Measuring the time-inconsistency of US monetary policy

EUROPEAN

CENTRAL

BANK

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W O R K I N G PA P E R S E R I E S

WORKING PAPER NO. 291 MEASURING THE TIME-INCONSISTENCY OF US MONETARY POLICY BY PAOLO SURICO November 2003

EUROPEAN

CENTRAL

BANK

W O R K I N G PA P E R S E R I E S

WORKING PAPER NO. 291 MEASURING THE TIME-INCONSISTENCY OF US MONETARY POLICY1 BY PAOLO SURICO2 November 2003

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I wish to thank Alberto Alesina, Filippo Altissimo, Efrem Castelnuovo, Gunter Coenen, Carlo Favero, Jordi Galì, Tommaso Monacelli, Anton Muscatelli, Jorges Rodrigues, Massimo Rostagno and Guido Tabellini for very useful comments.This paper has been prepared while the author was visiting the European Central Bank whose kind hospitality is gratefully acknowledged.The views expressed herein are those of the author and do not necessarily reflect views of the European Central Bank. Any remaining errors are of course the sole responsibility of the author. This paper can be downloaded without charge from http://www.ecb.int or from the Social Science Research Network electronic library at http://ssrn.com/abstract_id=487470. Address for correspondence: Istituto di Economia Politica, Università Bocconi,Via Gobbi 5, 20136 Milan, Italy. E-mail: [email protected].

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Contents

Abstract

4

Non-technical summary

5

1

Introduction

7

2

The model 2.1 Commitment 2.2 Discretion

9 11 12

3

The evidence 3.1 Preliminary analysis 3.2 The reduced-form 3.3 Empirical results 3.4 Robustness analysis

13 14 15 16 19

4

Concluding remarks

20

References

22

Tables and figures

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European Central Bank working paper series

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ECB • Working Paper No 291 • November 2003

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Abstract This paper o¤ers an alternative explanation for the behavior of postwar US in‡ation by measuring a novel source of monetary policy time-inconsistency due to Cukierman (2002). In the presence of asymmetric preferences, the monetary authorities end up generating a systematic in‡ation bias through the private sector expectations of a larger policy response in recessions than in booms. Reduced-form estimates of US monetary policy rules indicate that while the in‡ation target declines from the pre- to the post-Volcker regime, the average in‡ation bias, which is about one percent before 1979, tends to disappear over the last two decades. This result can be rationalized in terms of the preference on output stabilization, which is found to be large and asymmetric in the former but not in the latter period. JEL Classi…cation: E52, E58 Keywords: asymmetric preferences, time-inconsistency, average in‡ation bias, US in‡ation

4


NON TECHNICAL SUMMARY The behavior of postwar US inflation is characterized by two major episodes. The first is an initial rise that extends from the 1960s through the early 1980s. The second is a subsequent fall that lasts from the early 1980s to the present day. The difference of the average inflation rates across the two sub-samples is above 2%. While a more favorable macroeconomic environment, a better policy management or a persistent error in the real-time measures of potential output are also likely to have played a role, an important strand of the literature has investigated whether the time-consistency problem can explain the behavior of US inflation. In a stimulating contribution, Ireland (1999) shows that Barro and Gordon's (1983) model of time-consistent monetary policy imposes long-run restrictions on the time series properties of inflation and unemployment that are not rejected by the data. In the absence of a commitment technology, the monetary authorities face an incentive to surprise inflation in an effort to achieve a lower level of unemployment through an expectations-augmented Phillips curve. However, such an optimal plan is not time-consistent in the sense of Kydland and Prescott (1977), and private agents, who rationally understand such a temptation, adjust their decisions accordingly. In equilibrium, unemployment is still at its first-best level but the rate of inflation is inefficiently higher than it would otherwise be. This is the celebrated inflation bias result, according to which the higher the natural rate of unemployment the more severe the timeconsistency problem of monetary policy is. As Persson and Tabellini (1999) make clear, the central bankers' ambition of attaining a level of unemployment below the natural rate is crucial to generate the kind of inflation bias a la Barro and Gordon (1983), and both researchers and policy makers have challenged such an assumption on the ground of realism. McCallum (1997) argues that were this the case, the monetary authorities would learn by practising the time-inconsistency of their actions and eventually would revise their objective. Describing his experience as vice-Chairman, Blinder (1998) claims that the Fed actually targets the natural rate of real activity, thereby suggesting that overambitious policy makers cannot be at the root of any kind of inflation bias. While this may rationalize the failure of the theory to account for the short-run inflation dynamics (see Ireland, 1999), it does not necessarily imply that the time-consistency problem has been unimportant in the recent history of US monetary policy.


5

In an intriguing article, Ruge-Murcia (2003) constructs a model of asymmetric central bank preferences that nests the Barro-Gordon model as a special case. When applied to the full postwar period, the hypothesis that the Fed targets a level of real activity different from the natural rate is rejected but the hypothesis that it weights more severely output contractions than output expansions is not. This suggests the existence of a novel average inflation bias that according to Cukierman (2002) comes from the private sector expectations of a more vigorous policy response in recessions than in booms. The novel average inflation bias is a function of both the preferences of the central bank and the volatility of the output gap. To the extent that a significant policy regime shift has occurred at the beginning of the 1980s after the appointment of Paul Volcker as Fed Chairman, it is likely that the degree of asymmetry and therefore the degree of time-inconsistency has also changed during the last four decades. Hence, rather than focusing on the full postwar period like Ireland (1999) and Ruge-Murcia (2003), we study the sub-samples that are typically associated with a shift in the conduct of US monetary policy according to the reasoning that the timeinconsistency problem and the relative inflation bias are best interpreted as regime-specific. The observed decline in the volatility of the output gap also seems consistent with this view. This paper contributes to the literature on optimal monetary policy by measuring the relative contribution of the inflation target and the asymmetric preferences induced inflation bias to the rise and fall of postwar US inflation. Specifically, it is found that the inflation target is 3.42% and the average inflation bias is 1.01% during the pre-1979 policy regime while the target declines to 1.96% and the bias vanishes over the last two decades. This result can be rationalized by the fact that the policy preference on output stabilization appears to be large and asymmetric before but not after the appointment of Paul Volcker as Fed Chairman. Although other factors such as a better policy making and more favorable supply shocks are also likely to have played a role, this paper provides empirical support and quantitative measures of a new, additional explanation for the behavior of US inflation.

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1

Introduction

The behavior of postwar US in‡ation is characterized by two major episodes. The …rst is an initial rise that extends from the 1960s through the early 1980s. The second is a subsequent fall that lasts from the early 1980s to the present day. The important change that underlies such a path can be exempli…ed by the average rates reported in the second column of Table 1. In‡ation is measured as the annualized quarterly increase in the log GDP chain-type price index whereas the output gap is constructed as the log deviation of real GDP from the Congressional Budget O¢ce potential output. The di¤erence of the average in‡ation rates across the two sub-samples is above 2% and it is echoed by the decline in the volatility of the output gap displayed in the third column. While a more favorable macroeconomic environment during the second period, a better policy management or a persistent error in the real-time measures of potential output are also likely to have played a role, an important strand of the literature has investigated whether the time-consistency problem can explain the behavior of US in‡ation. In a stimulating contribution, Ireland (1999) shows that Barro and Gordon’s (1983) model of time-consistent monetary policy imposes long-run restrictions on the time series properties of in‡ation and unemployment that are not rejected by the data. In the absence of a commitment technology, the monetary authorities face an incentive to surprise in‡ation in an e¤ort to achieve a lower level of unemployment through an expectations-augmented Phillips curve. However, such an optimal plan is not time-consistent in the sense of Kydland and Prescott (1977), and private agents, who rationally understand such a temptation, adjust their decisions accordingly. In equilibrium, unemployment is still at its …rst-best level but the rate of in‡ation is ine¢ciently higher than it would otherwise be. This is the celebrated in‡ation bias result, according to which the higher the natural rate of unemployment the more severe the timeconsistency problem of monetary policy is. As Persson and Tabellini (1999) make clear, the central bankers’ ambition of attaining a level of unemployment below the natural rate is crucial to generate the kind of in‡ation bias


7

a la Barro and Gordon (1983), and both researchers and policy makers have challenged such an assumption on the ground of realism. McCallum (1997) argues that were this the case, the monetary authorities would learn by practicing the time-inconsistency of their actions and eventually would revise their objective. Describing his experience as vice-Chairman, Blinder (1998) claims that the Fed actually targets the natural rate of real activity, thereby suggesting that overambitious policy makers cannot be at the root of any kind of in‡ation bias. While this may rationalize the failure of the theory to account for the short-run in‡ation dynamics (see Ireland, 1999), it does not necessarily imply that the time-consistency problem has been unimportant in the recent history of US monetary policy. In an intriguing article, Ruge-Murcia (2003) constructs a model of asymmetric central bank preferences that nests the Barro-Gordon model as a special case. When applied to the full postwar period, the hypothesis that the Fed targets a level of real activity di¤erent from the natural rate is rejected but the hypothesis that it weights more severely output contractions than output expansions is not. This suggests the existence of a novel average in‡ation bias that according to Cukierman (2002) comes from the private sector expectations of a more vigorous policy response in recessions than in booms. Speci…cally, the average in‡ation bias is a function of both the preferences of the central bank and the volatility of the output gap. To the extent that a signi…cant policy regime shift has occurred at the beginning of the 1980s after the appointment of Paul Volcker as Fed Chairman, it is likely that the degree of asymmetry and therefore the degree of timeinconsistency has also changed during the last four decades. Hence, rather than focusing on the full postwar period like Ireland (1999) and Ruge-Murcia (2003), we study the sub-samples that are typically associated with a shift in the conduct of US monetary policy according to the reasoning that the time-inconsistency problem and the relative in‡ation bias are best interpreted as regime-speci…c. The di¤erence in the sub-sample volatility of the output gap shown in the third column of Table 1 also seems consistent with this view. This paper contributes to the literature on optimal monetary policy by proposing a measure of the average in‡ation bias that arises in a model of asymmetric central bank preferences.

8


To this end, it is developed a novel identi…cation strategy that allows to recover the relevant parameters in the central bank objective function and, most importantly, to translate them into a measure of time-inconsistency. The comparison between the commitment and the discretionary solutions shows how the observed in‡ation mean can be successfully decomposed into a target and a bias argument, a result that to our knowledge of the existing literature comes as new. Reduced-form estimates of US monetary policy rules indicate that a signi…cant regime shift has occurred during the last forty years as measured by the change in the Fed policy preferences. In particular, while the in‡ation target declines from 3:42% to 1:96%, the average in‡ation bias, which is estimated at 1:01% before 1979, is found to disappear over the last two decades. The result can be rationalized in terms of the policy preference on output stabilization, which is found to be large and asymmetric in the pre- but not in the post-Volcker period. The paper is organized as follows. Section 2 sets up the model and solves for the optimal monetary policy. Section 3 derives its reduced-form version and reports the estimates of both the feedback rule coe¢cients and the average in‡ation bias. Section 4 concludes.

2

The model

Following the literature, the private sector behavior is characterized by an expectationsaugmented Phillips curve: yt = µ (¼t ¡ ¼et) + ut , µ > 0

(1)

where yt is the output gap measured as the di¤erence between actual and potential output, ¼t denotes in‡ation and ¼et stands for the expectations on the in‡ation rate in period t from the standpoint of period t ¡ 1. The supply disturbance, ut, obeys a potentially autoregressive process ut = ½ut¡1 + "t where ½ 2 [0; 1) and "t is an i.i.d. shock with zero mean and variance ¾2" . The private sector has rational expectations ¼et = Et¡1¼t

(2)

with Et¡1 being the expectation conditional upon the information available at time t ¡ 1.


9

Potential output is identi…ed with the real GDP trend so that the mean of the output gap is normalized to zero. Moreover, yt is also a random variable as it depends on ut , and its variance, which is a positive function of both ½ and ¾2" , is denoted by ¾2y . As customary in the literature, the central bank is assumed to have full and direct control over in‡ation, which is chosen to minimize the following intertemporal criterion: Min Et¡1 f¼tg

1 X

±¿ Lt+¿

(3)

¿=0

where ± is the discount factor and Lt stands for the period loss function. The latter is speci…ed in a linear-exponential form: ¶ µ 1 exp (°yt ) ¡ °yt ¡ 1 ¤ 2 Lt = (¼ t ¡ ¼ ) + ¸ 2 °2

(4)

where ¸ > 0 and ° represent the relative weight and the asymmetric preference on output stabilization, respectively. The in‡ation target, ¼¤, is assumed stable enough to be approximated by a positive constant that possibly di¤ers across sub-samples. Unlike in the Barro-Gordon model, the target level of output is not meant to overambitiously exceed potential. This is consistent with the empirical evidence reported by Ruge-Murcia (2003). The objective function (4) tends to its minimum whenever both in‡ation and output gaps shrink and larger losses are associated with larger absolute values at an increasing rate. The linex speci…cation, which has been originally proposed by Varian (1974) and Zellner (1986) in the context of Bayesian econometric analysis and introduced by Nobay and Peel (2003) in the optimal monetary policy literature, allows departures from the quadratic objective in that policy makers may treat di¤erently output contractions and output expansions. Indeed, under an asymmetric loss function deviations of the same size but opposite sign yield di¤erent losses and a negative value of ° implies that negative gaps are weighted more severely than positive ones. To see this notice that whenever yt < 0 the exponential component of the loss function dominates the linear component while the opposite is true for yt > 0. The reasoning is reversed for positive values of °. The linex speci…cation nests the quadratic form as a special case and by means of L’Hôpital’s rule it can be shown that whenever ° tends to zero the central bank objective function (4)

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reduces to the conventional symmetric parametrization Lt =

1 2

h i (¼t ¡ ¼ ¤)2 + ¸y2t . As argued

by Ruge-Murcia (2003), this feature is attractive as it allows us to test whether the relevant preference parameter is statistically di¤erent from zero.1

The speci…cation of an asymmetric loss with respect to the output gap only is motivated by empirical as well as theoretical considerations. At the empirical level, Surico (2003b) derives a general, nonlinear interest rate rule within a model of nonquadratic preferences over both in‡ation and output, and …nds evidence of an asymmetric objective for the latter but not for the former variable. At the theoretical level, Geraats (1999) shows that the labor market ‡ows over the business cycle provide a natural microfoundation for an asymmetric welfare criterion as the …rms’ hiring-…ring decisions are mainly taken along the extensive margin during recessions but along the intensive margin during booms.

2.1

Commitment

This subsection solves for the optimal monetary policy under commitment. Because no endogenous state variable enters the model, the intertemporal policy problem reduces to a sequence of static optimization problems. Accordingly, the monetary authorities, who can manipulate in‡ation expectations, choose both planned in‡ation, ¼t, and expected in‡ation, ¼et , to minimize the asymmetric loss function (4) subject to the augmented Phillips curve (1) and to the additional constraint (2) imposed by the rational expectations hypothesis. The corresponding …rst order conditions are, respectively: ¤

(¼t ¡ ¼ ) + Et¡1

¡Et¡1

½

½

¾ ¸µ [exp (°yt ) ¡ 1] ¡ ¹ = 0 °

(5)

¾ ¸µ [exp (°yt ) ¡ 1] + ¹ = 0 °

with ¹ being the Lagrange multiplier associated to the rational expectation constraint. Combining the optimality conditions to eliminate ¹, and taking expectations of the resulting ex1 An alternative to the linex function is the cubic speci…cation proposed by Surico(2003a and 2003b). The relative advantage of using the cubic form as the primitive function is that it does not require any approximation of the optimal monetary policy rule. Nevertheless, for a realistic range of values of the in‡ation and the output gaps, and given the estimates of ° reported below, the cubic and the linex function behave very similarly.


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pression produce E (¼ t ) = ¼¤

(6)

where we have used the law of iterated expectations to get rid of Et¡1 . Equation (6) states that the planned in‡ation rate equals on average the socially desirable in‡ation rate and therefore it is independent of the output gap.

2.2

Discretion

If commitment is infeasible, the monetary authorities choose the in‡ation rate ¼t at the beginning of the period after the private agents have formed their expectations but before the realization of the real shock ut . Accordingly, the discretionary solution reads ¤

(¼t ¡ ¼ ) + Et¡1

½

¾ ¸µ [exp (°yt) ¡ 1] = 0 °

(7)

It is instructive at this point to compare the solution obtained under asymmetric preferences with the solution obtained under the standard quadratic case. Whenever ° tends to zero, it is possible to show using L’Hôpital’s rule that the optimal monetary policy becomes (¼t ¡ ¼ ¤) = ¡¸µEt¡1 (yt )

(8)

This implies that under quadratic preferences there exists a one to one mapping between the in‡ation bias and the output gap conditional mean. Moreover, in the face of white noise supply disturbances (i.e. ½ = 0) the in‡ation bias is zero re‡ecting the notion of potential output targeting. Turning back to equation (7), we notice that if the output gap is a zero mean, normally ³ 2 ´ distributed process, then exp (°yt ) is distributed log normal with mean exp °2 ¾2 . It follows

that by taking expectations of (7) and rearranging terms, it is possible to write the optimality condition as: 1¡

12

µ 2 ¶ ° ° 2 E (¼ t ¡ ¼¤) = exp ¾ ¸µ 2 y

(9)


To compute the average in‡ation bias, we use a simple transformation of the model that confronts directly the time-inconsistency of monetary policy. This amounts to take logs of both side of (9) and gives the following expression: E (¼ t ) ' ¼¤ ¡

¸µ° 2 ¾ 2

(10)

A comparison between the expected rates under commitment (6) and under discretion (10) illustrates the source of a novel average in‡ation bias. The time-inconsistency of monetary policy arises here because policy preferences are asymmetric rather than because the desired level of output is above potential like in the Barro-Gordon model. As the private sector correctly anticipates the monetary authorities’ incentive to respond more aggressively to output contractions than to output expansions (i.e. ° < 0), the in‡ation rate exceeds the …rst-best solution attainable under commitment. Hence, policy makers end up generating a systematic boost in in‡ation expectations, which is higher the larger and the more asymmetric the policy preference on output stabilization is. Possible improvements to the discretionary solution would require the appointment of either a more conservative central banker, who is one endowed with a lower relative weight ¸ in the spirit of Rogo¤ (1985) and/or a lower in‡ation target than society, or a more symmetric policy maker, who is one endowed with a smaller absolute value of °. Lastly, the average in‡ation bias is proportional to the variance of the output gap as the marginal bene…t of an in‡ation surprise in (7) is convex in the output gap. When ° goes to zero as it does in equation (8), such a marginal bene…t becomes linear and the average in‡ation bias disappears together with the precautionary motive.

3

The evidence

This section investigates the empirical merits of the asymmetric preference model to account for the behavior of postwar US in‡ation. The analysis spans the period 1960:1-2002:3 and it is conducted on quarterly, seasonally adjusted data that have been obtained in February 2003 from the web site of the Federal Reserve Bank of St. Louis. In‡ation is measured as


13

the annualized change in the log GDP chain-weighted price index, whereas the output gap is constructed as the di¤erence between the log real GDP and the log real potential output provided by the Congressional Budget O¢ce. To make our results comparable with those reported by Ruge-Murcia (2003), we …rst consider the whole sample. Then, we use our identi…cation strategy to estimate the asymmetric preference and to obtain a measure of the in‡ation bias for both the pre- and the post-Volcker regimes. We also address the issue of sub-sample stability by re-estimating the model over Greenspan’s tenure, which begins in the third quarter of 1987. Indeed, equation (10) makes it clear that the in‡ation bias is a function of policy makers’ preferences and therefore it can only be interpreted as regime-speci…c. To the extent that a signi…cant break has occurred in the conduct of US monetary policy during the last forty years, our identi…cation scheme provides a sharper evaluation of the model by measuring the time-inconsistency across the two eras.

3.1

Preliminary analysis

As a way to provide a preliminary evidence before turning to the estimates of the nonlinear optimal monetary policy (7), we evaluate the performance of the symmetric quadratic paradigm upon the behavior of the in‡ation bias that this speci…cation predicts. According to equation (8), the conditional mean of the output gap is informative about the di¤erence between the realized in‡ation and the in‡ation target. In particular, in the face of i.i.d. supply shocks the conditional mean and therefore the in‡ation bias should be zero re‡ecting the notion of quadratic preferences and potential output targeting. Figure 1 displays the kernel estimates of the output gap conditional mean (with the sign switched) over the full sample using the Nadaraya-Watson estimator, a second order Gaussian kernel and the likelihood cross validation procedure to obtain a value for the …xed bandwidth parameter. The results are una¤ected by using the least squares cross validation criterion and an higher-order kernel. Before proceeding however it is important to stress what we are not doing in this exercise. In particular, we are not using the output gap as the dependent variable while estimating the optimality condition (8). Rather, we are computing from the

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bivariate time-series model of in‡ation and output the conditional mean of the output gap, which according to the model of quadratic preferences and potential output targeting is the measure of the in‡ation bias at each point in time. A number of interesting results emerge from Figure 1. First, the third quarter of 1982 appears to witness the beginning of a new era as represented by the intersection between the lower bound of the 95% con…dence interval and the zero line. This is consistent with the conventional wisdom that a regime-switch in the conduct of US monetary policy has occurred at the beginning of the 1980s, especially with the end of the so-called ’Volcker experiment’ of non-borrowed reserves targeting that Bernanke and Mihov (1998) date in 1982:3. Moreover, the measure of the in‡ation bias is not statistically signi…cant only over the last two decades, implying that the model of quadratic preferences and potential output targeting is rejected by the data over the earlier regime. Although part of the di¤erence may be due to a change in the persistence of the supply shocks, the output gap conditional mean and hence the in‡ation bias appears to be on average statistically di¤erent from zero during the …rst half of the sample. This …nding proves inconsistent with a quadratic preference model and therefore calls for an extension of the theory.

3.2

The reduced-form

The parameter ° and the exponential function in (7) govern the asymmetric response of the policy rate to positive and negative deviations of output from potential. Our task is to estimate a nonlinear reaction function in order to evaluate whether the asymmetric preference is signi…cantly di¤erent from zero. This amounts to test linearity against a nonlinear speci…cation, which is complicated by the fact that it is not possible to recover all structural parameters of the model from the reduced-form estimates. To overcome the issue and identify both ° and the in‡ation bias, we take a simple transformation of the model. This involves the linearization of the exponential terms in (7) by means of a …rst-order Taylor series expansion, and produces: (¼ t ¡ ¼¤ ) + ¸µEt¡1 (yt ) +

¡ ¢ ¸µ° Et¡1 yt2 + et = 0 2

(11)

with et being the remainder of the approximation.


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This condition relates the in‡ation rate to the expected values of the level and the squared of the output gap conditional upon the information available at time t ¡ 1. We solve equation (11) for ¼t and prior to estimation we replace expected output gaps with actual values. The empirical version of the feedback rule is given by: ¼t = ¼¤ + ®yt + ¯y 2t + vt

(12)

which is linear in the coe¢cients ® = ¡¸µ and ¯ = ¡

¸µ° 2

and whose error term is de…ned as ¡ ¢¤ ª © £ vt ´ ¡ ® (yt ¡ Et¡1yt ) + ¯ yt2 ¡ Et¡1 y2t + et

Under the null of quadratic preferences, the term in curly brackets is a linear combination of forecast errors and therefore vt is orthogonal to any variable in the information set available at time t ¡ 1. Equation (12) reveals that by assuming an optimizing central bank behavior the reaction function parameters can only be interpreted as convolutions of the coe¢cients representing policy makers’ preferences and those describing the structure of the economy. Nevertheless, the reduced-form parameters allow now to recover both the asymmetric preferences, ° = 2¯=®, and the average in‡ation bias that results from the di¤erence between equations (6) and (10), namely ¯¾2y .

3.3

Empirical results

To the extent that the penalty associated to an output contraction is larger than the penalty associated to an output expansion of the same size, the model predicts ° < 0, ® < 0 (since ¸, µ > 0), and ¯ > 0. When coupled with the expectations-augmented Phillips curve (1), this implies that the central bank faces an incentive to surprise in‡ation in an e¤ort to hedge against the occurrence of an economic downturn. Put it di¤erently, the asymmetric preference

16


on output generates a precautionary demand for expansions as the model predicts a positive relation between average in‡ation and the variance of the output gap. The orthogonality conditions implied by the rational expectation hypothesis makes the Generalized Method of Moments (GMM) a natural candidate to estimate equation (12). This has also the advantage that no arbitrary restrictions need to be imposed on the information set that private agents use to form expectations. To control for possible heteroskedasticity and serial correlation in the error terms we use the optimal weighting scheme in Hansen (1982) with a four lag Newey-West estimate of the covariance matrix. Three lags of in‡ation, output gap and squared output gap are used as instruments corresponding to a set of 7 overidentifying restrictions that can be tested for. The choice of a relatively small number of instruments is meant to minimize the potential small sample bias that may arise when too many overidentifying restrictions are imposed. We also check the robustness of our results to changes in the instrument set. In particular, we re-estimate the model using …ve lags of in‡ation and two lags of output gap and squared output gap. The F-test applied to the …rst stage regressions, which Staiger and Stock (1997) argue to be important in evaluating the relevance of the instruments, always rejects the null of weak correlation between the endogenous regressors and the variables in the instrument sets. Table 2 reports the estimates of the feedback rule (12) for the full sample. Each row corresponds to a di¤erent set of instruments. The parameter on the output gap, ®, is not statistically di¤erent from zero whereas the parameter on the squared output gap, ¯, is signi…cant and positive. The estimates of the slope coe¢cients as well as the estimates of the in‡ation target are robust to the instrument selection and the hypothesis of valid overidentifying restrictions is never rejected. These results are similar to those reported by Ruge-Murcia (2003) as they con…rm the presence of asymmetric preference using a di¤erent method of estimation and a di¤erent measure of real activity. Table 3 reports the estimates for the pre- and post-Volcker regimes. We remove from the second sub-sample the period 1979:3-1982:3 when the temporary switch in the Fed operating procedure documented by Bernanke and Mihov (1998) appears to be responsible for the failure


17

to gain control over in‡ation. The sample selection is also consistent with the nonparametric evidence reported in the preliminary analysis. The …rst two rows of Table 3 refer to the pre-Volcker era and show large negative values for the level of the output gap besides to positive and signi…cant parameters for its squared. The point estimates of the in‡ation target range from 3:42% to 3:69% while the asymmetric preference parameter is negative and statistically signi…cant. These results sharply contrast with the post-1979 values that are displayed in the middle rows and the bottom rows of Table 3. Indeed, not only the in‡ation target statistically declines to values around 2%, but also the impact of the output gap level on in‡ation appears to be weaker, although still signi…cant. To the extent that the structure of the economy has remained stable during the last forty years, a smaller value of ® can only be rationalized by a decline in ¸, which corresponds to a more conservative monetary policy stance. The most dramatic di¤erence between the two regimes emerges however on the squared output gap, which actually loses explanatory power for both set of instruments as well as for both post-1979 samples. This translates into values of the policy parameter ° that are not statistically di¤erent from zero. Turning to the measure of the asymmetric preference induced time-inconsistency, Table 4 reports the estimates of the average in‡ation bias. According to equation (10), the bias is a convolution of the structural parameters of the model and the variance of the output gap. Given the decline in the latter reported in the third column of Table 1, we expect also the in‡ation bias to decline moving from the pre- to the post-Volcker period. This seems consistent with the change in the volatility of the supply shocks documented by Hamilton (1996) between the 1970s and the 1980s. The second column of Table 4 shows the measure of the average in‡ation bias implied by the reduced-form estimates of Table 3. The …rst block reports the pre-Volcker values whose point estimates range from 1:01% in the baseline case to 1:36% for the alternative instrument set. By contrast, the in‡ation bias is found to be not statistically di¤erent from zero over the post-1979 era, re‡ecting the fact that US monetary policy can be characterized by a nonlinear feedback rule during the former but not during the latter period. Empirical support for this

18


form of regime shift can also be found in the cross-country evidence over 22 OECD economies reported by Cukierman and Gerlach (2003). Lastly, the realized in‡ation mean over the pre-1979 sample falls in the range of estimates implied by the sum of the in‡ation target and the in‡ation bias while its post-Volcker counterparts appear to be higher than the model predicts. This suggests that the theory can e¤ectively decompose the observed in‡ation mean into a measure of the target and a measure of the bias over the pre-1979 regime, though it needs to be extended to account more fully for the gap that appears in the data over the last two decades.

3.4

Robustness analysis

This subsection evaluates the robustness of our results to the empirical speci…cation of the optimal monetary policy. An alternative to replacing expected values with realized values prior to GMM estimation is to compute the conditional mean and the conditional variance of the output gap, which according to the model of asymmetric preferences are helpful to predict in‡ation. To this end, we write the central bank’s best response function as follows: ¼t = c + aEt¡1 (yt ) + b¾2y;t + "t

(13)

where ¾2y;t stands for the output gap conditional variance. As shown by Ruge-Murcia (2003), the reduced-form (13) does not make it possible to recover the relevant structural parameters, though the estimates of c, a, and b are informative about the signi…cance and the sign of the asymmetric preference °. In particular, a superior concern to output contractions relative to output expansions implies a negative coe¢cient on the conditional mean and a positive coe¢cient on the conditional variance. The empirical analysis is complicated by the fact that neither the conditional mean nor the conditional variance of the output gap are directly observed. While the kernel method described above provides us with the estimates of the conditional mean, a model for the conditional variance needs to be constructed. To address this issue, we use a GARCH (1,1) which appears to e¤ectively capture the conditional heteroskedasticity in the US output gap. In so doing, we introduce a direct link between the baseline (12) and the alternative (13) as the output gap,


19

which through the residuals enters squared the conditional variance ¾2y;t , contributes also in the latter speci…cation to forecast in‡ation nonlinearly. Although the conditional variance is a generated regressor, the LM tests applied to the standardized squared residuals cannot reject the null hypothesis of no misspeci…cation in the ARCH model (see Pagan and Ullah, 1988) and therefore corroborate the choice of the parsimonious GARCH (1,1). Table 5 reports the estimates of equation (13) over the three sub-samples using Two-stage Least Square and a White’s correction for computing the standard errors. The use of an IV estimator is dictated by the endogeneity problem that is implicit in the calculation of the output gap conditional mean. The instrument set includes three lags of in‡ation and three lags of the explanatory variables. The results con…rm, by and large, the estimates obtained with the baseline speci…cation. The parameters on the output gap conditional mean is always negative and signi…cant whereas the positive coe¢cient on the conditional variance is signi…cant during the pre-Volcker regime only. While this implies a negative value for the asymmetric preference °, it reveals that US monetary policy can be e¤ectively described by a nonlinear policy rule before but not after 1979.

4

Concluding remarks

This paper develops a method to measure the time-inconsistency of monetary policy when the preferences of the central bank are asymmetric. As demonstrated by Cukierman (2002), if policy makers are more concerned about output contractions than output expansions, an in‡ation bias can emerge on average even though the level of output is targeted at potential. In addition, both casual observations and formal empirical analyses challenge the predictions of the Barro-Gordon model by arguing that the Fed’s desired level of output does not exceed the natural rate (see Blinder, 1998, and Ruge-Murcia, 2003). Using a model of asymmetric preferences and potential output targeting, it is shown how the observed in‡ation mean can be successfully decomposed into a target and a bias argument. When applied to postwar US data, our identi…cation method indicates that the target is 3:42% and the bias 1:01% during the pre-1979 policy regime. By contrast, over the last two decades

20


the in‡ation target declines to 1:96% while the average in‡ation bias tends to disappear. This result can be rationalized by the fact that the policy preference on output stabilization is found to be large and asymmetric before but not after the appointment of Paul Volcker as Fed Chairman. Although other factors such as a better policy making and more favorable supply shocks are also likely to have played a role, this paper provides empirical support and quantitative measures of a new, additional explanation for the behavior of US in‡ation during the postwar period. While suggestive, the results reported in this paper are based on a simple model, and the speci…cation of a richer structure of the economy is likely to produce also a state-contingent bias as well as a stabilization bias. However, as shown by Svensson (1997) and Cukierman (2002), the average in‡ation bias would then be larger than it is with a standard expectationsaugmented Phillips curve. This suggests not only that our estimates are better interpreted as a lower bound but also that a richer speci…cation of the private agents’ behavior may account for the gap between the model-based average in‡ation and the actual average in‡ation during the last two decades. Given our limited knowledge of the channel(s) through which the timeconsistency problem a¤ects policy outcomes, measuring and disentangling the in‡ation bias remains a challenging topic for future research.


21

References Barro, R.J. and D. Gordon, 1983, A Positive Theory of Monetary Policy in a Natural Rate Model, Journal of Political Economy 91, 589-610. Bernanke, B. and I. Mihov, 1998, Measuring Monetary Policy, Quarterly Journal of Economics 63, 869-902. Blinder, A., 1998, Central Banking in Theory and Practice, (Mit Press). Cukierman, A., 2002, Are Contemporary Central Banks Transparent about Economic Models and Objectives and What Di¤erence Does it Make?, Federal Reserve Bank of St. Louis Review 84, 15-45. Cukierman, A, and S. Gerlach, 2003, The In‡ation Bias Revisited: Theory and Some International Evidence, The Manchester School 71, 541-565. Geraats, P., 1999, In‡ation and Its Variation: An Alternative Explanation, CIDER Working Paper C99-105. Hamilton, J.D., 1996, That is what Happened to the Oil Price-Macroeconomy Relationship, Journal of Monetary Economics 38, 215-220. Hansen, L.P., 1982, Large Sample Properties of Generalized Method of Moments Estimators. Econometrica 50, 1029-1054. Ireland, P.N., 1999, Does the Time-Consistency Problem Explain the Behavior of US In‡ation?, Journal of Monetary Economics 44, 279-292. Kydland, F. and E. Prescott, 1977, Rules Rather than Discretion: the Inconsistency of Optimal Plans, Journal of Political Economy 85, 473-490. McCallum, B.T., 1997, Crucial Issues Concerning Central Bank Independence, Journal of Monetary Economics 39, 99-112. Nobay, R. and D. Peel, 2003, Optimal Discretionary Monetary Policy in a Model of Asymmetric Central Bank Preferences, Economic Journal 113, 657-665. Pagan, A. and A. Ullah, The Econometric Analysis of Models with Risk Terms, Journal of Applied Econometrics 3, 87-105.

22


Persson, T. and G. Tabellini, 1999, Political Economics and Macroeconomic Policy, in: Taylor, J. and M. Woodford, eds, Handbook of Macroeconomics (North Holland). Rogo¤, K., 1985, The Optimal Degree of Commitment to a Monetary Target, Quarterly Journal of Economics 100, 1169-1190. Ruge-Murcia, F.J., 2003, Does the Barro-Gordon Model Explain the Behavior of US In‡ation? A Reexamination of the Empirical Evidence, Journal of Monetary Economics 50, 13751390. Staiger, D., and J. Stock, 1997, Instrumental Variables Regression with Weak Instruments, Econometrica 65, 557-586. Surico, P., 2003a, Measuring the Time-Inconsistency of US Monetary Policy, mimeo, Bocconi University. Surico, P., 2003b, In‡ation Targeting and Nonlinear Policy Rules: the Case of Asymmetric Preferences, mimeo, Bocconi University. Svensson, L.E.O., 1997, Optimal In‡ation Targets, ”Conservative” Central Banks, and Linear In‡ation Contracts, American Economic Review 87, 98-114. Varian, H., 1974, A Bayesian Approach to Real Estate Assessment, in: Feinberg, S.E., and A. Zellner, eds., Studies in Bayesian Economics in Honour of L.J. Savage (North Holland). Zellner, A., 1986, Bayesian Estimation and Prediction Using Asymmetric Loss Functions, Journal of the American Statistical Association 81, 446-451.


23

Table 1: Descriptive Statistics Inflation mean

Output gap standard deviation

1960 – 2002

3.78

2.61

1960 – 1982

4.87

3.03

1983 - 2002

2.51

1.98

Sample

US quarterly data. Inflation is measured as changes in the GDP chaintype price index and output gap is obtained from the CBO.

24


Table 2: Reaction Function and Policy Preference Estimates - full sample π*

α

β

Instruments

p-values

Sample 1960:1 2002:3 (1)

(2)

2.34**

0.09

0.04**

F-stat: .00/.00

(0.24)

(0.11)

(0.01)

J(7): .13

2.33**

0.10

0.04**

F-stat: .00/.00

(0.24)

(0.12)

(0.02)

J(7): .14

Specification: πt = π* + α yt + β yt2 + vt Standard errors using a four lag Newey-West covariance matrix are reported in brackets. Inflation is measured as changes in the GDP chain-type price index and output gap is obtained from the CBO. The instrument set (1) includes a constant, three lags of inflation, output gap and squared output gap. The instrument set (2) includes a constant, five lags of inflation, and two lags of output gap and squared output gap. F-stat refers to the statistics of the hypothesis testing for weak instruments relative to output gap and squared output gap, respectively. J(m) refers to the statistics of Hansen’s test for m overidentifying restrictions which is distributed as a χ2 (m) under the null hypothesis of valid overidentifying restrictions. The superscript ** and * denote the rejection of the null hypothesis that the true coefficient is zero at the 5 percent and 10 percent significance levels, respectively.


25

Table 3: Reaction Function and Policy Preference Estimates - sub samples π*

α

β

γ

Instruments

p-values

Sample 1960:1-1979:2 (1)

(2)

3.42**

-0.63**

0.14**

-0.46**

F-stat: .00/.00

(0.58)

(0.19)

(0.06)

(0.15)

J(7): .35

3.69**

-0.84**

0.19**

-0.46**

F-stat: .00/.00

(0.67)

(0.27)

(0.08)

(0.13)

J(7): .37

1.96**

-0.18**

0.01

-0.07

F-stat: .00/.00

(0.13)

(0.08)

(0.01)

(0.17)

J(7): .51

1.94**

-0.16*

0.01

-0.10

F-stat: .00/.00

(0.14)

(0.09)

(0.02)

(0.24)

J(7): .47

1.76**

-0.13**

0.04

-0.79

F-stat: .00/.00

(0.19)

(0.06)

(0.04)

(0.83)

J(7): .73

1.96**

-0.17**

-0.01

-0.03

F-stat: .00/.00

(0.18)

(0.08)

(0.04)

(0.49)

J(7): .38

Sample 1982:4-2002:3 (1)

(2)

Sample 1987:3-2002:3 (1)

(2)

Specification: πt = π * + α yt + β yt2 + vt Standard errors using a four lag Newey-West covariance matrix are reported in brackets. Inflation is measured as changes in the GDP chain-type price index and output gap is obtained from the CBO. The instrument set (1) includes a constant, three lags of inflation, output gap and squared output gap. The instrument set (2) includes a constant, five lags of inflation, and two lags of output gap and squared output gap. F-stat refers to the statistics of the hypothesis testing for weak instruments relative to output gap and squared output gap, respectively. J(m) refers to the statistics of Hansen’s test for m overidentifying restrictions which is distributed as a χ2 (m) under the null hypothesis of valid overidentifying restrictions. The superscript ** and * denote the rejection of the null hypothesis that the true coefficient is zero at the 5 percent and 10 percent significance levels, respectively.

26


Table 4: The Average Inflation Bias Inflation Bias

Inflation Target

1.01** (0.39)

3.42** (0.58)

4.43** (0.52)

1.36** (0.54)

3.69** (0.57)

5.05** (0.68)

0.03 (0.06)

1.96** (0.13)

1.99** (0.14)

0.04 (0.07)

1.94** (0.14)

1.98** (0.14)

0.16 (0.11)

1.76** (0.19)

1.92** (0.12)

-0.01 (0.13)

1.96** (0.18)

1.95** (0.13)

Instruments Sample 1960:1-1979:2 (1) (2) Sample 1982:4-2002:3 (1) (2) Sample 1987:3-2002:3 (1) (2)

Inflation Bias Inflation + Mean Inflation Target 4.39

2.53

2.36

Standard errors in parenthesis. The instrument set (1) includes a constant, three lags of inflation, output gap and squared output gap. The instrument set (2) includes a constant, five lags of inflation, and two lags of output gap and squared output gap. The superscript ** and * denote the rejection of the null hypothesis that the true coefficient is zero at the 5 percent and 10 percent significance levels, respectively. The inflation bias is computed as βσy . 2


27

Table 5: Reaction Function and Policy Preference Estimates - alternative estimates of the output gap process c

a

b σε

Sample 1960:1-1979:2

p-values

0.39 1.53**

-7.67**

0.013**

(0.08)

(0.30)

(0.006)

Sample 1982:4-2002:3

F-stat: .00/.00

0.07 1.34**

-9.69**

0.001

(0.01)

(0.11)

(0.001)

Sample 1987:3-2002:3

F-stat: .00/.00

0.08 1.32**

-9.67**

0.005

(0.02)

(0.11)

(0.003)

F-stat: .00/.00

Specification: πt = c + aEt −1 ( yt ) + bσ y ,t + εt 2

Two-stage Least Squares. Standard errors using White’s correction are reported in brackets. Inflation is measured as changes in the GDP chain-type price index, the CBO output gap conditional mean is obtained using the kernel method explained above and the conditional variance of the CBO output gap is estimated through a GARCH(1,1) specification. The instruments set includes a constant, three lags of inflation and three lags of the explanatory variables. F-stat refers to the statistics of the hypothesis testing for weak instruments relative to the output gap conditional mean and conditional variance, respectively. The superscript ** and * denote the rejection of the null hypothesis that the true coefficient is zero at the 5 percent and 10 percent significance levels, respectively.

28


Figure 1: The Evolution of the Inflation Bias over Time 2.5

2.0

1.5

1.0

0.5

0.0

-0.5 60

65

70

75

80

output gap conditional mean

85

90

95

00

bounds

Sample: 1960:1 – 2002:3, US quarterly data. Inflation is measured as changes in the GDP chain-type price index and output gap is obtained from the CBO. The kernel estimates of the output gap conditional mean on inflation are obtained using the Nadaraya-Watson method, a second order Gaussian kernel and the likelihood cross validation procedure to get a value for the fixed bandwidth parameter. Dashed lines represent upper and lower bounds of the 95% confidence interval.


29

European Central Bank working paper series For a complete list of Working Papers published by the ECB, please visit the ECB’s website (http://www.ecb.int). 202 “Aggregate loans to the euro area private sector” by A. Calza, M. Manrique and J. Sousa, January 2003. 203 “Myopic loss aversion, disappointment aversion and the equity premium puzzle” by D. Fielding and L. Stracca, January 2003. 204 “Asymmetric dynamics in the correlations of global equity and bond returns” by L. Cappiello, R.F. Engle and K. Sheppard, January 2003. 205 “Real exchange rate in an inter-temporal n-country-model with incomplete markets” by B. Mercereau, January 2003. 206 “Empirical estimates of reaction functions for the euro area” by D. Gerdesmeier and B. Roffia, January 2003. 207 “A comprehensive model on the euro overnight rate” by F. R. Würtz, January 2003. 208 “Do demographic changes affect risk premiums? Evidence from international data” by A. Ang and A. Maddaloni, January 2003. 209 “A framework for collateral risk control determination” by D. Cossin, Z. Huang, D. Aunon-Nerin and F. González, January 2003. 210 “Anticipated Ramsey reforms and the uniform taxation principle: the role of international financial markets” by S. Schmitt-Grohé and M. Uribe, January 2003. 211 “Self-control and savings” by P. Michel and J.P. Vidal, January 2003. 212 “Modelling the implied probability of stock market movements” by E. Glatzer and M. Scheicher, January 2003. 213 “Aggregation and euro area Phillips curves” by S. Fabiani and J. Morgan, February 2003. 214 “On the selection of forecasting models” by A. Inoue and L. Kilian, February 2003. 215 “Budget institutions and fiscal performance in Central and Eastern European countries” by H. Gleich, February 2003. 216 “The admission of accession countries to an enlarged monetary union: a tentative assessment” by M. Ca’Zorzi and R. A. De Santis, February 2003. 217 “The role of product market regulations in the process of structural change” by J. Messina, March 2003.

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218 “The zero-interest-rate bound and the role of the exchange rate for monetary policy in Japan” by G. Coenen and V. Wieland, March 2003. 219 “Extra-euro area manufacturing import prices and exchange rate pass-through” by B. Anderton, March 2003. 220 “The allocation of competencies in an international union: a positive analysis” by M. Ruta, April 2003. 221 “Estimating risk premia in money market rates” by A. Durré, S. Evjen and R. Pilegaard, April 2003. 222 “Inflation dynamics and subjective expectations in the United States” by K. Adam and M. Padula, April 2003. 223 “Optimal monetary policy with imperfect common knowledge” by K. Adam, April 2003. 224 “The rise of the yen vis-à-vis the (“synthetic”) euro: is it supported by economic fundamentals?” by C. Osbat, R. Rüffer and B. Schnatz, April 2003. 225 “Productivity and the (“synthetic”) euro-dollar exchange rate” by C. Osbat, F. Vijselaar and B. Schnatz, April 2003. 226 “The central banker as a risk manager: quantifying and forecasting inflation risks” by L. Kilian and S. Manganelli, April 2003. 227 “Monetary policy in a low pass-through environment” by T. Monacelli, April 2003. 228 “Monetary policy shocks – a nonfundamental look at the data” by M. Klaeffing, May 2003. 229 “How does the ECB target inflation?” by P. Surico, May 2003. 230 “The euro area financial system: structure, integration and policy initiatives” by P. Hartmann, A. Maddaloni and S. Manganelli, May 2003. 231 “Price stability and monetary policy effectiveness when nominal interest rates are bounded at zero” by G. Coenen, A. Orphanides and V. Wieland, May 2003. 232 “Describing the Fed’s conduct with Taylor rules: is interest rate smoothing important?” by E. Castelnuovo, May 2003. 233 “The natural real rate of interest in the euro area” by N. Giammarioli and N. Valla, May 2003. 234 “Unemployment, hysteresis and transition” by M. León-Ledesma and P. McAdam, May 2003. 235 “Volatility of interest rates in the euro area: evidence from high frequency data” by N. Cassola and C. Morana, June 2003.


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236 “Swiss monetary targeting 1974-1996: the role of internal policy analysis” by G. Rich, June 2003. 237 “Growth expectations, capital flows and international risk sharing” by O. Castrén, M. Miller and R. Stiegert, June 2003. 238 “The impact of monetary union on trade prices” by R. Anderton, R. E. Baldwin and D. Taglioni, June 2003. 239 “Temporary shocks and unavoidable transitions to a high-unemployment regime” by W. J. Denhaan, June 2003. 240 “Monetary policy transmission in the euro area: any changes after EMU?” by I. Angeloni and M. Ehrmann, July 2003. 241 Maintaining price stability under free-floating: a fearless way out of the corner?” by C. Detken and V. Gaspar, July 2003. 242 “Public sector efficiency: an international comparison” by A. Afonso, L. Schuknecht and V. Tanzi, July 2003. 243 “Pass-through of external shocks to euro area inflation” by E. Hahn, July 2003. 244 “How does the ECB allot liquidity in its weekly main refinancing operations? A look at the empirical evidence” by S. Ejerskov, C. Martin Moss and L. Stracca, July 2003. 245 “Money and payments: a modern perspective” by C. Holthausen and C. Monnet, July 2003. 246 “Public finances and long-term growth in Europe – evidence from a panel data analysis” by D. R. de Ávila Torrijos and R. Strauch, July 2003. 247 “Forecasting euro area inflation: does aggregating forecasts by HICP component improve forecast accuracy?” by K. Hubrich, August 2003. 248 “Exchange rates and fundamentals” by C. Engel and K. D. West, August 2003. 249 “Trade advantages and specialisation dynamics in acceding countries” by A. Zaghini, August 2003. 250 “Persistence, the transmission mechanism and robust monetary policy” by I. Angeloni, G. Coenen and F. Smets, August 2003. 251 “Consumption, habit persistence, imperfect information and the lifetime budget constraint” by A. Willman, August 2003. 252 “”Interpolation and backdating with a large information set” by E. Angelini, J. Henry and M. Marcellino, August 2003. 253 “Bond market inflation expectations and longer-term trends in broad monetary growth and inflation in industrial countries, 1880-2001” by W. G. Dewald, September 2003.

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254 “Forecasting real GDP: what role for narrow money?” by C. Brand, H.-E. Reimers and F. Seitz, September 2003. 255 “Is the demand for euro area M3 stable?” by A. Bruggeman, P. Donati and A. Warne, September 2003. 256 “Information acquisition and decision making in committees: a survey” by K. Gerling, H. P. Grüner, A. Kiel and E. Schulte, September 2003. 257 “Macroeconomic modelling of monetary policy” by M. Klaeffling, September 2003. 258 “Interest rate reaction functions and the Taylor rule in the euro area” by P. GerlachKristen, September 2003. 259 “Implicit tax co-ordination under repeated policy interactions” by M. Catenaro and J.-P. Vidal, September 2003. 260 “Aggregation-theoretic monetary aggregation over the euro area, when countries are heterogeneous” by W. A. Barnett, September 2003. 261 “Why has broad money demand been more stable in the euro area than in other economies? A literature review” by A. Calza and J. Sousa, September 2003. 262 “Indeterminacy of rational expectations equilibria in sequential financial markets” by P. Donati, September 2003. 263 “Measuring contagion with a Bayesian, time-varying coefficient model” by M. Ciccarelli and A. Rebucci, September 2003. 264 “A monthly monetary model with banking intermediation for the euro area” by A. Bruggeman and M. Donnay, September 2003. 265 “New Keynesian Phillips Curves: a reassessment using euro area data” by P. McAdam and A. Willman, September 2003. 266 “Finance and growth in the EU: new evidence from the liberalisation and harmonisation of the banking industry” by D. Romero de Ávila, September 2003. 267 “Comparing economic dynamics in the EU and CEE accession countries” by R. Süppel, September 2003. 268 “The output composition puzzle: a difference in the monetary transmission mechanism in the euro area and the US” by I. Angeloni, A. K. Kashyap, B. Mojon and D. Terlizzese, September 2003. 269 “Zero lower bound: is it a problem with the euro area?" by G. Coenen, September 2003. 270 “Downward nominal wage rigidity and the long-run Phillips curve: simulation-based evidence for the euro area” by G. Coenen, September 2003. 271 “Indeterminacy and search theory” by N. Giammarioli, September 2003.


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272 “”Inflation targets and the liquidity trap” by M. Klaeffling and V. López Pérez, September 2003. 273 “Definition of price stability, range and point inflation targets: the anchoring of long-term inflation expectations” by E. Castelnuovo, S. Nicoletti-Altimari and D. RodriguezPalenzuela, September 2003. 274 “Interpreting implied risk neutral densities: the role of risk premia” by P. Hördahl and D. Vestin, September 2003. 275 “Identifying the monetary transmission mechanism using structural breaks” by A. Beyer and R. Farmer, September 2003. 276 “Short-term estimates of euro area real GDP by means of monthly data” by G. Rünstler, September 2003. 277 “On the indeterminacy of determinacy and indeterminacy" by A. Beyer and R. Farmer, September 2003. 278 “Relevant economic issues concerning the optimal rate of inflation” by D. R. Palenzuela, G. Camba-Méndez and J. Á. García, September 2003. 279 “Designing targeting rules for international monetary policy cooperation” by G. Benigno and P. Benigno, October 2003. 280 “Inflation, factor substitution and growth” by R. Klump, October 2003. 281 “Identifying fiscal shocks and policy regimes in OECD countries” by G. de Arcangelis and S. Lamartina, October 2003. 282 “Optimal dynamic risk sharing when enforcement is a decision variable” by T. V. Koeppl, October 2003. 283 “US, Japan and the euro area: comparing business-cycle features” by P. McAdam, November 2003. 284 “The credibility of the monetary policy ‘free lunch’” by J. Yetman, November 2003. 285 “Government deficits, wealth effects and the price level in an optimizing model” by B. Annicchiarico, November 2003. 286 “Country and sector-specific spillover effects in the euro area, the United States and Japan” by B. Kaltenhaeuser, November 2003. 287 “Consumer inflation expectations in Poland” by T. Łyziak, November 2003. 288 “Implementing optimal control cointegrated I(1) structural VAR models” by F. V. Monti, November 2003. 289 “Monetary and fiscal interactions in open economies” by G. Lombardo and A. Sutherland, November 2003.

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290 “Inflation persistence and robust monetary policy design” by G. Coenen, November 2003. 291 “Measuring the time-inconsitency of US monetary policy” by P. Surico, November 2003.


35