Unit Root Tests
ADF Test Statistic
KPSS Test Statistic
H0: No Unit Root
Variable
H0: Unit Root
Gap
Inf
Real Time
-1.365
No trend
0.234
With trend
0.106
Ex Post
-0.885
0.307
0.105
1yr forecast
0.3685
0.257
0.099
Ex Post
-0.096
0.503*
0.096
1yrforecast
-0.435
0.550*
0.084
-0.767
0.162
0.146*
Real Time
-1.744
0.102
0.109
Ex Post
-0.046
0.302
0.177**
1yr forecast
-0.648
0.272
0.187**
Real Time
-1.167
0.397*
0.114
Ex Post
-0.857
0.256
0.137*
1yr forecast
-0.948
0.188
0.159**
Real Time
-2.125
0.182
0.145*
Ex Post
-1.141
0.567**
0.112
1yr forecast
-1.968
0.121
0.127*
i
CAB
PB
Debt
*,**,*** indicate rejection of the null hypothesis at the 10,5, and 1% significance levels respectively