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Constrained discretion in Sweden
Research in Economics 66 (2012) 33–44
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Constrained discretion in Sweden✩ Jérôme Creel a,b,∗ , Paul Hubert b a
ESCP Europe, 79, avenue de la République, 75011 Paris, France
b
OFCE-Sciences Po, 69, quai d’Orsay, 75340 Paris Cedex 07, France
article
info
Article history: Received 17 January 2011 Accepted 25 August 2011 Keywords: Monetary policy Inflation targeting MSVAR Regime-switching Sweden
abstract We study whether the institutional adoption of inflation targeting (IT) has constituted both a policy and a macroeconomic switch in Sweden using the nonlinear MSVAR technique. We assess the relative weight put on inflation in the monetary reaction function and the capacity of IT to reduce macroeconomic uncertainty. We show that IT has constituted a policy switch to a lower focus on inflation, in contrast with the usual argument that has been put forth by IT opponents. Moreover, IT adoption is shown to have reduced uncertainty, through lower inflation and output variabilities simultaneously. Last, counterfactuals suggest IT provides higher monetary policy leeway. © 2011 University of Venice. Published by Elsevier Ltd. All rights reserved.
1. Introduction In the 1990s, Sweden experienced an institutional policy shift, from a fixed exchange rate regime to a floating exchange rate regime with an inflation target. The fixed exchange rate was abandoned in November 1992 and an inflation target was formulated in 1993, and planned to be effective from 1995 onwards. In addition, there have been two institutional shifts in fiscal policy, one with the introduction of fiscal rules in 1997 (see, e.g. Lindh and Ohlsson, 2000) and another with the creation of the Swedish fiscal policy council in 2007 (see, e.g. Calmfors, 2011). All these shifts were consistent with the greater emphasis on price stability adopted by Swedish governments since 1991 (see, e.g. Lindbeck, 1997). In this paper, we study the implications of the adoption of the inflation targeting (IT) regime. We investigate whether there is empirical evidence for a regime switch and whether this institutional switch has been associated with changes in the conduct of monetary policy, and in inflation and output processes. To this end, we rely on a multivariate regime-switching method (Markov-Switching VAR, hereafter MSVAR). Our paper has been motivated by three issues that this method can handle. The first two of them concern the evaluation of IT characteristics, while the third is methodological. First, the regime-switching method allows assessing changes in the conduct of monetary policy after the adoption of this framework, as IT has generally been assumed to focus on inflation.1 Our first test then focuses on changes in coefficients in order to assess potential modification of monetary policy rules.2 Moreover, ✩ The authors are very grateful to an anonymous referee for her/his careful reading, comments and remarks on an earlier draft. This paper benefited from funding by the European Community’s Seventh Framework Programme (FP7/2007-2013) under Socio-economic Sciences and Humanities, grant agreement no. 225408 (POLHIA). ∗ Corresponding author. Tel.: +33 1 44 18 54 56; fax: +33 1 44 18 54 78. E-mail address: [email protected] (J. Creel). 1 Friedman (2004), Leijonhufvud (2007) and Walsh (2009) among others have argued that IT places too much emphasis on inflation, potentially at the
expense of other monetary policy goals. At the opposite, Bernanke et al. (1999) and others have advocated inflation targeting as a general framing of monetary policymaking rather than a rule, encompassing clear targets, accountable policymakers and a flexible strategy. 2 At this stage, the paper does not differ from papers which estimate changes in monetary policy rules after IT adoption (see e.g. Kuttner, 2004; Muscatelli et al., 2002), except that we introduce a multivariate framework. Moreover, as stressed by Sims and Zha (2006), standard estimations generally do not capture multiple shifts in variance because they do not make enough allowance for heteroskedasticity; and identification of forward-looking policy rules is generally fragile. MSVAR thus improve estimation outcomes. 1090-9443/$ – see front matter © 2011 University of Venice. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.rie.2011.08.002
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J. Creel, P. Hubert / Research in Economics 66 (2012) 33–44
IT is not about interest rate rules per se but it is a practical framework which promotes the optimality of constrained discretion for monetary policy, and MSVAR permits us to go further than checking for a change in regime that would only occur at the level of the monetary policy rule: changes in inflation and output processes are also investigated since the Swedish crisis at the beginning of the 1990s has produced heteroskedasticity. Second, IT implies forecast communication and transparency involvement in order to anchor public expectations (Gürkaynak et al., 2010). Thus, according to economic theory, anchoring expectations contributes to economic stability. Our second test with MSVAR therefore highlights, by keeping coefficients constant, changes in the macroeconomic environment, where the environment is defined by the reliability of the underlying model to forecast future outcomes.3 Hence, the environment is approximated by the estimation residuals. Third, at least one reason why the empirical literature has not shown a clear advantage of IT vs. non IT monetary policy, although no country ever quit IT,4 is that most articles endeavoring to conclude on the stability property of IT focused on a control group method during a period where differences between IT and non-IT countries were all insignificant (Gertler, 2003). In contrast, our starting point has been to focus on an individual country and to study changes over time that may have been implied by IT adoption. We use the non-linear multivariate MSVAR model methodology developed by Hamilton (1994) and applied by Assenmacher-Wesche (2006). Contrary to Sims and Zha (2006) who apply the methodology to US data, no subjective priors are introduced in the model and the number of endogenous variables is reduced. Our benchmark VAR model comprising the output gap, inflation and the central bank interest rate is estimated with two types of specifications: on the one hand, a specification with full changes, i.e. changes in coefficients, intercepts and variances of all 3 equations of the VAR; and, on the other hand, a specification where changes across ‘‘regimes’’ are only due to intercepts and variances, while coefficients of all 3 equations are kept fixed. The first specification enables us to assess the conduct of monetary policy through a standard backward-looking Taylor rule – the interest rate equation – whereas the second specification enables us to assess the changes in the structure of the economy. Our results contribute to the literature in the following respect. First, they suggest that IT adoption has constituted a change in the monetary policy rule, but not in a way that has generally been admitted in the literature: the new regime which has been consecutive to IT adoption has less focused on inflation than during the pre-IT period. Lower and more stable inflation rates with a less aggressive central bank suggest a high level of credibility and a high potential to target other objectives than inflation. Second, a steep switch in the macroeconomic environment is also visible: this switch has been concomitant with a reduction of uncertainty precisely when IT has been adopted. Drawing on an empirical assessment of the success of IT to anchor expectations in Sweden (Fregert and Jonung, 2008), the latter result suggests that the reduction in overall uncertainty pertaining to IT adoption may depend on the credibility of the new implemented regime through the increased transparency and communication of the Swedish central bank. Last, IT seems to offer some policy leeway: a counterfactual study approach suggests that central bank interest rate would have been higher had IT been adopted earlier. It is important to acknowledge that the paper does not constitute an assessment of the efficiency of monetary policy in Sweden. Hence, our tests do not address the debate on the Great Moderation. The latter is usually associated with the great decline in output, employment and inflation volatility and attributed to more efficient monetary policy, increased globalization, better inventory policies and/or ‘‘good luck’’ (see Davis and Kahn, 2008, for a critical empirical review of these arguments).5 Our results are not blurred by the debate around the Great Moderation which predates our sample. First of all, we do not investigate the reasons for the decline in inflation; rather, we focus on the relationship between the inflation rate and the policy instrument, without any judgment on its optimality over time. We simply analyze the changes in monetary policy which have occurred since IT adoption in the early 1990s. Second, the regime switch which is clearly apparent from our estimation after IT was adopted in Sweden is not consistent with the estimated date of the steep shift in inflation and GDP volatility, either in industrialized economies (early 1980s, see e.g. Summers, 2005) or in Sweden (early 1980s, see Gonzalez Cabanillas and Ruscher, 2008). Two intertwined mechanisms may explain our main result. First, IT is meant to help anchor private inflation expectations (see Gürkaynak et al., 2010), which will enable a central bank to control inflation without pursuing aggressive action towards inflation variations. Second, the central bank’s decision to lower inflation may have led to low and stable inflation and hence to a lower response to inflation. The credibility of the monetary policy framework change may have thus led to changes in inflation expectations and inflation process (see Benati, 2008; Fregert and Jonung, 2008). The decrease in the weight put on inflation is therefore consistent with changes in the structure of the economy and the decrease in inflation and output variabilities. Our interpretation of results is related to the advantages of IT advocated by Bernanke et al. (1999) and Svensson (1999) where IT lies somewhere between rules and discretion, labeled ‘‘constrained discretion’’. Discipline arises from anchoring 3 Walsh (2009) has a three-dimension characterization of ‘‘monetary policy environment’’: constraints, objectives and beliefs. Our first set of estimations, with changes in coefficients, endorses the objectives dimension, whereas our second set of estimations endorses the beliefs dimension. 4 Papers focusing on the impact of IT either did so through the lens of expectations (e.g. Fregert and Jonung, 2008; Gürkaynak et al., 2010; Johnson, 2002; Levin et al., 2004) or assessed the stability of monetary objectives (e.g. Angeriz and Arestis, 2007; Ball and Sheridan, 2003; Lin and Ye, 2007). The former showed that in comparison with non-IT countries, IT countries have been able to better anchor long-run inflation expectations. The latter generally concluded that IT countries have performed as well as non-IT countries. The fact that no country ever quit IT is a forceful argument by Mihov and Rose (2008) about the durability of IT regimes. 5 Davis and Kahn (2008) use US micro data and conclude that improved supply-chain management (or better inventory controls) is the most prominent cause of the Great Moderation. They also show that no decline in uncertainty for the households can be associated with the Great Moderation.
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expectations thanks to the publicly announced inflation target range. It also permits some flexibility: deviations from the target do not incur a loss of credibility and reputation provided the reasons for the deviations are explained to the public. This flexibility is meant to give some leeway to monetary policy. It is noteworthy that the ability of monetary policy to stabilize inflation at a low level do not always lead to Rogoff’s (1985) trade-off between lower inflation variability and higher output variability.6 The remainder of the paper is organized as follows. Section 2 deals with the data. In Section 3, the regime-switching method is presented. Section 4 displays and comments results. Robustness checks and counterfactuals are performed, and our main results are confronted with the literature. Section 5 summarizes our main conclusions. 2. Data Reasons to test potential IT impact in an economy like Sweden are manifold. First, IT was adopted relatively early (in January 1993, with the objective to be fully applied in January 1995), but contrary to some countries like New Zealand, which adopted IT before Sweden, the Swedish inflation target has remained the same since the beginning of the IT regime and no decelerating path of inflation occurred. Second, Sweden is still under an IT regime, whereas Finland and Spain adopted the Euro in 1999. Third, unlike Chile and Israel, Sweden is a developed country: lessons from its IT experience may be of interest for its US or European counterparts.7 Fourth, the Swedish case remains specific: as Fregert and Jonung (2008) showed, the credibility of IT was immediate after it was announced and inflation rates were considerably reduced short after its adoption. We use monthly data from 1987:1 to 2007:12. The interest rate is the central bank key interest rate.8 (source: Sveriges Riksbank) The inflation rate expressed in month-over-month growth rate is the measure of inflation targeted by the Sveriges Riksbank: UND1X, which corresponds to consumer inflation (source: Statistics Sweden). The output gap measure is the interpolated monthly measure of the OECD.9 The inflation variable is expressed in first difference of the log of the price index and all variables in the VAR and MSVAR are expressed in percent. Each model includes 4 lags according to usual statistical tests. Contrary to Sims and Zha (Sims and Zha, 2006) who always include a monetary aggregate, commodity prices and the unemployment rate, our benchmark VAR specification focuses on a smaller structural model comprising three endogenous variables10 : the output gap (or the unemployment rate), the nominal short-run interest rate and the officially-targeted inflation rate. The inflation measure UND1X excludes households’ mortgage interest payments and the direct effects of changes in indirect taxes and subsidies. This underlying inflation does not exclude energy and fresh food prices or imported inflation. The focus on this consumer inflation index targeted by the Swedish central bank enables to take into account in the analysis the effects of the exchange rate, potentially important in a small open economy like Sweden, and the focus of the Riksbank on the exchange rate throughout the sample. 3. Method A strand of the literature making use of Markov-Switching VAR (MSVAR) has developed in macroeconomics and permits to date breaks and new regimes while letting data speak. It enables us to reveal the different regimes which have occurred in IT countries. Moreover, the choice of regimes rather than pure breaks allows checking the argument that disinflation policies had already existed in the past, either shortly before IT adoption or hand-in-hand with the ‘‘Great Moderation’’ process, i.e. exogenously to IT adoption. Ammer and Freeman (1995) estimated a canonical VAR whose sample stopped just before inflation targets were first announced, and then, they compared actual values for GDP, inflation, and the real interest rate with the (out-of-sample) forecast ones. They interpreted the difference between both variables – actual and forecast – as evidence of a change of regime. In contrast, using MSVAR technique with sufficiently-long samples after IT adoption can reveal a new regime rather than assume it. Our approach is very close to that of Assenmacher-Wesche (2006). 6 This point was already raised by Rogoff (1985) after he mentioned inflation-rate (or price level) targeting: the reason why the tradeoff does not apply lies in the specificities of constrained discretion. 7 Despite its numerical inflation target, the European Central Bank has not adopted an IT framework. 8 It is the Riksbank’s primary tool for influencing inflation. It corresponds to the marginal rate until 1994:6 and the repo rate afterwards. Given the suddenly and temporary spike of the central bank interest rate to 79.8% during the EMS crisis in 1992:9 which is uncorrelated to inflation and output gap levels at this date, we proceed to a linear smoothing interpolation between 1992:8 and 1992:10 to correct for this outlier. 9 For robustness purposes, we also use the unemployment rate that we substitute to the output gap in our estimations. The Swedish unemployment rate
is I (0) at the 5% level according to the KPSS test. Though it is generally acknowledged that the unemployment rate is better modeled as a I (1) process (the ADF test rejects non stationarity), we follow Orphanides and Williams (2002) and Orphanides and van Norden (2002) and check the robustness of results to the inclusion of the unemployment rate. 10 Alternative models with three to six variables have also been tested; they have always included the central bank interest rate, the inflation rate (either CPI, GDP deflator or UND1X), and the output gap, and they have been extended to real GDP, M2, energy prices and/or exchange rate. These latter variables do not improve the fit of the estimated first three variables. Moreover, the effects of a monetary shock are similar to those of the benchmark VAR and confirm that the latter is not misidentified when controlling for extra-variables. Disregarding these extra-variables has one major advantage: it saves some degrees of freedom, MSVARs being pretty much ‘‘data-consuming’’: the number of parameters to estimate depends on the number of variables, lags and states and can quickly be explosive. With n variables, p lags and m states, there are m × (n × (n × p + 1) + (n × (n − 1)/2)) parameters for the VAR plus m × (m − 1) elements in the transition probabilities matrix.
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The Markov-Switching VAR, as proposed by Hamilton (1994),11 allows the structural coefficients and the covariance matrix to be dependent on an unobserved state variable St which is assumed to follow a 1st order Markov chain. The joint distribution of the shocks can be non-constant across our sample periods. The general framework is described by the following equation:
yt = xt · βSt + ut t = 1, . . . , T ut |St ∼ N (0, ΣSt ) St = {1, . . . , M }
(1)
where yt = (y1,t , . . . , yp,t ) is an 1 × n vector of endogenous variables, with n the number of variables of interest, xt is an 1 × np vector of p lagged endogenous variables, St is an unobserved state, βSt is an np × 1 vector of parameters, T is the sample size and M the number of states (or regimes). This baseline equation of the model is free of restrictions. Since we do not know ex ante the possible changes of monetary policy effects implied by IT and because the empirical approach is data driven (i.e. we are looking for what data tell us about this framework setting aside any preconceived conclusions), we do not impose restrictions on parameters.12 The covariance matrix ΣSt takes the form:
ΣSt = σS2 (St ) · Ip . The transition probabilities matrix, noted P, is defined following Hamilton (1994): p11 p12
P = ..
.
p1M
··· ··· ··· ···
pM1 pM2
.. , .
pMM
with j=1 pkj = 1 and pkj ≥ 0, ∀k, j ∈ {1 . . . M }. Two models are tested:
∑M
yt = (1n , yt −1 , . . . , yt −p ) · βSt + ut yt = 1n · βSt + (yt −1 , . . . , yt −p ) · δ + ut and can be written as: yt = xt · βSt + z · δ + ut
(2)
M different models are then simultaneously estimated such as:
(1p ⊗ yt ) = xt · (β1 | · · · |βM ) + (1p ⊗ zt ) · δ + (u1t | · · · |uM t ) and lead for a given regime St = j to: yt = xt · βj + zt · δ + ut ut |St = j ∼ N (0, Σj ). Initial values of the vector of parameters are calculated. A conditional probability density function is defined according to the information set in t − 1. The model is recursively estimated through the maximum likelihood ‘‘EM’’ algorithm, starting from the unconditional density of yt which is calculated by summing conditional densities over possible values for St . The maximum likelihood estimates are finally obtained by maximizing the log-likelihood function and allow attaining the final matrix of parameters. 4. Results 4.1. Estimates with switching coefficients In this section, we present the key results of the benchmark VAR specification – comprising the output gap, inflation and the interest rate – with changes in coefficients, intercepts and variances of all 3 equations. It must be acknowledged that this benchmark VAR model provides the usual characterization of the effects of monetary policy shocks as can be seen in Fig. A.1 in the Appendix. Moreover, our identification of monetary shocks is consistent with the findings of Jacobson et al. (2001) and Alexius and Holmlund (2008) for Sweden. We estimate this 3-equation VAR through the Markov-Switching procedure with two states since we are interested in the institutional switch that occurred in Sweden: we are looking for the date break between two states and we question the correspondence of one of these states with the IT period. Robustness checks to the introduction of a third state have been performed and confirm our conclusions.13 11 See Hamilton (1994, Chapter 22) for a more in-depth details. 12 The ad hoc nature of restrictions is opposed to the seminal motivation of our methodology. The use of Bayesian techniques, though it represents a great advancement in structural estimation, runs up against the same motivation. Indeed, the link between estimation and calibration is strong and depends on subjective priors, which we chose not to use. In the end, the nearest method to Bayesian one is the Maximum Likelihood, which is free of calibration as our approach requires. 13 Performing estimations of the benchmark VAR model with a three-state specification testifies for the existence of only two significant different regimes, hence our choice of modeling a two-state specification. Results for the three-state specification do not provide additional useful information and are available from the authors upon request.
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1 0.8 0.6 0.4 0.2 0 1987
1991
1995
1999
2003
2007
1991
1995
1999
2003
2007
1 0.8 0.6 0.4 0.2 0 1987
Fig. 1. Switching coefficients—3-equation VAR with Output gap. Note: Figures report the respective probability of occurrence across time of each regime estimated with the Markov-Switching procedure with switching coefficients, intercepts and variances for the benchmark VAR with output gap, inflation and the interest rate. The coefficients above each figure are those of the interest rate equation estimated for each regime: respectively the degree of inertia, the response to inflation and the response to output gap. The gray interval presents the IT adoption transition period.
Fig. 1 displays the implied state probabilities for the 3-equation VAR. We specify for each regime only the coefficients of response in the interest equation.14 Table 1 reports the individual coefficients of the VAR and Table 2 the transition matrices. The first regime, as defined by the estimation, shows a weak response of the interest rate to inflation and output gap, while regime 2 exhibits a strong response to inflation but a weaker response than regime 1 to output. Regime 2 occurs before IT was adopted whereas regime 1 occurs afterwards. Indeed, a change in the conduct of monetary policy has taken place with the adoption of IT. Moreover, the coefficient of response of the central bank reference rate to inflation is quite high: 2.4 in the regime preceding IT (Regime 2) and superior to the IT one: 0.2 (Regime 1). These results run counter to the usual statement that central banks under IT react toughly to inflation deviations from the target. The date of the switch and the lower response to inflation are robust to the following robustness check. As a matter of fact, the sign of the coefficient on the output gap in the first specification is opposite to what is usually expected and calls for further investigation. An alternative specification consists in replacing the output gap with the unemployment rate. It confirms our first set of results and exhibits the expected sign for the unemployment rate (Fig. 2 and Table 1). The two regimes are not intertwined: regime 2 occurs until IT has been adopted, but it is no longer apparent afterwards, whereas regime 1 started occurring as soon as IT has been adopted. Second, the lower reactivity of monetary policy to inflation deviations under IT (Regime 1) in comparison with the pre-IT period (Regime 2) remains. Under this specification, monetary policy is shown to have been less prone to react to inflation deviations and to a change in the unemployment rate after IT was adopted. We will later interpret this result in terms of enhanced credibility in the IT framework and better anchoring of expectations. 4.2. Estimates with fixed coefficients It appears from the data that there have been changes in intercepts and variances of inflation, the output gap and the central bank interest rate between the beginning and the end of the sample. Because the Swedish crises at the beginning of the 1990s were deep – the European Monetary System crisis, a Swedish and Finnish financial crisis and the disruption of the USSR – it is necessary to take into account the changes in the variances of the model.15 This was done in the previous sub-section with switching coefficients, intercepts and variances. One further step is to assess specifically the changes in the
14 Coefficients of response are ‘‘artificial long run responses’’ of the policy rate to both objectives of monetary policy, and they have been computed as in Sims and Zha (2006), using the same confidence interval at 68%. ρ, βπ , βy , correspond respectively to the AR coefficient, the long run coefficient on inflation and the long run coefficient on either the output gap or the unemployment rate, in the interest rate equation. According to SZ, ‘‘(artificial long run responses) are neither an equilibrium outcome nor multivariate impulse responses, but are calculated from the policy reaction function alone, asking what would be the permanent response in (the policy rate) to a permanent increase in the level or rate of change of the variable in question, if all other variables remained constant’’. 15 It appears from the literature (see, e.g. Lindbeck, 1997) that these macro shocks urged a shift in the Swedish economic policy setting, hence the adoption of inflation targeting. In this view, the regime switch in 1993 estimated in the first specification with full changes reveals that the 3-equation model fits well the evolution of the macroeconomic environment in Sweden.
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Table 1 Individual coefficients.
Note: standard errors are in parenthesis. * p < 0.3. This table provides the coefficients of the interest rate equation of the benchmark VAR with output gap, inflation and interest rate (first column), the VAR with unemployment rate, inflation and interest rate (second column). These two VAR are estimated with switching coefficients, intercepts and variances. In the third column, the benchmark VAR is estimated with fixed coefficients and switching intercepts and variances. The long run responses are the coefficients of response of the policy rate to both objectives of monetary policy, and they have been computed as in Sims and Zha (2006). The long-run responses for inflation are annualized to match the annual rate of interest.
Table 2 Matrix of Markovian transition probabilities P [i, j]. Switching coefficients—with output gap 0.902 0.098
0.393 0.607
Switching coefficients—with unemployment 0.932 0.068
0.271 0.729
Note: the probability to switch from regime 1 to 1 is given in the case [1,1], from 1 to 2 in the case [1,2], etc. The upper matrix presents the probabilities for the benchmark VAR with output gap, inflation and interest rate, while the lower one presents the VAR with the unemployment rate, inflation and interest rate.
variances and intercepts only of the model. Indeed, Benati (2008) reports evidence of changes in the level and persistence of inflation for inflation targeting countries when they adopted this framework. In our benchmark VAR specification with fixed-coefficients and switching intercepts and variances, we assess whether and when the change of monetary policy framework has induced changes in the intercept and disturbances term of inflation and output gap equations, while keeping constant across regimes the coefficients of all 3 equations, including therefore the ‘‘policy’’ coefficients of the interest rate equation. The IT framework assumes that the monetary policy regime can mix discipline and flexibility and can reduce overall variability (Kuttner, 2004). Discipline, in the vein of Barro and Gordon (1983), should refrain central banks (or government in their seminal framework) from using their instruments, hence taming their ‘inflation bias’. Consequently, deviations of variables from their steady-state values would be minimal. Policy flexibility after a shock, insofar as it cushions the shock and helps variables converge towards the steady-state, produces the same effect. All in all, IT should produce lower uncertainty regarding inflation and output. Within a setting where all coefficients remain constant, the reducing-uncertainty property of
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1 0.8 0.6 0.4 0.2 0 1987
1991
1995
1999
2003
2007
1991
1995
1999
2003
2007
1 0.8 0.6 0.4 0.2 0 1987
Fig. 2. Switching coefficients—3-equation VAR with unemployment. Note: Same as for Fig. 1, except that the VAR comprises the unemployment rate, inflation and the interest rate.
1 0.8 0.6 0.4 0.2 0 1987
1991
1995
1999
2003
1991
1995
1999
2003
2007
1 0.8 0.6 0.4 0.2 0 1987
2007
Fig. 3. Fixed coefficients—3-equation VAR with Output gap. Note: Same as for Fig. 1, except that the benchmark VAR is estimated with fixed coefficients and switching intercepts and variances.
IT can be captured by the values of the constant terms and the variances in the inflation and output gap equations. Restraining to variance only may hide sharp moves in inflation or output gap that would appear in the constant term. We then test the null hypothesis that the adoption of IT has led to a reduction of uncertainty on inflation and output gap. Under this specification with fixed coefficients (Fig. 3 and Tables 1 and 3), the interest rate equation gives a higher weight on inflation deviations than on the output gap. Moreover, the sign of the coefficient on the output gap is as expected. Regime 1 emerges steeply and precisely with the adoption of IT and occurred quite steadily since then. It is characterized by a reduced uncertainty compared to regime 2, as shown by Table 3 that reports the values of the constant terms and the variances. Actually, it appears that the regime which has occurred under IT testifies for a smaller uncertainty than the regime which occurred before IT adoption. The simultaneous occurrence of lower inflation and output variabilities, in opposition to the Rogoff (1985) usual trade-off, sheds light on the properties of the IT framework in Sweden. This result is all the more striking that the steep switch to the new regime postdates a strong reduction of the level and volatility of inflation that occurred in Sweden before IT adoption. Indeed, there is a break in the inflation series in 1991 (Fig. 4).
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J. Creel, P. Hubert / Research in Economics 66 (2012) 33–44 Table 3 Intercepts and variance in the VAR specification with fixed coefficients. Regime 1
Regime 2
Intercept Inflation equation
0.014 (0.025) 0.022* (0.021)
Output gap equation
Variance
Intercept
Variance
0.019* (0.002)
0.127 (0.147) −0.005 (0.130)
0.770* (0.131)
Note: standard error in parentheses. * p < 0.3. This table plots the value of the intercept and the variance of the error term of both the inflation and the output gap equations in the benchmark VAR including output gap, inflation and the interest rate, estimated with fixed coefficients.
18 16 14 12 10 8 6 4 2 0 1987
1991
1995
1999
2003
2007
1991
1995
1999
2003
2007
10 9 8 7 6 5 4 3 2 1 0 –1 1987 4 2 0 –2 –4 –6 –8 1987
1991
1995
1999
2003
2007
Fig. 4. Historical values (solid line) versus counterfactuals (dashed line). Source: Historical values come from the Sveriges Riksbank (interest rate), Statistics Sweden (inflation rate) and the OECD (output gap). All are in percent.
4.3. Counterfactuals Counterfactuals are set up in order to investigate IT outcomes on the inflation rate, on the sacrifice ratio of achieving low inflation and on the level of the policy rate. We assess how and the extent to which the paths of the policy rate, the inflation rate and the output gap have changed over time. In this exercise, we artificially set, since the beginning of the sample, the three equations with the fixed coefficients and the values of intercepts and variances of the regime concomitant with IT. To obtain the path of the policy instrument as it would have evolved, had IT been adopted at the beginning of the sample, we simulate the policy equation without the disturbance term. With this simulated rule, we compute the corresponding inflation rate and output gap over the full sample. These counterfactuals do not provide assessment of the stance of monetary policy across time per se, but they suggest levels of the variables and the macroeconomic effect of the IT framework. Results are reported on Fig. 4. In the following, we compare the situation where IT regime would have always prevailed with the actual evolution of the different endogenous variables. Main outcomes are twofold. First, interest rates would have been relatively low earlier, and then they would have been higher since 1996. We interpret this latter result as a higher monetary leeway enabling to face a negative shock with a sharp fall in the interest rate. This interpretation is confirmed by the evolution of the actual inflation rate: disinflation would have occurred earlier had IT been adopted earlier also, but
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inflation rates would have then been higher after 1993. Thus the scope for achieving low real interest rates would have been higher in the most recent period. Second, the evolution of the output gap would have been similar had IT always been implemented; thus, this result does not point to an increase in the sacrifice ratio under IT regime. 4.4. Discussion To sum up, our results suggest the following conclusions: IT has produced a shift in the conduct of monetary policy, from tough to smooth reaction to inflation deviations, and a decrease in overall uncertainty through lower inflation and output variabilities simultaneously precisely when IT was adopted and long after the ‘‘Great Moderation’’, which we can interpret as testifying for a better anchoring of expectations under IT. Our estimates of artificial long run responses of the policy rate to macro variables are in line with the weights estimated for Sveriges Riksbank’ objectives in the literature. The revealed relative high weight on inflation vis-à-vis the output gap in the specification with fixed coefficients is comparable to results reported by Berg et al. (2004), Cecchetti et al. (2002), Kuttner (2004) and Muscatelli et al. (2002). Whereas the former article used output growth rather than the output gap, the latter two articles were unable to find either a significant impact of the output gap on the policy rate or the ‘correct’ sign for its coefficient in the policy reaction function, a problem we met in the specification with switching coefficients. Estimates obtained for our long run policy responses suggest that the underlying model is very close to those usually found in the literature. We depart from these papers in that we assess the extent to which adopting IT can be considered as a new regime, whether a new policy regime or a new regime related to overall macroeconomic uncertainty. We show that a new policy regime happened after IT adoption and that it was characterized by lower reactivity to usual components of a monetary policy rule. We show that IT adoption has been concomitant with lower overall uncertainty and that margins for maneuver have been raised. The result according to which variance of inflation and variance of output gap have been lower in Sweden since IT adoption can be linked to earlier tests of the incidence of central bank independence on the variance of growth. Using a panel of twenty industrial countries that included Sweden, Eijffinger et al. (1998)16 concluded that ‘‘a higher degree of central bank independence is not associated with greater variation of real economic growth rates’’ (p. 86). They also showed that over the most recent subsample (1982–1992), ‘‘an increase in central bank independence leads to a larger expected reduction of the variance of output growth (. . . ) than in the first (subsample)’’ (p. 86). In the end, reduction of inflation that stems from central bank independence has no costs in terms of growth. Fischer (1995, p. 205) argues that this inconsistency with the Rogoff (1985) model has three potential explanations: first, independent central banks (or IT central banks in the context of the present paper) come closer to the ‘‘stabilizationefficiency frontier’’; second, fiscal policy is more disciplined in countries where central banks are independent; and third, shocks might differ from country to country. As regards the second point, it is straightforward to show that between 1994 and 2000, fiscal policy in Sweden was drastically reversed, from a fiscal deficit of almost 12% of GDP to a fiscal surplus of 4% of GDP: the argument of fiscal discipline can therefore be applied in the Swedish context. As regards the third point, data from the OECD show that (labor) productivity growth in Sweden has accelerated after IT was adopted: the annual average growth was 2.5 between 1993 and 2007, whereas it was only 1.2 between 1987 and 1992. Anyway, its variance was exactly the same whatever the period: 0.8. This latter fact lessens the argument that productivity shocks in Sweden since IT adoption might have led to a trade-off between inflation variance and output variance. Rogoff (1985) already acknowledged that ‘‘in the absence of productivity disturbances, inflation-rate targeting works extremely well, since there is then no tradeoff with employment stabilization’’. (p. 1181) Now, as regards the ‘‘stabilization-efficiency’’ (related here to the sacrifice ratio) of Swedish central bankers, our results seem to confirm Swedish central bankers’ own assessment of the success of IT adoption (see Heikensten, 2005; Ingves, 2007; Svensson, 2009). More precisely, all three above-mentioned central bankers acknowledge that flexibility is part of the means of the Riksbank of conducting monetary policy, and Svensson (2009) brings together his own theoretical development on so called ‘‘flexible inflation targeting’’ (Svensson, 1999) and the Riksbank’s monetary policy under IT. He acknowledges that theory and practice are consistent one with the other, but he concludes that the Riksbank has to focus on enhancing the transparency of its flexible strategy and its method of forecasting the output gap. In a paper where they compare IT, potential IT and non-IT countries, Corbo et al. (2001) show that the sacrifice ratio during inflation stabilization in Sweden declined after IT adoption, and it was lower than the sacrifice ratio of an average of non-IT countries. 4.5. Does fiscal policy matter? We assess whether the inference from our MSVAR estimations on the IT regime is not biased by fiscal policy changes. Actually, there have been so many changes in fiscal policy that our estimates may not capture the role of the IT regime only.17 16 See also Walsh (2009). 17 We thank an anonymous referee for raising this issue.
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There has been a vast literature dedicated to the interactions between fiscal and monetary policies that, under the heading of the ‘‘Fiscal theory of the price level’’ (FTPL), concludes on the possibility that fiscal policy could be the driver of price setting. The key intuition of the FTPL is that, if current and future fiscal policies are set without concern for sustainability, the general price level will ‘‘jump’’ in order to fulfill the present value budget constraint (see e.g. Sims, 1994; Woodford, 1995). The inference on the IT regime, stemming from the existence of a stable regime from 1993 onwards in our estimations, could be over-emphasized if fiscal policy was shown to have induced a stable regime as well. It turns out that this argument can be overcome. First, institutional fiscal changes occurred in 1997, then 2007, hence after the adoption of an IT regime, and for the former change 4 years after data reveal a prominent empirical regime switch. Moreover, the data do not show a new regime switch around 1997, whatever the number of states we introduce.18 Second, the identification of the VAR model is well controlled for the inclusion or exclusion of fiscal variables. As a matter of fact, we performed two complementary VAR model, extending our benchmark VAR model either to the cyclically-adjusted primary balance19 or to public debt. In comparison with the benchmark model, the introduction of fiscal variables gives similar effects of monetary shocks.20 Third, Claeys (2008) assesses the changes in fiscal policy in Sweden using a MSVAR estimation and he finds that whatever fiscal rules he studies (debt ratio, fiscal surplus, fiscal spending and tax rules) there is no evidence of a regime switch in 1993 as documented by our estimation on the monetary side.21 This supports the idea that the effects of 1997 fiscal policy reform are not to be confused with the effects of the introduction of inflation targeting. Moreover, Claeys (2008) shows that there have been both active and passive fiscal regimes22 during the sample period defining the IT regime in Sweden (Regime 1 on Figs. 1–3). It therefore cannot be argued that the low reaction of monetary policy towards inflation has been made possible by passive fiscal policies, hence by a continuing stable fiscal regime. Finally, our main conclusion, that IT adoption has been able to improve economic performance in Sweden in terms of inflation and output, leading to a better anchoring of expectations, is consistent with Fregert and Jonung (2008). Both authors adopt a completely different approach from ours to assess IT in Sweden. They make use of the changes in the characteristics of collective wage agreements between 1908 and 2008 to review Sweden’s economic history. More noteworthy for our topic, they show that as soon as IT was announced, long term wage agreements soared: they conclude that ‘‘IT gained instant credibility’’. Two intertwined mechanisms may explain our main result. First, IT is meant to help anchor private inflation expectations (see Gürkaynak et al., 2010): hence, a central bank is able to control inflation without pursuing aggressive action towards inflation variations. Second, the central bank’s decision to lower inflation, in line with the official strategy of Swedish governments since 1991, may have led to low and stable inflation and hence to a lower observed response to inflation. What might have been pure ‘window-dressing’ led to credibility enhancement. The credibility of the monetary policy framework change produced changes in inflation expectations and inflation process (see Benati, 2008; Fregert and Jonung, 2008). The decrease in the weight put on inflation is therefore consistent with changes in the structure of the economy as well as with the decrease in inflation and output variabilities. 5. Conclusion In this paper, we study empirically whether IT adoption in Sweden has constituted a switch in the conduct of monetary policy and has reduced macroeconomic uncertainty. The MSVAR method has revealed that a steep regime shift did occur and that the adoption of inflation targeting in Sweden has not given rise to a sole focus on inflation. Sweden has thus moved to a regime with a lower response to inflation under IT. Moreover, a change is clearly visible as far as overall macroeconomic uncertainty is concerned: under IT, intercepts and variances of key variables are lower than under the regime preceding IT adoption. Since Sweden has enhanced monetary policy transparency it may have succeeded in anchoring expectations. Finally, a counterfactual exercise has shown that IT could give more monetary leeway, i.e. the Riksbank under an IT regime can achieve a lower real interest rate, to be compared with the situation where the central bank would not be under IT. In the face of a negative shock, this potentially low real interest rate can be viewed as a further gain associated with the adoption of IT, while the paper shows that this framework does not imply the sole focus on inflation. It is noteworthy that higher policy leeway would not have led to a sacrifice in terms of the output. This is in line with Walsh (2009)’s recent synthesis that ‘‘inflation targeting central banks have not experienced greater output volatility’’. Appendix See Fig. A.1. 18 See footnote 13. 19 This fiscal indicator is netted out of changes in the output gap and the interest rate; it is a good indicator of the discretionary fiscal stance. Hence, it can reveal changes in the fiscal process. 20 See Fig. A.1. in the Appendix. 21 Bi and Leeper (2010) assume a break in fiscal rules in 1997 based on the new fiscal reform whereas Claeys (2008) identifies break points in 1995 or 1996 depending on the fiscal instruments. 22 Passive (active) fiscal regimes correspond to a situation in which fiscal policy is (not) set in accordance with the fulfillment of their present value budget constraint, whereas active (passive) monetary regimes correspond to strong (weak) reaction to inflation (see Leeper, 1991).
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Fig. A.1. Responses to a Monetary Shock using different VAR specifications. Note: Estimates based on the benchmark VAR with output gap, inflation and interest rate (first column), the benchmark VAR augmented with the cyclically-adjusted primary government deficit (second column) and the benchmark VAR augmented with the government debt in percent of GDP (third column). The dotted lines represent the 95% confidence intervals. The impulse response corresponds to a percentage point change in the variables of interest, in response to a one percentage point increase in the central bank interest rate.
References Alexius, A., Holmlund, B., 2008. Monetary policy and Swedish unemployment fluctuations. Economics—The Open-Access, Open-Assessment E-Journal 2 (4), 1–25. Ammer, J., Freeman, R.T., 1995. Inflation targeting in the 1990s: the experiences of New Zealand, Canada, and the United Kingdom. Journal of Economics and Business 47, 165–192. Angeriz, A., Arestis, P., 2007. Monetary policy in the UK. Cambridge Journal of Economics 31 (6), 863–884. Assenmacher-Wesche, K., 2006. Estimating central banks’ preferences from a time-varying empirical reaction function. European Economic Review 50, 1951–1974. Ball, L., Sheridan, N., 2003. Does inflation targeting matter? In: Bernanke, B., Woodford, M. (Eds.), The Inflation Targeting Debate. NBER. Barro, R., Gordon, D.B., 1983. A positive theory of monetary policy in a natural rate model. Journal of Political Economy 91 (4), 589–610. Benati, L., 2008. Investigating inflation persistence across monetary regimes. Quarterly Journal of Economics 123, 1005–1060. Berg, C., Jansson, P., Vredin, A., 2004. How useful are simple rules for monetary policy? The Swedish experience. Sveriges Riksbank Working Paper. 169. August, pp. 1–39. Bernanke, B.S., Laubach, T., Mishkin, F.S., Posen, A.S., 1999. Inflation Targeting, Lessons from the International Experience. Princeton University Press. Bi, H., Leeper, E.M., 2010. Sovereign debt risk premia and fiscal policy in Sweden, Rapport till Finanspolitiska radet, 3. Calmfors, L., 2011. The Swedish fiscal policy council. Seminar Paper 769. Institute for International Economic Studies. Cecchetti, S.G., McConnel, M.M., Perez-Quiros, G., 2002. Policymakers’ revealed preferences and the output-inflation variability trade-off: implications for the ESCB. The Manchester School 70 (4), 596–618 (special issue).
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Claeys, P., 2008. Rules, and their effects on fiscal policy in Sweden. Swedish Economic Policy Review 15, 7–47. Corbo, V., Landerretche, O., Schmidt-Hebbel, K., 2001. Assessing inflation targeting after a decade of world experience. International Journal of Finance & Economics 6, 343–368. Davis, S.J., Kahn, J.A., 2008. Interpreting the great moderation: changes in the volatility of economic activity at the macro and micro levels. The Journal of Economic Perspectives 22 (4), 155–180. Fall. Eijffinger, S., Schaling, E., Hoeberichts, M., 1998. Central bank independence: a sensitivity analysis. European Journal of Political Economy 14, 73–88. Fischer, S., 1995. Central-bank independence revisited. American Economic Review: Papers and Proceedings 85 (2), 201–206. Fregert, K., Jonung, L., 2008. Inflation targeting is a success, so far: 100 years of evidence from Swedish wage contracts. Economics—The Open-Access, Open-Assessment E-Journal 2 (31), 1–25. Friedman, B.M., 2004. Why the Federal reserve should not adopt inflation targeting. International Finance 7 (1), 129–136. Gertler, M., 2003. Comment. In: Bernanke, B., Woodford, M. (Eds.), The Inflation Targeting Debate. NBER. Gonzalez Cabanillas, L., Ruscher, E., 2008. The great moderation in the Euro area: what role have macroeconomic policies played? European Economy— Economic Papers 331, 1–31. Gürkaynak, R., Levin, A., Swanson, E., 2010. Does inflation targeting anchor Long Run inflation expectations? Evidence from long-term bond yields in the US, UK and Sweden. Journal of the European Economic Association 8 (6), 1208–1242. Hamilton, J., 1994. Time Series Analysis. Princeton University Press. Heikensten, L., 2005. The past ten years—experiences and conclusions. Sveriges Riksbank Economic Review 4, 5–14. Ingves, S., 2007. Swedish experiences of monetary policy with an inflation target. In: Speech at a Conference on Inflation Targeting in Budapest. Hungary, 19 January, pp. 1–9. Jacobson, T., Jansson, P., Vredin, A., Warne, A., 2001. Monetary policy analysis and inflation targeting in a small open economy: a VAR approach. Journal of Applied Econometrics 16, 487–520. Johnson, D., 2002. The effect of inflation targeting on the behaviour of expected inflation: evidence from an 11 country panel. Journal of Monetary Economics 49, 1521–1538. Kuttner, K.N., 2004. A snapshot of inflation targeting in its adolescence. In: Kent, C., Guttmann, S. (Eds.), The Future of Inflation Targeting. Reserve Bank of Australia. Leeper, E., 1991. Equilibria under ‘active’ and ‘passive’ monetary and fiscal policies. Journal of Monetary Economics 27 (1), 129–147. Leijonhufvud, A., 2007. Monetary and financial stability. Center for Economic Policy Research Policy Insight No. 14. Levin, A.T., Natalucci, F.M., Piger, J.M., 2004. The macroeconomic effects of inflation targeting. Federal Reserve Bank of St. Louis Review 86 (4), 51–80. Lin, S., Ye, H., 2007. Does inflation targeting really make a difference? Evaluating the treatment effect of inflation targeting in seven industrial countries. Journal of Monetary Economics 54 (8), 2521–2533. Lindbeck, A., 1997. The Swedish experiment. Journal of Economic Literature 35, 1273–1319. Lindh, Y., Ohlsson, H., 2000. Trends in Swedish public finances—past and future. Sveriges Riksbank Economic Review 4, 81–100. Mihov, I., Rose, A., 2008. Is old money better than new? Duration and monetary regimes. Economics—The Open-Access, Open-Assessment E-Journal 1–24. (special issue) ‘‘Recent developments in international money and finance’’, R. MacDonald (Ed.). Muscatelli, V.A., Tirelli, P., Trecroci, C., 2002. Does institutional change really matter? Inflation targets, central bank reform and interest rate policy in the OECD countries. Manchester School 70 (4), 487–527. (special issue). Orphanides, A., van Norden, S., 2002. The unreliability of output-gap estimates in real time. The Review of Economics and Statistics 84 (4), 569–583. Orphanides, A., Williams, J.C., 2002. Robust monetary policy rules with unknown natural rates. Brookings Papers on Economic Activity 2, 63–145. Rogoff, K., 1985. The optimal degree of commitment to an intermediate monetary target. Quarterly Journal of Economics 100 (4), 1169–1189. Sims, C.A., 1994. A simple model for the determination of the price level and the interaction of monetary and fiscal policy. Economic Theory 4 (3), 381–399. Sims, C.A., Zha, T., 2006. Were there regime switches in US monetary policy? American Economic Review 36 (1), 54–81. Summers, P.M., 2005. What caused the great moderation? Some cross-country evidence. FRB of Kansas City Economic Review (Third Quarter), 5–32. Svensson, L.E.O., 1999. Inflation targeting: some extensions. Scandinavian Journal of Economics 101, 337–361. Svensson, L.E.O., 2009. Transparency under flexible inflation targeting: experiences and challenges. Sveriges Riksbank Economic Review 1, 5–44. Walsh, C., 2009. Inflation targeting: what have we learned? International Finance 12 (2), 195–233. Woodford, M., 1995. Price level determinacy without control of a monetary aggregate. In: Carnegie-Rochester Conference Series on Public Policy, vol. 43, pp. 1–46.
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Research in Economics journal homepage: www.elsevier.com/locate/rie
Constrained discretion in Sweden✩ Jérôme Creel a,b,∗ , Paul Hubert b a
ESCP Europe, 79, avenue de la République, 75011 Paris, France
b
OFCE-Sciences Po, 69, quai d’Orsay, 75340 Paris Cedex 07, France
article
info
Article history: Received 17 January 2011 Accepted 25 August 2011 Keywords: Monetary policy Inflation targeting MSVAR Regime-switching Sweden
abstract We study whether the institutional adoption of inflation targeting (IT) has constituted both a policy and a macroeconomic switch in Sweden using the nonlinear MSVAR technique. We assess the relative weight put on inflation in the monetary reaction function and the capacity of IT to reduce macroeconomic uncertainty. We show that IT has constituted a policy switch to a lower focus on inflation, in contrast with the usual argument that has been put forth by IT opponents. Moreover, IT adoption is shown to have reduced uncertainty, through lower inflation and output variabilities simultaneously. Last, counterfactuals suggest IT provides higher monetary policy leeway. © 2011 University of Venice. Published by Elsevier Ltd. All rights reserved.
1. Introduction In the 1990s, Sweden experienced an institutional policy shift, from a fixed exchange rate regime to a floating exchange rate regime with an inflation target. The fixed exchange rate was abandoned in November 1992 and an inflation target was formulated in 1993, and planned to be effective from 1995 onwards. In addition, there have been two institutional shifts in fiscal policy, one with the introduction of fiscal rules in 1997 (see, e.g. Lindh and Ohlsson, 2000) and another with the creation of the Swedish fiscal policy council in 2007 (see, e.g. Calmfors, 2011). All these shifts were consistent with the greater emphasis on price stability adopted by Swedish governments since 1991 (see, e.g. Lindbeck, 1997). In this paper, we study the implications of the adoption of the inflation targeting (IT) regime. We investigate whether there is empirical evidence for a regime switch and whether this institutional switch has been associated with changes in the conduct of monetary policy, and in inflation and output processes. To this end, we rely on a multivariate regime-switching method (Markov-Switching VAR, hereafter MSVAR). Our paper has been motivated by three issues that this method can handle. The first two of them concern the evaluation of IT characteristics, while the third is methodological. First, the regime-switching method allows assessing changes in the conduct of monetary policy after the adoption of this framework, as IT has generally been assumed to focus on inflation.1 Our first test then focuses on changes in coefficients in order to assess potential modification of monetary policy rules.2 Moreover, ✩ The authors are very grateful to an anonymous referee for her/his careful reading, comments and remarks on an earlier draft. This paper benefited from funding by the European Community’s Seventh Framework Programme (FP7/2007-2013) under Socio-economic Sciences and Humanities, grant agreement no. 225408 (POLHIA). ∗ Corresponding author. Tel.: +33 1 44 18 54 56; fax: +33 1 44 18 54 78. E-mail address: [email protected] (J. Creel). 1 Friedman (2004), Leijonhufvud (2007) and Walsh (2009) among others have argued that IT places too much emphasis on inflation, potentially at the
expense of other monetary policy goals. At the opposite, Bernanke et al. (1999) and others have advocated inflation targeting as a general framing of monetary policymaking rather than a rule, encompassing clear targets, accountable policymakers and a flexible strategy. 2 At this stage, the paper does not differ from papers which estimate changes in monetary policy rules after IT adoption (see e.g. Kuttner, 2004; Muscatelli et al., 2002), except that we introduce a multivariate framework. Moreover, as stressed by Sims and Zha (2006), standard estimations generally do not capture multiple shifts in variance because they do not make enough allowance for heteroskedasticity; and identification of forward-looking policy rules is generally fragile. MSVAR thus improve estimation outcomes. 1090-9443/$ – see front matter © 2011 University of Venice. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.rie.2011.08.002
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IT is not about interest rate rules per se but it is a practical framework which promotes the optimality of constrained discretion for monetary policy, and MSVAR permits us to go further than checking for a change in regime that would only occur at the level of the monetary policy rule: changes in inflation and output processes are also investigated since the Swedish crisis at the beginning of the 1990s has produced heteroskedasticity. Second, IT implies forecast communication and transparency involvement in order to anchor public expectations (Gürkaynak et al., 2010). Thus, according to economic theory, anchoring expectations contributes to economic stability. Our second test with MSVAR therefore highlights, by keeping coefficients constant, changes in the macroeconomic environment, where the environment is defined by the reliability of the underlying model to forecast future outcomes.3 Hence, the environment is approximated by the estimation residuals. Third, at least one reason why the empirical literature has not shown a clear advantage of IT vs. non IT monetary policy, although no country ever quit IT,4 is that most articles endeavoring to conclude on the stability property of IT focused on a control group method during a period where differences between IT and non-IT countries were all insignificant (Gertler, 2003). In contrast, our starting point has been to focus on an individual country and to study changes over time that may have been implied by IT adoption. We use the non-linear multivariate MSVAR model methodology developed by Hamilton (1994) and applied by Assenmacher-Wesche (2006). Contrary to Sims and Zha (2006) who apply the methodology to US data, no subjective priors are introduced in the model and the number of endogenous variables is reduced. Our benchmark VAR model comprising the output gap, inflation and the central bank interest rate is estimated with two types of specifications: on the one hand, a specification with full changes, i.e. changes in coefficients, intercepts and variances of all 3 equations of the VAR; and, on the other hand, a specification where changes across ‘‘regimes’’ are only due to intercepts and variances, while coefficients of all 3 equations are kept fixed. The first specification enables us to assess the conduct of monetary policy through a standard backward-looking Taylor rule – the interest rate equation – whereas the second specification enables us to assess the changes in the structure of the economy. Our results contribute to the literature in the following respect. First, they suggest that IT adoption has constituted a change in the monetary policy rule, but not in a way that has generally been admitted in the literature: the new regime which has been consecutive to IT adoption has less focused on inflation than during the pre-IT period. Lower and more stable inflation rates with a less aggressive central bank suggest a high level of credibility and a high potential to target other objectives than inflation. Second, a steep switch in the macroeconomic environment is also visible: this switch has been concomitant with a reduction of uncertainty precisely when IT has been adopted. Drawing on an empirical assessment of the success of IT to anchor expectations in Sweden (Fregert and Jonung, 2008), the latter result suggests that the reduction in overall uncertainty pertaining to IT adoption may depend on the credibility of the new implemented regime through the increased transparency and communication of the Swedish central bank. Last, IT seems to offer some policy leeway: a counterfactual study approach suggests that central bank interest rate would have been higher had IT been adopted earlier. It is important to acknowledge that the paper does not constitute an assessment of the efficiency of monetary policy in Sweden. Hence, our tests do not address the debate on the Great Moderation. The latter is usually associated with the great decline in output, employment and inflation volatility and attributed to more efficient monetary policy, increased globalization, better inventory policies and/or ‘‘good luck’’ (see Davis and Kahn, 2008, for a critical empirical review of these arguments).5 Our results are not blurred by the debate around the Great Moderation which predates our sample. First of all, we do not investigate the reasons for the decline in inflation; rather, we focus on the relationship between the inflation rate and the policy instrument, without any judgment on its optimality over time. We simply analyze the changes in monetary policy which have occurred since IT adoption in the early 1990s. Second, the regime switch which is clearly apparent from our estimation after IT was adopted in Sweden is not consistent with the estimated date of the steep shift in inflation and GDP volatility, either in industrialized economies (early 1980s, see e.g. Summers, 2005) or in Sweden (early 1980s, see Gonzalez Cabanillas and Ruscher, 2008). Two intertwined mechanisms may explain our main result. First, IT is meant to help anchor private inflation expectations (see Gürkaynak et al., 2010), which will enable a central bank to control inflation without pursuing aggressive action towards inflation variations. Second, the central bank’s decision to lower inflation may have led to low and stable inflation and hence to a lower response to inflation. The credibility of the monetary policy framework change may have thus led to changes in inflation expectations and inflation process (see Benati, 2008; Fregert and Jonung, 2008). The decrease in the weight put on inflation is therefore consistent with changes in the structure of the economy and the decrease in inflation and output variabilities. Our interpretation of results is related to the advantages of IT advocated by Bernanke et al. (1999) and Svensson (1999) where IT lies somewhere between rules and discretion, labeled ‘‘constrained discretion’’. Discipline arises from anchoring 3 Walsh (2009) has a three-dimension characterization of ‘‘monetary policy environment’’: constraints, objectives and beliefs. Our first set of estimations, with changes in coefficients, endorses the objectives dimension, whereas our second set of estimations endorses the beliefs dimension. 4 Papers focusing on the impact of IT either did so through the lens of expectations (e.g. Fregert and Jonung, 2008; Gürkaynak et al., 2010; Johnson, 2002; Levin et al., 2004) or assessed the stability of monetary objectives (e.g. Angeriz and Arestis, 2007; Ball and Sheridan, 2003; Lin and Ye, 2007). The former showed that in comparison with non-IT countries, IT countries have been able to better anchor long-run inflation expectations. The latter generally concluded that IT countries have performed as well as non-IT countries. The fact that no country ever quit IT is a forceful argument by Mihov and Rose (2008) about the durability of IT regimes. 5 Davis and Kahn (2008) use US micro data and conclude that improved supply-chain management (or better inventory controls) is the most prominent cause of the Great Moderation. They also show that no decline in uncertainty for the households can be associated with the Great Moderation.
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expectations thanks to the publicly announced inflation target range. It also permits some flexibility: deviations from the target do not incur a loss of credibility and reputation provided the reasons for the deviations are explained to the public. This flexibility is meant to give some leeway to monetary policy. It is noteworthy that the ability of monetary policy to stabilize inflation at a low level do not always lead to Rogoff’s (1985) trade-off between lower inflation variability and higher output variability.6 The remainder of the paper is organized as follows. Section 2 deals with the data. In Section 3, the regime-switching method is presented. Section 4 displays and comments results. Robustness checks and counterfactuals are performed, and our main results are confronted with the literature. Section 5 summarizes our main conclusions. 2. Data Reasons to test potential IT impact in an economy like Sweden are manifold. First, IT was adopted relatively early (in January 1993, with the objective to be fully applied in January 1995), but contrary to some countries like New Zealand, which adopted IT before Sweden, the Swedish inflation target has remained the same since the beginning of the IT regime and no decelerating path of inflation occurred. Second, Sweden is still under an IT regime, whereas Finland and Spain adopted the Euro in 1999. Third, unlike Chile and Israel, Sweden is a developed country: lessons from its IT experience may be of interest for its US or European counterparts.7 Fourth, the Swedish case remains specific: as Fregert and Jonung (2008) showed, the credibility of IT was immediate after it was announced and inflation rates were considerably reduced short after its adoption. We use monthly data from 1987:1 to 2007:12. The interest rate is the central bank key interest rate.8 (source: Sveriges Riksbank) The inflation rate expressed in month-over-month growth rate is the measure of inflation targeted by the Sveriges Riksbank: UND1X, which corresponds to consumer inflation (source: Statistics Sweden). The output gap measure is the interpolated monthly measure of the OECD.9 The inflation variable is expressed in first difference of the log of the price index and all variables in the VAR and MSVAR are expressed in percent. Each model includes 4 lags according to usual statistical tests. Contrary to Sims and Zha (Sims and Zha, 2006) who always include a monetary aggregate, commodity prices and the unemployment rate, our benchmark VAR specification focuses on a smaller structural model comprising three endogenous variables10 : the output gap (or the unemployment rate), the nominal short-run interest rate and the officially-targeted inflation rate. The inflation measure UND1X excludes households’ mortgage interest payments and the direct effects of changes in indirect taxes and subsidies. This underlying inflation does not exclude energy and fresh food prices or imported inflation. The focus on this consumer inflation index targeted by the Swedish central bank enables to take into account in the analysis the effects of the exchange rate, potentially important in a small open economy like Sweden, and the focus of the Riksbank on the exchange rate throughout the sample. 3. Method A strand of the literature making use of Markov-Switching VAR (MSVAR) has developed in macroeconomics and permits to date breaks and new regimes while letting data speak. It enables us to reveal the different regimes which have occurred in IT countries. Moreover, the choice of regimes rather than pure breaks allows checking the argument that disinflation policies had already existed in the past, either shortly before IT adoption or hand-in-hand with the ‘‘Great Moderation’’ process, i.e. exogenously to IT adoption. Ammer and Freeman (1995) estimated a canonical VAR whose sample stopped just before inflation targets were first announced, and then, they compared actual values for GDP, inflation, and the real interest rate with the (out-of-sample) forecast ones. They interpreted the difference between both variables – actual and forecast – as evidence of a change of regime. In contrast, using MSVAR technique with sufficiently-long samples after IT adoption can reveal a new regime rather than assume it. Our approach is very close to that of Assenmacher-Wesche (2006). 6 This point was already raised by Rogoff (1985) after he mentioned inflation-rate (or price level) targeting: the reason why the tradeoff does not apply lies in the specificities of constrained discretion. 7 Despite its numerical inflation target, the European Central Bank has not adopted an IT framework. 8 It is the Riksbank’s primary tool for influencing inflation. It corresponds to the marginal rate until 1994:6 and the repo rate afterwards. Given the suddenly and temporary spike of the central bank interest rate to 79.8% during the EMS crisis in 1992:9 which is uncorrelated to inflation and output gap levels at this date, we proceed to a linear smoothing interpolation between 1992:8 and 1992:10 to correct for this outlier. 9 For robustness purposes, we also use the unemployment rate that we substitute to the output gap in our estimations. The Swedish unemployment rate
is I (0) at the 5% level according to the KPSS test. Though it is generally acknowledged that the unemployment rate is better modeled as a I (1) process (the ADF test rejects non stationarity), we follow Orphanides and Williams (2002) and Orphanides and van Norden (2002) and check the robustness of results to the inclusion of the unemployment rate. 10 Alternative models with three to six variables have also been tested; they have always included the central bank interest rate, the inflation rate (either CPI, GDP deflator or UND1X), and the output gap, and they have been extended to real GDP, M2, energy prices and/or exchange rate. These latter variables do not improve the fit of the estimated first three variables. Moreover, the effects of a monetary shock are similar to those of the benchmark VAR and confirm that the latter is not misidentified when controlling for extra-variables. Disregarding these extra-variables has one major advantage: it saves some degrees of freedom, MSVARs being pretty much ‘‘data-consuming’’: the number of parameters to estimate depends on the number of variables, lags and states and can quickly be explosive. With n variables, p lags and m states, there are m × (n × (n × p + 1) + (n × (n − 1)/2)) parameters for the VAR plus m × (m − 1) elements in the transition probabilities matrix.
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The Markov-Switching VAR, as proposed by Hamilton (1994),11 allows the structural coefficients and the covariance matrix to be dependent on an unobserved state variable St which is assumed to follow a 1st order Markov chain. The joint distribution of the shocks can be non-constant across our sample periods. The general framework is described by the following equation:
yt = xt · βSt + ut t = 1, . . . , T ut |St ∼ N (0, ΣSt ) St = {1, . . . , M }
(1)
where yt = (y1,t , . . . , yp,t ) is an 1 × n vector of endogenous variables, with n the number of variables of interest, xt is an 1 × np vector of p lagged endogenous variables, St is an unobserved state, βSt is an np × 1 vector of parameters, T is the sample size and M the number of states (or regimes). This baseline equation of the model is free of restrictions. Since we do not know ex ante the possible changes of monetary policy effects implied by IT and because the empirical approach is data driven (i.e. we are looking for what data tell us about this framework setting aside any preconceived conclusions), we do not impose restrictions on parameters.12 The covariance matrix ΣSt takes the form:
ΣSt = σS2 (St ) · Ip . The transition probabilities matrix, noted P, is defined following Hamilton (1994): p11 p12
P = ..
.
p1M
··· ··· ··· ···
pM1 pM2
.. , .
pMM
with j=1 pkj = 1 and pkj ≥ 0, ∀k, j ∈ {1 . . . M }. Two models are tested:
∑M
yt = (1n , yt −1 , . . . , yt −p ) · βSt + ut yt = 1n · βSt + (yt −1 , . . . , yt −p ) · δ + ut and can be written as: yt = xt · βSt + z · δ + ut
(2)
M different models are then simultaneously estimated such as:
(1p ⊗ yt ) = xt · (β1 | · · · |βM ) + (1p ⊗ zt ) · δ + (u1t | · · · |uM t ) and lead for a given regime St = j to: yt = xt · βj + zt · δ + ut ut |St = j ∼ N (0, Σj ). Initial values of the vector of parameters are calculated. A conditional probability density function is defined according to the information set in t − 1. The model is recursively estimated through the maximum likelihood ‘‘EM’’ algorithm, starting from the unconditional density of yt which is calculated by summing conditional densities over possible values for St . The maximum likelihood estimates are finally obtained by maximizing the log-likelihood function and allow attaining the final matrix of parameters. 4. Results 4.1. Estimates with switching coefficients In this section, we present the key results of the benchmark VAR specification – comprising the output gap, inflation and the interest rate – with changes in coefficients, intercepts and variances of all 3 equations. It must be acknowledged that this benchmark VAR model provides the usual characterization of the effects of monetary policy shocks as can be seen in Fig. A.1 in the Appendix. Moreover, our identification of monetary shocks is consistent with the findings of Jacobson et al. (2001) and Alexius and Holmlund (2008) for Sweden. We estimate this 3-equation VAR through the Markov-Switching procedure with two states since we are interested in the institutional switch that occurred in Sweden: we are looking for the date break between two states and we question the correspondence of one of these states with the IT period. Robustness checks to the introduction of a third state have been performed and confirm our conclusions.13 11 See Hamilton (1994, Chapter 22) for a more in-depth details. 12 The ad hoc nature of restrictions is opposed to the seminal motivation of our methodology. The use of Bayesian techniques, though it represents a great advancement in structural estimation, runs up against the same motivation. Indeed, the link between estimation and calibration is strong and depends on subjective priors, which we chose not to use. In the end, the nearest method to Bayesian one is the Maximum Likelihood, which is free of calibration as our approach requires. 13 Performing estimations of the benchmark VAR model with a three-state specification testifies for the existence of only two significant different regimes, hence our choice of modeling a two-state specification. Results for the three-state specification do not provide additional useful information and are available from the authors upon request.
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1 0.8 0.6 0.4 0.2 0 1987
1991
1995
1999
2003
2007
1991
1995
1999
2003
2007
1 0.8 0.6 0.4 0.2 0 1987
Fig. 1. Switching coefficients—3-equation VAR with Output gap. Note: Figures report the respective probability of occurrence across time of each regime estimated with the Markov-Switching procedure with switching coefficients, intercepts and variances for the benchmark VAR with output gap, inflation and the interest rate. The coefficients above each figure are those of the interest rate equation estimated for each regime: respectively the degree of inertia, the response to inflation and the response to output gap. The gray interval presents the IT adoption transition period.
Fig. 1 displays the implied state probabilities for the 3-equation VAR. We specify for each regime only the coefficients of response in the interest equation.14 Table 1 reports the individual coefficients of the VAR and Table 2 the transition matrices. The first regime, as defined by the estimation, shows a weak response of the interest rate to inflation and output gap, while regime 2 exhibits a strong response to inflation but a weaker response than regime 1 to output. Regime 2 occurs before IT was adopted whereas regime 1 occurs afterwards. Indeed, a change in the conduct of monetary policy has taken place with the adoption of IT. Moreover, the coefficient of response of the central bank reference rate to inflation is quite high: 2.4 in the regime preceding IT (Regime 2) and superior to the IT one: 0.2 (Regime 1). These results run counter to the usual statement that central banks under IT react toughly to inflation deviations from the target. The date of the switch and the lower response to inflation are robust to the following robustness check. As a matter of fact, the sign of the coefficient on the output gap in the first specification is opposite to what is usually expected and calls for further investigation. An alternative specification consists in replacing the output gap with the unemployment rate. It confirms our first set of results and exhibits the expected sign for the unemployment rate (Fig. 2 and Table 1). The two regimes are not intertwined: regime 2 occurs until IT has been adopted, but it is no longer apparent afterwards, whereas regime 1 started occurring as soon as IT has been adopted. Second, the lower reactivity of monetary policy to inflation deviations under IT (Regime 1) in comparison with the pre-IT period (Regime 2) remains. Under this specification, monetary policy is shown to have been less prone to react to inflation deviations and to a change in the unemployment rate after IT was adopted. We will later interpret this result in terms of enhanced credibility in the IT framework and better anchoring of expectations. 4.2. Estimates with fixed coefficients It appears from the data that there have been changes in intercepts and variances of inflation, the output gap and the central bank interest rate between the beginning and the end of the sample. Because the Swedish crises at the beginning of the 1990s were deep – the European Monetary System crisis, a Swedish and Finnish financial crisis and the disruption of the USSR – it is necessary to take into account the changes in the variances of the model.15 This was done in the previous sub-section with switching coefficients, intercepts and variances. One further step is to assess specifically the changes in the
14 Coefficients of response are ‘‘artificial long run responses’’ of the policy rate to both objectives of monetary policy, and they have been computed as in Sims and Zha (2006), using the same confidence interval at 68%. ρ, βπ , βy , correspond respectively to the AR coefficient, the long run coefficient on inflation and the long run coefficient on either the output gap or the unemployment rate, in the interest rate equation. According to SZ, ‘‘(artificial long run responses) are neither an equilibrium outcome nor multivariate impulse responses, but are calculated from the policy reaction function alone, asking what would be the permanent response in (the policy rate) to a permanent increase in the level or rate of change of the variable in question, if all other variables remained constant’’. 15 It appears from the literature (see, e.g. Lindbeck, 1997) that these macro shocks urged a shift in the Swedish economic policy setting, hence the adoption of inflation targeting. In this view, the regime switch in 1993 estimated in the first specification with full changes reveals that the 3-equation model fits well the evolution of the macroeconomic environment in Sweden.
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Table 1 Individual coefficients.
Note: standard errors are in parenthesis. * p < 0.3. This table provides the coefficients of the interest rate equation of the benchmark VAR with output gap, inflation and interest rate (first column), the VAR with unemployment rate, inflation and interest rate (second column). These two VAR are estimated with switching coefficients, intercepts and variances. In the third column, the benchmark VAR is estimated with fixed coefficients and switching intercepts and variances. The long run responses are the coefficients of response of the policy rate to both objectives of monetary policy, and they have been computed as in Sims and Zha (2006). The long-run responses for inflation are annualized to match the annual rate of interest.
Table 2 Matrix of Markovian transition probabilities P [i, j]. Switching coefficients—with output gap 0.902 0.098
0.393 0.607
Switching coefficients—with unemployment 0.932 0.068
0.271 0.729
Note: the probability to switch from regime 1 to 1 is given in the case [1,1], from 1 to 2 in the case [1,2], etc. The upper matrix presents the probabilities for the benchmark VAR with output gap, inflation and interest rate, while the lower one presents the VAR with the unemployment rate, inflation and interest rate.
variances and intercepts only of the model. Indeed, Benati (2008) reports evidence of changes in the level and persistence of inflation for inflation targeting countries when they adopted this framework. In our benchmark VAR specification with fixed-coefficients and switching intercepts and variances, we assess whether and when the change of monetary policy framework has induced changes in the intercept and disturbances term of inflation and output gap equations, while keeping constant across regimes the coefficients of all 3 equations, including therefore the ‘‘policy’’ coefficients of the interest rate equation. The IT framework assumes that the monetary policy regime can mix discipline and flexibility and can reduce overall variability (Kuttner, 2004). Discipline, in the vein of Barro and Gordon (1983), should refrain central banks (or government in their seminal framework) from using their instruments, hence taming their ‘inflation bias’. Consequently, deviations of variables from their steady-state values would be minimal. Policy flexibility after a shock, insofar as it cushions the shock and helps variables converge towards the steady-state, produces the same effect. All in all, IT should produce lower uncertainty regarding inflation and output. Within a setting where all coefficients remain constant, the reducing-uncertainty property of
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1 0.8 0.6 0.4 0.2 0 1987
1991
1995
1999
2003
2007
1991
1995
1999
2003
2007
1 0.8 0.6 0.4 0.2 0 1987
Fig. 2. Switching coefficients—3-equation VAR with unemployment. Note: Same as for Fig. 1, except that the VAR comprises the unemployment rate, inflation and the interest rate.
1 0.8 0.6 0.4 0.2 0 1987
1991
1995
1999
2003
1991
1995
1999
2003
2007
1 0.8 0.6 0.4 0.2 0 1987
2007
Fig. 3. Fixed coefficients—3-equation VAR with Output gap. Note: Same as for Fig. 1, except that the benchmark VAR is estimated with fixed coefficients and switching intercepts and variances.
IT can be captured by the values of the constant terms and the variances in the inflation and output gap equations. Restraining to variance only may hide sharp moves in inflation or output gap that would appear in the constant term. We then test the null hypothesis that the adoption of IT has led to a reduction of uncertainty on inflation and output gap. Under this specification with fixed coefficients (Fig. 3 and Tables 1 and 3), the interest rate equation gives a higher weight on inflation deviations than on the output gap. Moreover, the sign of the coefficient on the output gap is as expected. Regime 1 emerges steeply and precisely with the adoption of IT and occurred quite steadily since then. It is characterized by a reduced uncertainty compared to regime 2, as shown by Table 3 that reports the values of the constant terms and the variances. Actually, it appears that the regime which has occurred under IT testifies for a smaller uncertainty than the regime which occurred before IT adoption. The simultaneous occurrence of lower inflation and output variabilities, in opposition to the Rogoff (1985) usual trade-off, sheds light on the properties of the IT framework in Sweden. This result is all the more striking that the steep switch to the new regime postdates a strong reduction of the level and volatility of inflation that occurred in Sweden before IT adoption. Indeed, there is a break in the inflation series in 1991 (Fig. 4).
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J. Creel, P. Hubert / Research in Economics 66 (2012) 33–44 Table 3 Intercepts and variance in the VAR specification with fixed coefficients. Regime 1
Regime 2
Intercept Inflation equation
0.014 (0.025) 0.022* (0.021)
Output gap equation
Variance
Intercept
Variance
0.019* (0.002)
0.127 (0.147) −0.005 (0.130)
0.770* (0.131)
Note: standard error in parentheses. * p < 0.3. This table plots the value of the intercept and the variance of the error term of both the inflation and the output gap equations in the benchmark VAR including output gap, inflation and the interest rate, estimated with fixed coefficients.
18 16 14 12 10 8 6 4 2 0 1987
1991
1995
1999
2003
2007
1991
1995
1999
2003
2007
10 9 8 7 6 5 4 3 2 1 0 –1 1987 4 2 0 –2 –4 –6 –8 1987
1991
1995
1999
2003
2007
Fig. 4. Historical values (solid line) versus counterfactuals (dashed line). Source: Historical values come from the Sveriges Riksbank (interest rate), Statistics Sweden (inflation rate) and the OECD (output gap). All are in percent.
4.3. Counterfactuals Counterfactuals are set up in order to investigate IT outcomes on the inflation rate, on the sacrifice ratio of achieving low inflation and on the level of the policy rate. We assess how and the extent to which the paths of the policy rate, the inflation rate and the output gap have changed over time. In this exercise, we artificially set, since the beginning of the sample, the three equations with the fixed coefficients and the values of intercepts and variances of the regime concomitant with IT. To obtain the path of the policy instrument as it would have evolved, had IT been adopted at the beginning of the sample, we simulate the policy equation without the disturbance term. With this simulated rule, we compute the corresponding inflation rate and output gap over the full sample. These counterfactuals do not provide assessment of the stance of monetary policy across time per se, but they suggest levels of the variables and the macroeconomic effect of the IT framework. Results are reported on Fig. 4. In the following, we compare the situation where IT regime would have always prevailed with the actual evolution of the different endogenous variables. Main outcomes are twofold. First, interest rates would have been relatively low earlier, and then they would have been higher since 1996. We interpret this latter result as a higher monetary leeway enabling to face a negative shock with a sharp fall in the interest rate. This interpretation is confirmed by the evolution of the actual inflation rate: disinflation would have occurred earlier had IT been adopted earlier also, but
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inflation rates would have then been higher after 1993. Thus the scope for achieving low real interest rates would have been higher in the most recent period. Second, the evolution of the output gap would have been similar had IT always been implemented; thus, this result does not point to an increase in the sacrifice ratio under IT regime. 4.4. Discussion To sum up, our results suggest the following conclusions: IT has produced a shift in the conduct of monetary policy, from tough to smooth reaction to inflation deviations, and a decrease in overall uncertainty through lower inflation and output variabilities simultaneously precisely when IT was adopted and long after the ‘‘Great Moderation’’, which we can interpret as testifying for a better anchoring of expectations under IT. Our estimates of artificial long run responses of the policy rate to macro variables are in line with the weights estimated for Sveriges Riksbank’ objectives in the literature. The revealed relative high weight on inflation vis-à-vis the output gap in the specification with fixed coefficients is comparable to results reported by Berg et al. (2004), Cecchetti et al. (2002), Kuttner (2004) and Muscatelli et al. (2002). Whereas the former article used output growth rather than the output gap, the latter two articles were unable to find either a significant impact of the output gap on the policy rate or the ‘correct’ sign for its coefficient in the policy reaction function, a problem we met in the specification with switching coefficients. Estimates obtained for our long run policy responses suggest that the underlying model is very close to those usually found in the literature. We depart from these papers in that we assess the extent to which adopting IT can be considered as a new regime, whether a new policy regime or a new regime related to overall macroeconomic uncertainty. We show that a new policy regime happened after IT adoption and that it was characterized by lower reactivity to usual components of a monetary policy rule. We show that IT adoption has been concomitant with lower overall uncertainty and that margins for maneuver have been raised. The result according to which variance of inflation and variance of output gap have been lower in Sweden since IT adoption can be linked to earlier tests of the incidence of central bank independence on the variance of growth. Using a panel of twenty industrial countries that included Sweden, Eijffinger et al. (1998)16 concluded that ‘‘a higher degree of central bank independence is not associated with greater variation of real economic growth rates’’ (p. 86). They also showed that over the most recent subsample (1982–1992), ‘‘an increase in central bank independence leads to a larger expected reduction of the variance of output growth (. . . ) than in the first (subsample)’’ (p. 86). In the end, reduction of inflation that stems from central bank independence has no costs in terms of growth. Fischer (1995, p. 205) argues that this inconsistency with the Rogoff (1985) model has three potential explanations: first, independent central banks (or IT central banks in the context of the present paper) come closer to the ‘‘stabilizationefficiency frontier’’; second, fiscal policy is more disciplined in countries where central banks are independent; and third, shocks might differ from country to country. As regards the second point, it is straightforward to show that between 1994 and 2000, fiscal policy in Sweden was drastically reversed, from a fiscal deficit of almost 12% of GDP to a fiscal surplus of 4% of GDP: the argument of fiscal discipline can therefore be applied in the Swedish context. As regards the third point, data from the OECD show that (labor) productivity growth in Sweden has accelerated after IT was adopted: the annual average growth was 2.5 between 1993 and 2007, whereas it was only 1.2 between 1987 and 1992. Anyway, its variance was exactly the same whatever the period: 0.8. This latter fact lessens the argument that productivity shocks in Sweden since IT adoption might have led to a trade-off between inflation variance and output variance. Rogoff (1985) already acknowledged that ‘‘in the absence of productivity disturbances, inflation-rate targeting works extremely well, since there is then no tradeoff with employment stabilization’’. (p. 1181) Now, as regards the ‘‘stabilization-efficiency’’ (related here to the sacrifice ratio) of Swedish central bankers, our results seem to confirm Swedish central bankers’ own assessment of the success of IT adoption (see Heikensten, 2005; Ingves, 2007; Svensson, 2009). More precisely, all three above-mentioned central bankers acknowledge that flexibility is part of the means of the Riksbank of conducting monetary policy, and Svensson (2009) brings together his own theoretical development on so called ‘‘flexible inflation targeting’’ (Svensson, 1999) and the Riksbank’s monetary policy under IT. He acknowledges that theory and practice are consistent one with the other, but he concludes that the Riksbank has to focus on enhancing the transparency of its flexible strategy and its method of forecasting the output gap. In a paper where they compare IT, potential IT and non-IT countries, Corbo et al. (2001) show that the sacrifice ratio during inflation stabilization in Sweden declined after IT adoption, and it was lower than the sacrifice ratio of an average of non-IT countries. 4.5. Does fiscal policy matter? We assess whether the inference from our MSVAR estimations on the IT regime is not biased by fiscal policy changes. Actually, there have been so many changes in fiscal policy that our estimates may not capture the role of the IT regime only.17 16 See also Walsh (2009). 17 We thank an anonymous referee for raising this issue.
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There has been a vast literature dedicated to the interactions between fiscal and monetary policies that, under the heading of the ‘‘Fiscal theory of the price level’’ (FTPL), concludes on the possibility that fiscal policy could be the driver of price setting. The key intuition of the FTPL is that, if current and future fiscal policies are set without concern for sustainability, the general price level will ‘‘jump’’ in order to fulfill the present value budget constraint (see e.g. Sims, 1994; Woodford, 1995). The inference on the IT regime, stemming from the existence of a stable regime from 1993 onwards in our estimations, could be over-emphasized if fiscal policy was shown to have induced a stable regime as well. It turns out that this argument can be overcome. First, institutional fiscal changes occurred in 1997, then 2007, hence after the adoption of an IT regime, and for the former change 4 years after data reveal a prominent empirical regime switch. Moreover, the data do not show a new regime switch around 1997, whatever the number of states we introduce.18 Second, the identification of the VAR model is well controlled for the inclusion or exclusion of fiscal variables. As a matter of fact, we performed two complementary VAR model, extending our benchmark VAR model either to the cyclically-adjusted primary balance19 or to public debt. In comparison with the benchmark model, the introduction of fiscal variables gives similar effects of monetary shocks.20 Third, Claeys (2008) assesses the changes in fiscal policy in Sweden using a MSVAR estimation and he finds that whatever fiscal rules he studies (debt ratio, fiscal surplus, fiscal spending and tax rules) there is no evidence of a regime switch in 1993 as documented by our estimation on the monetary side.21 This supports the idea that the effects of 1997 fiscal policy reform are not to be confused with the effects of the introduction of inflation targeting. Moreover, Claeys (2008) shows that there have been both active and passive fiscal regimes22 during the sample period defining the IT regime in Sweden (Regime 1 on Figs. 1–3). It therefore cannot be argued that the low reaction of monetary policy towards inflation has been made possible by passive fiscal policies, hence by a continuing stable fiscal regime. Finally, our main conclusion, that IT adoption has been able to improve economic performance in Sweden in terms of inflation and output, leading to a better anchoring of expectations, is consistent with Fregert and Jonung (2008). Both authors adopt a completely different approach from ours to assess IT in Sweden. They make use of the changes in the characteristics of collective wage agreements between 1908 and 2008 to review Sweden’s economic history. More noteworthy for our topic, they show that as soon as IT was announced, long term wage agreements soared: they conclude that ‘‘IT gained instant credibility’’. Two intertwined mechanisms may explain our main result. First, IT is meant to help anchor private inflation expectations (see Gürkaynak et al., 2010): hence, a central bank is able to control inflation without pursuing aggressive action towards inflation variations. Second, the central bank’s decision to lower inflation, in line with the official strategy of Swedish governments since 1991, may have led to low and stable inflation and hence to a lower observed response to inflation. What might have been pure ‘window-dressing’ led to credibility enhancement. The credibility of the monetary policy framework change produced changes in inflation expectations and inflation process (see Benati, 2008; Fregert and Jonung, 2008). The decrease in the weight put on inflation is therefore consistent with changes in the structure of the economy as well as with the decrease in inflation and output variabilities. 5. Conclusion In this paper, we study empirically whether IT adoption in Sweden has constituted a switch in the conduct of monetary policy and has reduced macroeconomic uncertainty. The MSVAR method has revealed that a steep regime shift did occur and that the adoption of inflation targeting in Sweden has not given rise to a sole focus on inflation. Sweden has thus moved to a regime with a lower response to inflation under IT. Moreover, a change is clearly visible as far as overall macroeconomic uncertainty is concerned: under IT, intercepts and variances of key variables are lower than under the regime preceding IT adoption. Since Sweden has enhanced monetary policy transparency it may have succeeded in anchoring expectations. Finally, a counterfactual exercise has shown that IT could give more monetary leeway, i.e. the Riksbank under an IT regime can achieve a lower real interest rate, to be compared with the situation where the central bank would not be under IT. In the face of a negative shock, this potentially low real interest rate can be viewed as a further gain associated with the adoption of IT, while the paper shows that this framework does not imply the sole focus on inflation. It is noteworthy that higher policy leeway would not have led to a sacrifice in terms of the output. This is in line with Walsh (2009)’s recent synthesis that ‘‘inflation targeting central banks have not experienced greater output volatility’’. Appendix See Fig. A.1. 18 See footnote 13. 19 This fiscal indicator is netted out of changes in the output gap and the interest rate; it is a good indicator of the discretionary fiscal stance. Hence, it can reveal changes in the fiscal process. 20 See Fig. A.1. in the Appendix. 21 Bi and Leeper (2010) assume a break in fiscal rules in 1997 based on the new fiscal reform whereas Claeys (2008) identifies break points in 1995 or 1996 depending on the fiscal instruments. 22 Passive (active) fiscal regimes correspond to a situation in which fiscal policy is (not) set in accordance with the fulfillment of their present value budget constraint, whereas active (passive) monetary regimes correspond to strong (weak) reaction to inflation (see Leeper, 1991).
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Fig. A.1. Responses to a Monetary Shock using different VAR specifications. Note: Estimates based on the benchmark VAR with output gap, inflation and interest rate (first column), the benchmark VAR augmented with the cyclically-adjusted primary government deficit (second column) and the benchmark VAR augmented with the government debt in percent of GDP (third column). The dotted lines represent the 95% confidence intervals. The impulse response corresponds to a percentage point change in the variables of interest, in response to a one percentage point increase in the central bank interest rate.
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