Announcements of Interest Rate Forecasts: Do Policymakers Stick to Them?

Mirkov, Nikola; Natvik, Gisle James Working Paper Announcements of Interest Rate Forecasts: Do Policymakers Stick to Them? Working Paper, No. 2013/11 Provided in Cooperation with: Norges Bank, Oslo Suggested Citation: Mirkov, Nikola; Natvik, Gisle James (2013) : Announcements of Interest Rate Forecasts: Do Policymakers Stick to Them?, Working Paper, No. 2013/11, ISBN 978-82-7553-745-2, Norges Bank, Oslo, https://hdl.handle.net/11250/2496698 This Version is available at: https://hdl.handle.net/10419/210034 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Working Paper Norges Bank Research Nikola Mirkov and Gisle James Natvik Working papers fra Norges Bank, fra 1992/1 til 2009/2 kan bestilles over e-post: [email protected] Fra 1999 og senere er publikasjonene tilgjengelige på www.norges-bank.no Working papers inneholder forskningsarbeider og utredninger som vanligvis ikke har fått sin endelige form. Hensikten er blant annet at forfatteren kan motta kommentarer fra kolleger og andre interesserte. Synspunkter og konklusjoner i arbeidene står for forfatternes regning. Working papers from Norges Bank, from 1992/1 to 2009/2 can be ordered by e-mail: [email protected] Working papers from 1999 onwards are available on www.norges-bank.no Norges Bank’s working papers present research projects and reports (not usually in their final form) and are intended inter alia to enable the author to benefit from the comments of colleagues and other interested parties. Views and conclusions expressed in working papers are the responsibility of the authors alone. ISSN 1502-8143 (online) ISBN 978-82-7553-745-2 (online) Announcements of Interest Rate Forecasts: Do Policymakers Stick to Them?∗ Nikola Mirkov†Gisle James Natvik‡ Universität St.Gallen Norges Bank April 8, 2013 Abstract If central banks value the ex-post accuracy of their forecasts, previously announced interest rate paths might affect the current policy rate. We explore whether this “forecast adherence” has influenced the monetary policies of the Reserve Bank of New Zealand and the Norges Bank, the two central banks with the longest history of publishing interest rate paths. We derive and estimate a policy rule for a central bank that is reluctant to deviate from its forecasts. The rule can nest a variety of interest rate rules. We find that policymakers appear to be constrained by their most recently announced forecasts. Keywords: Interest rates, forecasts, Taylor rule, adherence JEL Classifications: E43, E52, E58 ∗We are most grateful to Francesco Ravazzolo, Dagfinn Rime and Anders Vredin for insightful discussions at the early stage of the project. A great thanks to Glenn Rudebusch, Francis Diebold, Paul Söderlind, Alejandro Justiniano, Monika Piazzesi, Refet Gürkaynak, Bart Hobijn, Ian Dew-Becker, Rhys Bidder, Paul Hubert, Daniel Kienzler, Øistein Røisland, Kevin Lansing, Snorre Evjen, Mathis Mehlum and the seminar and conference participants at the Norges Bank, Universität St.Gallen, Stanford University, Federal Reserve Bank of San Francisco, the 32nd International Symposium on Forecasting and the 44th Money, Banking and Finance Conference. Views expressed in this paper are those of the authors, and do not necessarily reflect those of Norges Bank. †Nikola Nikodijevic Mirkov (corresponding author), Universität St.Gallen, Rosenbergstr. 52, 9000 St.Gallen, Switzerland, E-mail: [email protected], Tel: +1 415 697 41 37 ‡Gisle James Natvik, Norges Bank, Bankplassen 2, 0151 Oslo, Norway, E-mail: gislejames[email protected], Tel: +47 22 31 63 38 1 1 Introduction According to economic theory, monetary policy predominantly affects the economy through expectations regarding the future path of short-term interest rates.1This insight takes center stage in the debate on “forward guidance” and has motivated a number of central banks to communicate their policy intentions explicitly by publishing their own forecasts of future interest rates.2However, the practice of announcing policy intentions has long been somewhat controversial, and a key issue is whether past announcements could constrain future policy decisions,3and what the normative implications of such a constraint might be. The reduced flexibility could prevent sufficiently strong policy responses to macroeconomic shocks. On the other hand, the effectiveness of forward guidance requires that the central bank eventually implements the signaled policy and does not simply provide a view on the likely future path of the economy.4Importantly, even though there is a rich theoretical debate on the desirability of announcing interest rate forecasts, the empirical evidence on whether past announcements actually influence future policy is scarce. Our paper attempts to close this gap. We derive a simple policy rule for a central bank that perceives deviations from its previously announced forecasts to be costly and therefore has an incentive to stick to them. The specification is sufficiently flexible to nest a broad class of interest rate rules proposed elsewhere in the literature. We may therefore use a host of alternative policy formulations to separate the movements in the central bank’s “preferred” policy rate, i.e. movements in the policy instrument driven by the bank’s usual response to changes in the economy, from the effect of previously published interest rate forecasts. The rules are estimated on the actual policy rates of the Reserve Bank of New Zealand (RBNZ) and the Central Bank of Norway (Norges Bank) to answer the big question: do announced forecasts influence actual policy decisions? To the best of our knowledge, we are the first to address this question. 1See Eggertsson and Woodford (2004) and Woodford (2005). 2The Reserve Bank of New Zealand inaugurated the practice (in 1997), followed by the Central Bank of Norway, Norges Bank, (in 2005), the Swedish Riksbank and the Central Bank of Iceland (in 2007), the Czech National Bank (in 2008) and the Federal Reserve (in 2012). 3See for instance Svensson (2009), Mishkin (2004), Goodhart (2009) and Kohn (2008). Another debated issue concerns the merits of informing private agents about the central bank’s reaction pattern, see for example Morris and Shin (2002), Svensson (2006), Gosselin, Lotz and Wyplosz (2008) and Rudebusch and Williams (2008). 4See Woodford (2012) and Gersbach and Hahn (2011). 2 The work that most resembles our analysis is Campbell, Evans, Fisher and Justiniano (2012), who incorporates qualitative forward guidance (e.g., “considerable period” language) in the reaction function of the Fed and show that the extended policy rule offers improved empirical predictions. In contrast to our approach, however, the authors do not examine quantitative forward guidance and do not assume that the Fed faces costs from deviating from it. Their assumption is that the public knows that the Fed will renege on such “promises” in the future, as the policy rule describes its preferred behavior.5 Our main result suggests that both the RBNZ and Norges Bank are reluctant to deviate from previously announced interest rate forecasts when setting their policy rates. Specifically, the two central banks appear constrained by their 1- quarter-ahead forecast announced in the quarter before the actual decision takes place. The forecasts older than one quarter have no effect on the current policy rate. The result holds both when we model the preferred policy rate using estimated rules, and when we approximate it using the central banks’ “nowcasts” of the policy rate published in the monetary policy reports. Finally, we show that policy rules augmented to allow for forecast adherence explain several episodes in the behaviors of the two banks much better than policy rules without interest rate forecasts. We perform two robustness checks of the main result. First, we ask whether our empirical strategy “cries wolf” i.e., whether simple policy rules tend to indicate forecast adherence, when the policymaker has no such preferences. To this end, we use a basic New Keynesian model from Gersbach and Hahn (2011) to simulate the optimal behavior of a central bank that minimizes a loss function with a weight on forecast errors. The model is simulated for different values of the weight to mimic different degrees of preference towards forecast adherence. We then apply our empirical strategy to simulated data and show that the estimated policy rules do not lead us to commit false positive errors: the estimated coefficient on forecasts is positive and significant only if the central bank has a sufficiently strong desire to reduce forecast deviations. 5The authors refer to such forward guidance as “Odyssean” forward guidance, as it resembles Odysseus commanding his sailors to tie him to the ship’s mast, so that he can enjoy the Sirens’ song without jumping overboard. 3 Second, we discuss whether our results can be explained by a completely different assumption regarding policymakers’ preferences, namely that the two central banks minimize surprises in the policy rate, as suggested by Svensson (2003).6 We argue that our results would only be consistent with such preferences if we assume that: 1) the central bank’s forecasts and market expectations of future short-rates are perfectly aligned; 2) the central bank adopts market expectations as its own. We conjecture that the second assumption is unlikely, given the lack of evidence of such behavior. In addition, we run a “placebo test” on the Norges Bank data before it began publishing interest rate forecasts, and show that a previous quarter 3-month forward rate, as a proxy for market expectations, had no effect on the policy rate.7 The reminder of the paper is organized as follows. In Section 2, we discuss the dataset and the institutional setting in which the two central banks announce interest rate forecasts. Section 3provides details on our estimation strategy. Section 4reports the main results and illustrates the robustness checks we perform. 2 Dataset on Interest Rate Forecasts 2.1 Reserve Bank of New Zealand The Reserve Bank of New Zealand (RBNZ) was the first central bank to publish its own interest rate forecasts, together with projections for CPI inflation and GDP growth. Beginning in March 1997, the forecasts for the 90-days Bank Bill rate have been published in the quarterly Monetary Policy Statement (MPS), and the upper panel of Figure 1illustrates an example from the June 2012 MPS. The RBNZ only publishes the central forecast, and in addition it provides a qualitative assessment of “what the RBNZ sees as the main risks and uncertainties around the central forecast.”8Starting with the MPS of June 2003, the Bank has published both the current and previous quarter projections as Figure 1shows. FIGURE 1ABOUT HERE 6For an insightful discussion of such preferences, see Rudebusch (2006). 7We are unable to perform the placebo test for the RBNZ, since its operational procedures were significantly changed at the beginning of our sample in March 1999. 8See Drew and Karagedikli (2008). 4 The main tool used to produce all of the forecasts is the RBNZ’s core macroeconomic model,9where the policy rate is set according to a forward-looking Taylor rule. Interest rate forecasts are conditional on the RBNZ’s projections of future inflation, and the mechanism for producing those forecasts is referred to as the endogenous policy forecast system.10 Finally, the model-based forecasts are subject to a considerable amount of judgment before ultimately being released in the MPS.11 The Bank’s interest rate forecasts cover an 8-quarter horizon, and the upper panel of Figure 2illustrates the 1-, 2- and 3-quarters ahead forecasts over time against the realized 90-day Bank Bill rate. The start date for the analysis of the New Zealand data is March 1999, when the RBNZ adopted the Official Cash Rate (OCR) system, and the operating procedures of the RBNZ have remained broadly unchanged since. 2.2 Norges Bank Three times a year, usually in March, June and October, the Central Bank of Norway publishes its Monetary Policy Report (MPR), which includes projections of the future key policy rate, CPI inflation, the output gap and CPI inflation that excludes changes in tax and energy prices.12 All of the forecasts are published in the form of fan charts, illustrated in the lower panel of Figure 1. The reason for publishing central forecasts together with confidence bands is to emphasize the contingency of those forecasts.13 FIGURE 2ABOUT HERE The main tool for producing interest rate forecasts is the core macroeconomic model of the Norges Bank, NEMO, combined with judgment.14 The model-generated forecasts are conditional on key macroeconomic projections, various exogenous variables (e.g., government spending, oil investments) and financial market infor- 9The most recent available documentation on this model is Benes, Binning, Fukac, Lees and Matheson (2009). 10See Ranchhod (2002). 11See Drew and Karagedikli (2008). 12As of 2013, Norges Bank will publish its path four times a year. 13See Holmsen, Qvigstad, Røisland and Solberg-Johansen (2008). 14The Norwegian Economy Model (NEMO), a medium-size DSGE model, has been used for policy making since 2008 and details can be found in Brubakk, Husebø, Maih, Olsen and Magne (2006). For a discussion of the use of judgment, see Holmsen et al. (2008). 5 mation, and derived under the condition that the interest rate is set to minimize a loss function over macroeconomic outcomes. At this stage, Norges Bank staff follows a set of three criteria for “appropriate” interest rate forecasts: 1) achievement of the inflation target; 2) a reasonable balance between inflation and capacity utilization; 3) robustness. These criteria are reflected by the loss function that is minimized subject to the NEMO model equations. Finally, the Executive Board decides on the likely interval for the policy rate over the next three months (the “strategy interval”), and the staff produces a forecast for the interest rate path.15 The lower panel of Figure 2plots the point interest rate forecasts (solid lines) for the period from August 2006 to December 2011 together with the realized key policy rate (dashed line). 3 Model of Interest Rate Adherence 3.1 Deriving the Reaction Function Consider a policymaker who at each time tsets the current interest rate itand announces a future path of that rate. The path consists of interest rate forecasts for a number of consecutive periods in the future, given the central bank’s expectations regarding future macroeconomic variables, such as inflation or unemployment. We assume that the central bank only publishes two such interest rate forecasts, a short-horizon forecast ip t,t+s(e.g., 1 quarter) and a long-horizon forecast ip t,t+l (e.g., 8 quarters). In this way, we attempt to keep the exposition simple, while mimicking the cross-section of published interest rate forecasts. The Bank sets it, ip t,t+sand ip t,t+lin every tto minimize the expected discounted sum of future per period losses: Lt=1 2Et ∞ X k=0 δk ¡it+k−i∗ t+k¢2+ϕ(it+k−it+k−1)2 +κs³it+k−ip t+k−s,t+k´2 +κl³it+k−ip t+k−l,t+k´2 (1) The first term in the loss function represents the costs of deviating from an implied target level of the policy rate. The target rate i∗ tsummarizes the central bank’s preferences and the state of the economy in period t, and can be any non-inertial 15For further details on the process, see Alstadheim, Bache, Holmsen, Maih and Røisland (2010). 6 ip t,t+h=β0+β1it−1+εp,h t(11) and use the residuals from the regression, instead of the original forecast series, in the interest rate rules. In such a way, the forecast variables added to different rules include only information beyond the general level of interest rates. Interestingly, when the original forecast series are included in rules, the lagged policy rate, and not the forecasts, become insignificant due to collinearity.25 4 Results Our main results are reported in tables 1and 2, where we test for adherence to 1-quarter-ahead forecasts announced in the quarter before the actual policy rate is set. We discuss the findings for each country separately, beginning with New Zealand. 4.1 Estimated Reaction Functions 4.1.1 RBNZ The first column from the left in table 1reports the estimated coefficients of the policy rule from the K.I.T.T. model, with inflation expectations and lagged interest rate as the only arguments. The second column reports the estimated coefficients for the same interest rate rule when the forecast of the 1-quarter-ahead Bank Bill rate announced in a previous quarter is added. Similar pairwise exercises are performed for the Clarida et al. (1999) rule in columns 3 and 4 and for the forwardlooking Calvo-type rule in columns 5 and 6. For each coefficient, the t-statistic is reported in brackets. Our main parameter of interest is κs, the weight on past forecasts. TABLE 1ABOUT HERE 25In all the augmented specifications we estimate for the RBNZ, and in the “Calvo” specification we estimate for the Norges Bank, we constrain the coefficient ϕin front of the lagged policy rate to be equal to its value from the rule without interest rate forecasts. When we exclude the constraint, the algorithm does not converge to a finite solution. 13 The main insight from table 1is that for all three specifications, κsis positive and statistically significant. Therefore, the RBNZ seem to have adhered to its own forecasts. Furthermore, adding these forecasts to the reaction functions makes the AR(1) element of the error terms in the KITT and the “Calvo” rules become insignificant. In other words, previously published forecasts seem to explain some of the systematic deviation of the actual policy rate from the “target” rate implied by the conventional simple rule without forecasts. However, this is not the case for all of the estimated reaction functions. We also see that the 2-years-ahead inflation expectations seem to explain a significant amount of the variation in the actual policy rate. This is reasonable, as the RBNZ is a strict inflation targeter. Yet it is somewhat puzzling that the inflation expectations variable is not statistically significant in all specifications, especially when the interest rate forecast is added to the rule. As a robustness check, we therefore extended the analysis by adding the forecast term to the rule that is orthogonal to both the lagged policy rate and expected inflation. The results did not change, and we therefore do not report the outcome of this exercise.26 The in-sample fit of all the rules without the forecast terms is relatively high, so the forecasts are less likely to capture some other (forward-looking) information omitted in the original rules. However, the forecasts do explain a statistically significant portion of the actual policy rate variation. The upper panel of Figure 3plots the estimated residuals from the Clarida et al. (1999) rule without (red bars) and with (solid blue line) the 1-quarter-ahead forecast in the rule. We see that the interest rate forecasts seem to play an important role in the policymaker’s reaction pattern, as the residuals from the augmented rule on average are lower than those of the original rule.27 FIGURE 3ABOUT HERE Moreover, augmenting the standard policy rules with the forecast terms identifies some episodes in the actual policy rate setting, where the rate differed from what the original rule suggested. For instance, the RBNZ increased the policy rate two times during the second quarter of 2002, from 5 to 5.5%. The CGG rule suggests the average policy rate for the quarter of 4.91% as appropriate and the 1-quarter 26The results are available on request. 27The original rule residuals have a mean of -1.6 and standard deviation of 30 basis points, versus the -1 basis point mean and 20 basis point standard deviation of the augmented rule residuals. 14 ahead forecast for the 90-day Bill rate that the RBNZ published in the previous quarter was 5.41%. Another example would be March 9, 2006 when the RBNZ decided to leave the OCR unchanged at 7.25%, whereas the policy rule suggests a cut to 7% and the previously announced 1-quarter ahead forecast for the 90- day Bill rate was 7.6%. On September 16, 2010, the RBNZ’s OCR rate was at 3%, while the Taylor rule suggests 2.5% as appropriate, and the 1-quarter ahead interest rate forecast announced in June of the same year was 3.28%. It took another 3 policy meetings before the policy rate was cut to 2.5% in March 2011, and the 1-quarter forecast for the 90-day Bank bill rate was lowered to 2.86%. 4.1.2 Norges Bank Table 2reports the corresponding results for the estimated reaction function of the Norges Bank. Coefficients and t-statistics are calculated using asymptotic standard errors (in brackets). TABLE 2ABOUT HERE Similar to the case of the RBNZ, the Norges Bank seems to adhere to the 1- quarter-ahead interest rate forecast, as κsis estimated to be significant in all specifications. The expected output gap is another important variable in the estimated policy rules, whereas the coefficient of inflation is significantly different from zero only when the Clarida et al. (1999) rule is used. The improvement of the fit due to the inclusion of the interest rate forecasts is again marginal, but helpful in explaining some of the estimated policy shocks from the original rules. The lower panel of Figure 3again plots the residuals from the estimated Clarida et al. (1999) rule without the forecasts terms (red bars) and including the 1-quarter-ahead forecasts (blue solid line).28 The rule augmented with adherence apparently explains several policy decisions better than the original rule, which indicates that these decisions might have been particularly strongly affected by the previously announced forecasts. For example, the key policy rate in the third quarter of 2008 was 5.75%, whereas the estimated policy rule suggests 5.5% as the appropriate level (the residual from the original rule is 26.4 basis points) and the 1-quarter ahead forecast for the key policy rate, published in June 2008, was 28Similar to the RBNZ case, augmenting the original rule reduces the mean of the estimated shocks from -1.3 basis points to 0.2 and the standard deviation from 21 basis points to 12. 15 5.75%. Another example is the second quarter of 2010, when the previously announced 1-quarter-ahead forecast stood at 1.9% and the policy rate was set to 2% at the end of the quarter, while the original Clarida et al. (1999) rule suggests 1.75%. As the figure shows, there are other episodes where the forecasts added no additional information to the original rules, yet on average policy seems to have adhered to previously announced short-horizon forecasts. 4.2 The Preferred Policy Rates In this section we approach our empirical question from a slightly different angle. We consider two different proxies for what the interest rate would have been without adherence, referred to as the “preferred” rate, and thereafter evaluate whether adherence is significant after controlling for the preferred rate. 4.2.1 Using the Estimated Rules We “construct” the preferred policy rate series for the two central banks from the estimated interest rate rules without the previously announced forecasts. There are three such estimates for each central bank, namely the fitted policy rate according to the institution-specific rules (the KITT model documentation for the RBNZ and Bernhardsen (2008) for the Norges Bank), the generalized Taylor rule (Clarida et al. (1999)) and the “forward looking” rule (Levine et al. (2007)). Once we obtain the fitted policy rates, we perform the following two regressions: it=e Ωe it+f Å1ip t−1,t+εt(12) and it=e Ωe it+f Å1εp,1 t+εt(13) where e itis the preferred policy rate, ip t−1,tis the 1-quarter-ahead forecast announced in a previous quarter and εp,1 tis the residual from the regression (11), i.e., the 1-quarter-ahead forecast orthogonal to the lagged policy rate. We use the 16 2-step General Least Squares (GLS) model of Hoffman (1987) to estimate the regression coefficients and therefore account for the so called “generated regressor” problem. Table 3reports the parameter estimates. TABLE 3ABOUT HERE The “weight” the RBNZ places on the 1-quarter ahead interest rate forecasts is significantly different from zero in all the estimated equations, independent of whether we use the original time-series of the forecast or that orthogonal to the lagged policy rate (the residual term from the equation (11)). The estimates for the Norges Bank are similar, whereas the forecast series orthogonal to the lagged policy rate is only marginally significant. Overall, the main result holds. 4.2.2 Using “Nowcasts” as the Preferred Policy Rate Interest rate rules provide a simplistic description of monetary policy. Decisions regarding the appropriate policy rate can be systematically influenced by the omitted factors such as financial market conditions, house prices or judgment. All of these factors could in principle be correlated with past interest rate forecasts. In addition to the omitted variable problem, it could also be the case that the 1- quarter-ahead forecasts are simply “good” forecasts of the policy rate, which we misinterpret as forecast adherence. We address these potential issues by using the two central banks’ “nowcasts” of the policy rate as the preferred policy stance in a current quarter.29 The nowcasts are produced by the core macroeconomic models of the central banks and combined with judgment before being released in the monetary policy reports.30 It is therefore likely that these nowcasts capture the factors that have systematically influenced monetary policy in the two countries. Moreover, the information content from past forecasts is embedded in the current information set. If the announced paths are merely forecasts, they should have no predictive power on the policy rate over and above the nowcasts. TABLE 4ABOUT HERE 29The nowcasts published by the Norges Bank concern the key policy rate, while as previously explained, the RBNZ announces the nowcasts of the 90-day Bank Bill rate. We adjust the latter for the spread between the Bank Bill rate and the policy rate. 30See Drew and Karagedikli (2008) and Holmsen et al. (2008). 17 Table 4reports the estimated coefficients from equations (12) and (13) where we use the nowcasts as the preferred policy rate e it. The key insight remains intact: the two central banks appear constrained by their most recently announced forecasts. When we use the orthogonalized series of forecasts, the result remains the same for the RBNZ and we obtain a marginally significant f Å1for the Norges Bank. As the nowcasts contain the most up-to-date information about the current state of the economy and the two central banks’ judgments about the appropriate policy, it is unlikely that our main result reflects a superior forecasting ability of past interest rate forecasts. 4.3 Longer Horizon Forecasts The actual policy rate seems to be affected by the interest rate forecast announced in the preceding quarter, but not by the forecasts announced before that. This is clear from tables 5and 6, which illustrate the results for s=1 (1 quarter) and l=2 (2 quarters ahead), for the RBNZ and the Norges Bank, respectively. TABLES 5AND 6ABOUT HERE We have also estimated the rules using forecast horizons from 3 to 8 quarters ahead. The mid- and long-range forecast above 1-quarter ahead do not add any information to the estimated rules. 4.4 Does Our Empirical Strategy “Cry Wolf”? We specify policy in terms of simple rules rather than the minimization of an explicit objective function. A natural concern is that our findings falsely indicate a preference for adherence, when in reality no such preference exists. To address this issue, we apply our empirical approach to data that are artificially generated from an environment where the central bank’s true preferences are known. 4.4.1 The Model We simulate data from the standard 3-equation New Keynesian model used in Gersbach and Hahn (2011), where the central bank optimally sets policy to min- 18 imize a loss function over output and inflation, and potentially is also concerned about deviations from the previously announced 1-period-ahead forecasts of the policy rate. As explained in the Data Section, both the RBNZ and the Norges Bank announce interest rate forecasts conditional on future inflation and output gap forecasts. In principle, these forecasts might also carry a weight. We thus incorporate the costs of deviating from inflation projections, as in Gersbach and Hahn (2011). The Phillips curve, determined by forward-looking price-setters, reads: πt=δEt[πt+1]+λyt+χt, where χtis an AR(1) cost-push shock: χt=ρχχt−1+εχ t. The dynamic IS curve is given by: yt=Et[yt+1]+σ¡io t−Et[πt+1]¢+ωt, where ωtis an AR(1) demand shock: ωt=ρωωt−1+εω t. In every period t, the central bank sets the current interest rate it, the 1-quarter- ahead inflation forecast πP t+1,t, and the 1-quarter-ahead interest rate forecast iP t+1,t to minimize the following loss function: Lt=1 2Et ∞ X k=0 δj³π2 t+k+ay2 t+k+b(πt+k−πP t−1+k,t+k)2+c(it+k−iP t−1+k,t+k)2´(14) The parameters a,band cdescribe the central bank’s preference for stabilizing output and minimizing the costs of deviating from previous inflation- and interest 19 rate forecasts, respectively. The three weights are all normalized by the weight on inflation. Note that the central bank internalizes how its choice of interest rate forecast affects future policy and thereby future output and inflation. In this sense, the interest rate path becomes a “commitment device”, allowing the central bank to affect private expectations, because reneging on these “promises” is costly. 4.4.2 Model Simulation and Estimated Policy Rule As the central bank re-optimizes in every period, by setting the current policy rate and announcing the optimal policy rate in the next period, the policymaker’s reaction function can not be expressed in a closed form (in terms of a,band c). To relate our empirical approach to the optimal policy in this specific environment, we first simulate the model, assuming different values of deviation costs band c, and then estimate the following non-inertial Taylor rule on simulated data samples: isim t=γππsim t+γyysim t+ρ1iP,sim t−1,t+ϑt(15) where ϑtis an AR(1) process. The higher we set the coefficient cin the loss function, the higher the estimate of coefficient ρ1in the rule should be. The model is solved for optimal policy under discretion using the algorithm of Söderlind (1999). We generate 3,000 samples of data, where each sample contains 60 observations. We then estimate the equation (15) on each sample and report the means of estimated parameters in table 7, together with t-statistics (in brackets) calculated using the standard deviation of those estimates. The upper panel provides the model parametrization that we employ, which consists of the same values as in Clarida et al. (2000). TABLE 7ABOUT HERE The lower panel illustrates the key takeaway from the exercise: our estimated simple policy rules do not commit false positive errors. The coefficient on the interest rate forecast ρ1is only significantly different from zero if the “true” reluctance to deviate from previous forecasts is relatively strong. In our exercise, the empirical strategy implies adherence when cis above 0.2, i.e. when the weight on deviations from the announced interest rate forecasts is equal to one-fifth of the weight on 20 inflation. In the other two cases, when c=10−7i.e. practically zero,31 and c=.1, previous interest rate forecasts appear unimportant in the reduced-form reaction function.32 TABLE 8ABOUT HERE Most important, this result is independent of whether the policy rule we estimate on the simulated data is misspecified. Excluding the output gap term from equation (15) will still not lead us to commit false positive errors when measuring forecast adherence with the interest rate rules, see table 8. 4.5 Policy Rate Surprises Our interpretation of the empirical findings is that the two central banks find it costly to deviate from their own forecasts. Such costs introduce an additional adjustment term in the banks’ reaction functions, and constrain policymaking over and above the desire to smooth the policy rate itself. In this section we discuss whether our results might be explained by a completely different assumption, namely that the central banks aim to minimize surprises in the policy rate, as suggested by Svensson (2003).33 Suppose that the central bank’s optimization problem can be described by the following loss function: Lt=1 2Et ∞ X k=0 δk"¡it+k−i∗ t+k¢2+ϕ(it+k−it+k−1)2 κE 1(it+k−Et+k−1it+k)2#, (16) where the first two terms describe, as previously explained, the central bank’s objectives to set the actual policy rate according to the state of the economy, in a gradual fashion, respectively. The parameter κE 1captures the bank’s preferences for minimizing the difference between the current policy rate itand the expected 31With c=0, the interest rate deviation term from the loss function vanishes and the interest rate forecasts are not determined. 32The variation in the values of the estimated inflation coefficient γπis in line with Cochrane (2007), who argues that the Taylor rule parameter γπcannot be identified by regressing the policy rate on inflation. 33See also Rudebusch (2006). 21 policy rate one period before the decision, Et−1it. If the future short-rate expectations of the central bank and the public are perfectly aligned, and if we further assume that the public and not the central bank “dictates” those expectations, our empirical strategy captures the policymaker’s effort to reduce surprise movements in the policy rate and not to stick to its promises. Let us consider the two assumptions individually. As we have shown, our main result concerns the shortest-horizon forecasts announced a quarter before the actual policy rate is set. Over the course of any three-month period, we might indeed assume that the uncertainty around a policymaker’s decisions is relatively low (with respect to medium- or long term outlook) and thus the expectations of the central bank and the markets are broadly aligned. The better the proxy for market expectations one has, the closer the results of estimating the reaction function of the policymaker in (16) are going to be to our results. Yet, the central banks we consider publish their own interest rate forecasts, and thus a positive and significant κE 1coefficient de facto means that the two central banks adhere to their own forecasts, irrespective of whether the underlying motive is to avoid the loss of reputation or minimize surprises in the policy rate. The two explanations are complementary and empirically indistinguishable. Only if the second assumption holds, does our explanation that the central banks adhere to their own short-horizon forecasts fail. As the market expectations are those that guide the central banks’ short-rate expectations, the estimates of κs that we report in tables 1and 2measure the policymaker’s effort to reduce policy surprises. Nevertheless, the assumption is quite strong. It means that the RBNZ and the Norges Bank publish their own forecasts by relabeling market expectations. There might be some anecdotal evidence that the central banks that publish interest rate forecasts occasionally adjust those forecasts to appear similar to the observed forward rate curve on the day prior to the announcement, but it is unlikely that this relabeling is done in a systematic way, without discussing it openly in monetary policy reports. Finally, if our results are entirely driven by a preference for conforming to market expectations, this preference should also have influenced policy before the practice of publishing paths was introduced. This is testable, and we turn to such a test in the next section. 22 Figures and Tables Figure 1: How do Interest Rate Forecasts get Published? The figure reports examples of published interest rate forecasts in the Monetary Policy Statement of the Reserve Bank of New Zealand (upper panel) and in the Monetary Policy Report of the Norges Bank (lower panel). A. RBNZ B. Norges Bank 29 Figure 2: Time-Series of Interest Rate Forecasts. The figure plots monthly series of realized interest rates (dashed black lines) in New Zealand (upper panel) and Norway (lower panel) together with previously announced 1-quarter (solid blue), the 2-quarters (solid green) and 3-quarters (solid red) ahead forecasts for that period. A. RBNZ B. Norges Bank 30 Figure 3: Estimated Policy Shocks. The figure plots residuals from the estimated Clarida et al. (1999) rule from the equation (8) without the 1-quarter-ahead interest rate forecast (red bars), together with residuals from the same rule that includes the previously announced forecast iP t−1,t(solid blue line), estimated for the RBNZ (upper panel) and the Norges Bank (lower panel). A. RBNZ B. Norges Bank 31 Table 1: Policy Rules for the RBNZ from 1999 - 2011 (1Q Forecasts). Table reports the estimated parameters of the rule from the RBNZ documentation (column KITT), the rule by Clarida et al. (1999) (column CGG) and the Calvo-type rule by Levine et al. (2007) (column Calvo). All specifications are estimated without and with the 3-month-ahead interest rate forecast. Reported t-statistics (in brackets) are calculated using asymptotic standard errors. The remaining rows report F-statistic (F-stat), Durbin-Watson statistic (DW) and adjusted R-squared, and the number of observations is 51. KITT CGG Calvo -s=1 - s=1 - s=1 γπ3.350 3.983 3.754 4.986 3.202 2.269 (8.547) (1.900) (11.93) (1.523) (23.79) (3.328) γy1.619 1.204 0.800 0.365 (8.914) (0.602) (17.40) (1.263) ϕ2.256 2.256 5.084 5.084 2.140 1.907 (7.290) (1.069) (20.99) (1.177) (15.25) (1.693) δ0.467 0.524 (3.943) (0.380) κs1.552 2.513 1.145 (2.956) (1.738) (2.613) λ0.895 0.982 0.607 0.939 0.970 0.995 (6.095) (2.181) (5.966) (1.808) (23.758) (3.605) F-stat 3.156 1.219 19.28 4.39 13.847 18.96 F-stat (CV) 4.218 3.747 3.747 3.444 3.444 3.232 DW Statistic 1.518 1.739 1.634 1.995 1.443 1.554 Adjusted R20.997 0.995 0.999 0.997 0.999 0.999 32 Table 2: Policy Rules for the Norges Bank from 2005 - 2011 (1Q Forecasts). Table reports the estimated parameters from the policy rule in Bernhardsen (2008) (column B), the rule by Clarida et al. (1999) (column CGG) and the Calvo-type rule by Levine et al. (2007) (column Calvo). All specifications are estimated without and with the 3-month-ahead interest rate forecast. Reported t-statistics (in brackets) are calculated using asymptotic standard errors. The remaining rows report F-statistic (F-stat), Durbin-Watson statistic (DW) and adjusted R-squared, and the number of observations is 25. B CGG Calvo -s=1 - s=1 - s=1 γπ0.242 0.615 0.831 1.595 1.093 1.438 (0.804) (1.305) (6.416) (2.250) (3.699) (3.192) γint 0.832 0.121 (2.137) (0.565) γw-0.049 1.272 -(0.087) (3.948) γy0.468 0.524 0.961 1.309 1.264 1.128 (1.270) (3.053) (4.043) (4.298) (1.894) (6.504) ϕ0.351 0.796 0.627 1.303 0.824 0.824 (0.686) (1.387) (5.976) (1.613) (1.506) (2.571) δ0.382 0.006 (1.195) (0.041) κs0.915 0.799 0.518 (3.267) (1.898) (2.075) λ0.923 0.240 0.367 0.086 0.367 0.138 (3.083) (0.237) (1.821) (0.089) (0.586) (0.152) F-stat 7.68 114.96 45.70 95.17 33.96 70.80 F-stat (CV) 4.015 3.927 4.431 4.171 4.171 4.015 DW Statistic 1.204 1.960 1.845 1.964 1.845 1.976 Adjusted R20.989 0.999 0.999 1.000 0.998 0.999 33 Table 3: Fitted Policy Rules as the Preferred Policy Rate. Table reports the estimated parameters from the equation (12) (upper panel) and (13) (lower panel) where the preferred policy rate e itis estimated using the previously mentioned policy rules for the two central banks. The coefficients are calculated using the 2-step GLS estimator of Hoffman (1987). Reported t-statistics (in brackets) are calculated using Newey-West standard errors. The remaining rows report Durbin- Watson statistic (DW) and adjusted R-squared. ip t,t+1 RBNZ Norges Bank KITT CGG Calvo B CGG Calvo e Ω-0.312 0.488 0.974 0.130 0.522 0.623 -(2.416) -(1.507) -(0.272) -(8.554) -(3.523) -(4.352) f Å11.230 0.557 0.187 0.834 0.458 0.377 (27.46) (11.21) (4.652) (6.265) (5.615) (5.537) DW Statistic 0.576 0.849 1.337 1.309 1.300 1.551 Adjusted R20.916 0.957 0.996 0.966 0.975 0.989 εp,1 t RBNZ Norges Bank KITT CGG Calvo B CGG Calvo e Ω1.253 0.992 0.981 0.748 0.937 1.003 (0.433) -(0.025) -(0.185) -(3.039) -(0.525) (0.034) f Å10.461 0.259 0.070 0.138 0.128 0.028 (9.264) (4.770) (2.596) (1.046) (1.645) (0.991) DW Statistic 0.576 0.849 1.507 1.309 1.300 1.711 Adjusted R20.916 0.957 0.994 0.966 0.975 0.989 34 Table 4: Central banks’ Nowcasts as the Preferred Policy rate. Table reports the estimated parameters from the equation (12) (the columns ip t,t+1) and the equation (13) (the columns εp,1 t). Reported t-statistics (in brackets) are calculated using Newey-West standard errors. The statistics tell us whether the coefficients e Ωand f Å1are statistically different from 1 and 0, respectively. The remaining rows report F-statistic (F-stat), Durbin-Watson statistic (DW), adjusted R-squared and the number of observations (N.Obs.). RBNZ Norges Bank ip t,t+1εp,1 tip t,t+1εp,1 t e Ω1.065 1.001 0.875 1.010 (2.545) (0.404) -(1.667) (2.301) f Å1-0.063 0.108 0.133 0.018 -(2.594) (2.264) (1.681) (1.601) DW Statistic 1.548 1.715 1.723 2.207 Adjusted R20.998 0.998 0.994 0.993 N.Obs. 55 55 24 24 35 Table 5: Policy Rules for the RBNZ from 1999 - 2011 (1Q & 2Q Forecasts). Table reports the estimated parameters of the rule from the RBNZ documentation (column KITT), the rule by Clarida et al. (1999) (column CGG) and the Calvotype rule by Levine et al. (2007) (column Calvo). All specifications are estimated without and with interest rate forecasts, whereas the short-horizon forecast is s= 1 (3 months) and the long-range forecast is l=2 (6 months). Reported t-statistics (in brackets) are calculated using asymptotic standard errors. The remaining rows report F-statistic (F-stat), Durbin-Watson statistic (DW) and adjusted R-squared, and the number of observations is 50. KITT CGG Calvo s=1,l=2s=1,l=2s=1,l=2 γπ3.764 4.003 1.940 (3.867) (4.211) (2.906) γy1.307 0.270 (2.519) (0.711) ϕ2.237 5.095 1.536 (3.772) (4.468) (2.135) δ0.584 (1.633) κs1.462 1.906 0.953 (1.307) (7.275) (1.833) κl-0.306 -1.155 0.177 -(0.136) -(1.011) (0.524) λ0.978 0.824 1.000 (3.527) (2.953) (3.181) F-stat 1.36 11.49 16.87 F-stat (CV) 3.454 3.243 3.087 DW Statistic 1.739 1.932 1.413 Adjusted R20.995 0.999 0.999 36 Table 6: Policy Rules for the Norges Bank from 2005 - 2011 (1Q & 2Q Forecasts). Table reports the estimated parameters from the policy rule in Bernhardsen (2008) (column B), the rule by Clarida et al. (1999) (column CGG) and the Calvo-type rule by Levine et al. (2007) (column Calvo). All specifications are estimated without and with interest rate forecasts, whereas the short-horizon forecast is s=1 (3 months) and the long-range forecast is l=2 (6 months). Reported t-statistics (in brackets) are calculated using asymptotic standard errors. The remaining rows report F-statistic (F-stat), Durbin-Watson statistic (DW) and adjusted R-squared, and the number of observations is 23. B CGG Calvo s=1,l=2s=1,l=2s=1,l=2 γπ0.558 1.507 1.092 (1.036) (1.997) (1.622) γint 0.950 (2.413) γw0.598 (1.069) γy0.200 1.409 0.748 (0.522) (4.589) (2.578) ϕ-0.055 1.668 2.898 -(0.115) (2.129) (6.682) δ-0.577 -(5.493) κs0.250 1.017 1.064 (1.135) (4.205) (3.281) κl0.527 -0.213 1.050 (1.567) -(0.480) (2.558) λ0.872 0.051 -0.028 (2.278) (0.129) -(0.043) F-stat 16.98 94.87 128.95 F-stat (CV) 4.004 4.102 4.026 DW Statistic 1.669 1.932 1.824 Adjusted R20.995 0.999 0.999 37 Table 7: Estimated Taylor Rule on Simulated Data. Table illustrates the calibration of the model in Section 4.4.1 (upper panel) and the estimated parameters of the policy rule (15) without and with the interest rate forecast term (lower panel), whereas t-statistics (in brackets) are calculated using standard errors from the Monte Carlo simulation. The average values of Durbin-Watson statistic (DW) and adjusted R-squared are also reported, whereas every generated sample contains 60 observations and we simulate the model 3,000 times. Calibration NK Phillips Curve: δ=0.99 λ=0.3 IS curve: σ=1 Cost-Push Shock: ρχ=0.9 σχ=1 Demand Shock: ρω=0.9 σω=1 Loss-Function: a=0.3 b=0.2 c=10−7c=0.1 c=0.2 without with without with without with γπ1.394 1.393 0.196 0.240 0.691 0.620 (1.241) (1.231) (0.852) (0.948) (4.247) (3.271) γy0.566 0.564 0.605 0.635 0.075 0.032 (0.616) (0.609) (5.000) (4.677) (1.202) (0.413) ρ1-0.016 0.100 0.120 (-0.113) (1.212) (1.677) λ0.888 0.889 0.928 0.938 0.920 0.934 (12.77) (12.64) (20.88) (24.95) (18.77) (22.95) DW Statistic 1.955 1.952 1.329 1.170 1.400 1.174 Adjusted R20.835 0.837 0.879 0.884 0.859 0.868 38