Managing price and financial stability objectives in inflation targeting economies in Asia and the Pacific

PurposeThe global financial crisis demonstrated that monetary policy alone cannot ensure both price and financial stability. According to the Tinbergen (1952) rule, there was a gap in the policymakers’ toolkit for safeguarding financial stability, as the number of available policy instruments was insufficient relative to the number of policy objectives. That gap is now being closed through the creation of new macroprudential policy instruments. Both monetary policy and macroprudential policy have the capacity to influence both price and financial stability objectives. This paper develops a framework for determining how best to assign instruments to objectives.Design/methodology/approachUsing a simplified New-Keynesian model, the authors examine two sets of policy trade-offs, the first concerning the relative effectiveness of monetary and macroprudential policy instruments in achieving price and financial stability objectives and the second concerning trade-offs between macroprudential policy instruments themselves.FindingsThis model shows that regardless of whether the objective is to enhance financial system resilience or to moderate the financial cycle, macroprudential policies are more effective than monetary policy. Likewise, monetary policy is more effective than macroprudential policy in achieving price stability. According to the Mundell (1962) principle of effective market classification, this implies that macroprudential policy instruments should be paired with financial stability objectives, and monetary policy instruments should be paired with the price stability objective. The authors also find a trade-off between the two sets of macroprudential policy instruments, which indicates that failure to moderate the financial cycle would require greater financial system resilience.Originality/valueThe main contribution of the paper is to establish – with the help of a model framework – the relative effectiveness of monetary and macroprudential policies in achieving price and financial stability objectives. By so doing, it provides a rationale for macroprudential policy and it shows how macroprudential policy can unburden monetary policy in leaning against the wind of financial imbalances.

This chapter explores interactions of monetary policy, price stability, and financial stability for a developing country. A new, parsimonious index of financial instability is introduced and the dates of financial stress in Pakistan are documented. The dynamic interactions are investigated via impulse responses (IRs) using the local projections. Building on an augmented Taylor rule, the authors segregate the IRs across business cycle. The authors find that the monetary policy (MP) shocks generate asymmetric responses: more effective during recessions than expansions. The impulses in MP dampen inflation and output during recessions while the financial stress increases, calling for a cautious policy approach to avoid trade-offs between twin objectives of price and financial stability. The MP tightens and the output falls in the event of a financial instability shock with no significant impact on inflation. The trade-off between two objectives during downturns is also manifest here. Finally, MP responds forcefully to inflationary shocks during expansions, but at the cost of worsening financial conditions.

This study adds to a recent and growing literature that assesses the effects of macroprudential policy. We compare the effects of monetary policy and loan-to-value ratio shocks for Korea, an inflation targeting economy and an active user of loan-to-value limits. We identify shocks using sign-restricted structural VARs and rely on a recent approach within this method to conduct structural inference. This study finds that both monetary policy and loan-to-value ratio shocks have effects on different measures of credit, i.e., real bank credit, real total credit and real household credit. We also find that both shocks have non-negligible effects on real house prices, including effects on real output, real consumption and real investment. We do, however, find that loan-to-value ratio shocks have negligible effects on the price level. These findings indicate that for the period covered by this study, limits on loan-to-value achieved their financial stability objectives in Korea in terms of limiting credit and house price appreciation under an inflation targeting regime. Furthermore, it attained these objectives without posing any threat to its price stability objective. Overall, these findings suggest that limits on loan-to-value have important aggregate consequences despite it being a sectoral, targeted policy instrument.

The 1998 Asian Financial Crisis was a milestone in the existence of structural policy reforms in the Indonesian financial sector. Most of the empirical results show that the financial crisis was caused by the lack of soundness and instability of the financial sector. This problem changed the perspective of Bank Indonesia, the central bank in Indonesia, that financial stability is as important as price stability. This highlights the need for the central bank to also ensure financial stability, while monetary policy focuses on price stability and economic growth. However, achieving these goals does not always ensure financial stability. To address systemic risk, Indonesia has begun adopting macroprudential policies. Thus, monetary policy cannot secure both price and financial stability, and a policy mix with macroprudential measures is needed to achieve both price and financial stability. This research examines the relationship between monetary and macroprudential policies and their effects on stability. Monetary policy was measured by the BI Rate and macroprudential policy was measured by Loan to Value (LTV). Price stability was proxied by inflation, and financial stability by credit growth. We analyzed the causality between the variables using the Vector Autoregression Model (VAR) and Granger Causality Test, using quarterly time series data from 2005:Q1 to 2018:Q3. The findings indicate that monetary and macroprudential policies significantly affect price and financial stability. Empirical findings show that tightening the BI rate and LTV significantly reduces inflation and credit growth. This paper highlights the need for a policy mix to ensure price and financial stability.

Previous articleNext article FreeCommentHarald Uhlig, Harald UhligUniversity of Chicago Search for more articles by this author , University of ChicagoFull TextPDF Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreThe paper by Boivin, Giannoni, and Mojon seeks to understand the transmission mechanism of monetary policy in the euro area and its constituent countries, document its change since the creation of the euro, and provide a structural interpretation by means of an open‐economy model. To do so, it is building on state‐of‐the‐art modeling techniques, most notably Bernanke, Boivin, and Eliasz’s (2005) factor‐augmented vector autoregressive (FAVAR) approach for the empirical part and Ferrero, Gertler, and Svensson’s (forthcoming) open‐economy dynamic stochastic general equilibrium (DSGE) model for the structural interpretation. The authors combine both with several innovations, well described in the paper, most notably adding a risk premium on intra‐area exchange rates. They report estimated responses to monetary policy largely consistent with conventional wisdom. They document that the creation of the euro has contributed to a widespread reduction in the effect of monetary policy shocks. They interpret this as stemming not only from the adoption of a single currency but also from European Central Bank policy, shifting toward a more aggressive response to inflation and output.Boivin et al.’s paper exemplifies the best of research that is currently done at central banks as well as in a number of academic departments, seeking to understand aggregate fluctuations and the role of monetary policy from both an empirical and a theoretical perspective. These approaches have started to replace the educated guesses with a serious analysis based on state‐of‐the‐art modeling as the starting point for policy debates. That, in principle, is a good development.Therefore, I hope that the approach taken here is right. But I fear that severe problems remain and that the route taken here is not yet convincing enough for others to follow. Below I shall explain why, including material found subsequently to my presentation in Boston. Much of what is stated here may apply with equal force to the predecessors on which the paper at hand is built, and that may seem like a good defense for the authors. But this is their paper in the end, and it is their choice which methodology to apply. Therefore, it is only fair to raise these points here.I need to warn the reader that this is a discussion. My aim shall be to throw up some challenges and questions and to provoke further thinking on some of these issues. Whether these are fatal flaws or whether all this can be repaired or whether everything is all right after all is something that future research urgently needs to clarify before this approach should be put to wider use. With this disclaimer, let me get in medias res.I. The FAVAR ModelThere are three basic premises of the empirical approach. First, there is considerable comovement in the selected macroeconomic time series so that their most relevant dynamics is captured by a few factors. Second, the strategy here correctly captures the dynamics associated with monetary policy shocks and correctly identifies their effects. Third, the data are sufficiently informative about the changing impact of monetary policy after the introduction of the euro. I am skeptical about all three.II. Is There Comovement in European Data?The idea that macroeconomic variables comove has considerable appeal in the United States, but perhaps less so in Europe, with its diverse set of countries. Nonetheless, the $$R^{2}$$’s reported by Boivin et al. in their table 1 seem impressive and convincing.But I was still skeptical. If indeed a few factors explain most of what is going on, then the sum of the few largest eigenvalues of the variance‐covariance matrix of the data should be near the entire sum of all eigenvalues: that ratio is essentially the $$R^{2}$$’s of all variables on the factors corresponding to these eigenvalues. In fact, one would want more: one would want that sum to be considerably larger than in an artificial data set, generated with the same univariate autocovariance structure as in the data, but no comovement among the artificially generated series.So, I did the following (and I am grateful to the authors for sharing their data set with me to do this). I transformed the data from 1987:Q1 to 2007:Q3 by taking the difference of the log of the current value and its fourth lag and multiplying by 100, except for interest rates, unemployment rates, and capacity utilization: that way, all data are in percents. This appears to be the transformation chosen by the authors. I call this my baseline data set. I calculated eigenvalues in three ways. First, I took the eigenvalues of the variance‐covariance matrix of the baseline data set, summing the largest and calculating the ratio of those partial sums to the total sum. Next, I took the residuals from a regression of the data on current oil and short‐term interest rates, that is, series 1 and 243, and a constant and calculated the eigenvalues from the variance‐covariance matrix of these residuals (and, as an aside, that seemed to me to be a simpler approach than what the authors have done). Finally, I rescaled all time series to have the same standard deviations before calculating the regression and the eigenvalues of the residuals: from discussions with the authors, it may be that this is closest to the route they have chosen. The results can be seen in figure 1, which lists the number of factors (or largest eigenvalues) on the x axis and the fraction of the total sum of eigenvalues on the y axis. For the x axis I stopped at 30 factors, although there would be 243 (or 83) in principle. One can see that 11 factors in the nonrescaled version explain about 90%, seven factors get you to about 80% (coinciding roughly with the individual series results in table 1 of the authors), and five factors (think: above and beyond short‐term rates and oil) explain about 75%. This initially looks like good news for the approach taken by the authors.Fig. 1. Calculated factors and their contribution to overall variance. Three methods of calculating eigenvalues. Authors’ original data. This appears to look good.View Large ImageDownload PowerPointNext, I calculated the first‐order autocorrelations of my baseline data set. I then generated an artificial data set as a set of independent AR(1) processes, driven by normally distributed shocks and with the calculated autocorrelations, starting at zero (rather than a draw from the stationary distribution) and rescaled, so that each artificial series has the same standard deviation as the corresponding series in the data. I redid the exact same calculation of the contribution of the factors as above, using the new artificial data series 1 and 243 as regressors: while they have the same autocorrelation as the original data series, there is obviously no reason to expect them to have any explanatory power for the other series. In fact, in the artificial data set, there is no genuine comovement among the series at all.The result for the artificial data set can be seen in figure 2. I would have expected that figure to be quite different from figure 1 and the factors with the largest eigenvalues to explain considerably less than in the original data set. But the figures look surprisingly and uncomfortably alike. When I first saw a first version of this figure, I thought that it had to be due to a programming error, accidentally storing the figure coming from the data. But it is really the figure coming from the random data. Yes, there are differences. One factor explains as much now, for either of the three methods. It takes a few more factors to get to the same fraction of variance explained. At five factors for the residual, one is at about 60% rather than 75%. Seven factors deliver about 70% for the baseline random data rather than 80% in the original baseline data. And 12 factors are at 85% rather than 90%. For the residuals from the scaled data, the differences are even somewhat bigger. New random draws will generate slightly different pictures anyhow. “Slightly” is important here. The differences from figure 1, while there, remain strikingly small.Fig. 2. Like fig. 1 but applied to artificial data: independent AR(1)’s, with autoregressive coefficients distributed as in the original data. This figure is not much different from fig. 1, even though there are no “true” factors in the artificial data. Thus perhaps in the original data, too, the true factors may account for much less comovement in the original data than fig. 1 or the authors’ calculations would lead one to believe.View Large ImageDownload PowerPointThe reason is easy to explain but perhaps tricky to formalize. There is considerable autocorrelation in the data. Figure 3 shows the autocorrelation coefficients, calculated by ordinary least squares and sorted by size: many are close to unity. With persistent roots, deviations from the mean will linger for many periods. Thus, the calculated correlation of two series with persistent roots may easily appear to be large in a finite sample, even though there is none asymptotically. The factors extracted from a finite sample interpret these large correlations as comovements, even though there is none. It all works nicely asymptotically; it just does not work in the short sample at hand and with the large autocorrelations that are in the data. There may be ways around this problem, for example, by prewhitening the series or, at the least, by calculating the factors from the residuals of univariate AR(1) regressions. But this is not what the authors appear to have done.Fig. 3. Distribution of the AR(1) coefficients in the original data, when fitting univariate AR(1)’s to each series. The artificial data for fig. 2 were created as independent AR(1)’s, with the same distribution of AR(1) coefficients.View Large ImageDownload PowerPointSo in sum, I fear that the approach taken and the evidence presented by the authors are quite consistent with a world in which there is no comovement among the series at all, and they are probably perfectly consistent with a world in which only very few factors matter at the European scale, but explaining considerably less than what the authors make us believe. And without such comovement or too little variation explained by too few factors, the approach has severe problems.III. Are Monetary Policy Shocks Identified and Identified Correctly?But let me give Boivin et al. the benefit of the doubt and hope that my arguments or calculations turn out to be somehow incorrect or not appropriate. That is, suppose that the authors did indeed capture the key comovements and 80% of the variance in the data with their seven factors, including interest rates and oil, even if the sample was truly large. Did they correctly identify monetary policy shocks? I have my doubts.For starters, it may be that all the movements due to monetary policy shocks have dropped from the sample, once one concentrates on the movement explained by the factors. Cochrane (1994) and many others have argued that monetary policy shocks explain no more than 20% of the movement in the data. It could be that much or even all of that is in the 20% not explained by the leading factors. It is easy to see how this can happen when extracting factors in an unrestricted manner. The authors smartly include the key monetary policy instrument in their factors, but even then, it could happen if the majority of the interest rate movements are not due to monetary policy shocks and if other parts of the movement in interest rates get captured by the seven‐factor dynamics and across‐variable correlation.To be more specific, it is worrisome that the fractions explained for M1 and M3 by the factors are among the lowest of all the series (see table 1). We used to think that moving money or moving interest rates is just as good a tool for a central bank to pick a particular point on the demand curve for money. But table 1 would have to be read as if that demand curve is subject to huge and idiosyncratic fluctuations having nothing to do with the rest of the economy. To put it differently, according to these estimates, money has little or nothing to do with monetary policy and the main movements in aggregate activity, but rather has a life on its own. If you believe this, you have an interesting research agenda at hand.But even leaving these arguments aside, I seriously wonder whether the approach to identify monetary policy shocks is reasonable. Section IV. A states that it is assumed that “the latent factors … and the oil price inflation … cannot respond contemporaneously to a surprise interest rate change.” The argument for this approach is in Bernanke et al. (2005), in which the authors argue that the movement in the factors is movement due to “slow‐moving” variables, since any additional systemic movement in the “fast‐moving” variables is one‐dimensional; they interpret this as being largely explained by the surprise in interest rate movements. But there are no such things as slow‐moving variables. After all, all variables have a nonzero one‐step‐ahead prediction error: they thus move fast with respect to something. The identifying assumption here really is that whatever it is they are reacting to contemporaneously and quickly, it cannot be monetary policy. Why should that be the case? If inflation and employment can suddenly jump a bit because of shifts in market demands, why can they not do so when monetary policy surprisingly changes interest rates?The defense seems to be that the impulse responses look conventional. But they don’t. As figure 1c in Boivin et al.’s paper shows, consumer price index inflation in Germany, France, Italy, and the euro area as a whole tends to move up rather than down after a monetary tightening, and wage inflation moves up in Germany, Italy, and Spain. Additionally, these responses are estimated with a fairly wide error band. The reaction seems to be somewhere between −0.3% and 0.3% in the year following the shock. By contrast, the reaction of GDP is fairly sharp and always down, ranging from −1% or below to about −0.2% in the year following the shock. That seems large compared to the (non)movement in inflation.A more convincing approach to identification is to employ the conventional wisdom and therefore sign restrictions for identification, as I have proposed in Uhlig (2005). With a panel of macroeconomic time series and a factor approach, as in the paper at hand, there are considerably more sign restrictions that can aid in identification, and the methodology then provides for considerably sharper bounds as well as reasonable results (see Ahmadi and Uhlig 2008).IV. Are the Data Informative about the Change after the Introduction of the Euro?I do not need to answer that question. Boivin et al. themselves provide ample warning in their paper that this is not so. Note in particular that no error bands have been provided to the post‐euro responses in figures 1a–c or the comparison pictures. Be wary of econometricians who draw conclusions by comparing means without telling you the degree of uncertainty! It is a fair guess that it is large. There simply was not much time‐series variation in monetary policy since the introduction of the euro. Figure 4 shows what is going on: large and heterogeneous movements in interest rates before the introduction of the euro. Hardly any movements afterward.Fig. 4. Short‐term interest rates in the EMU, authors’ data: euro area, Germany, France, Italy, Spain, Netherlands, and Belgium.View Large ImageDownload PowerPointThe authors are probably happy that the impulse responses did not change too dramatically for several key variables. Unfortunately, there are some in which the responses did change, leading us even further away from conventional wisdom. Consumption moves up after a monetary tightening. M3 moves up substantially now after a monetary tightening, quite in contrast to what happened before the euro.One explanation within the philosophy of the authors is that post‐euro monetary policy shocks identified here are really capturing movements in the stock market. For suppose that there are practically no monetary policy shocks and that monetary policy is instead also reacting to movements in some other fast‐moving variable, such as the stock market. Suppose an econometrician knew that and wanted to identify stock market surprise movements above and beyond those of slow‐moving variables. That econometrician would have proceeded exactly as the authors did, except that the impulse responses now would have to be interpreted as responses to stock market shocks rather than monetary policy shocks. How can one tell them apart? Again, sign restrictions might help.V. The Structural ModelThe paper complements the empirical analysis with a structural model that allows one to interpret the data from that vantage point. The key difficulty for this model is to explain the interest rate convergence in figure 4, happening without correspondingly large inflation differences. The authors readily admit this problem in Section V.C.1, when they write that “the basic version of the model cannot replicate the transmission of monetary policy observed in low‐credibility regimes since long‐term rates are tightly tied to expected future riskless short‐term rates.” One possibility would be to scrap the model at this point.The authors instead invent a clever deus ex machina: shocks to the uncovered interest rate parity (UIP) condition, which furthermore are tied with a key parameter to foreign (or “German”) monetary policy shocks (see Sec. V.B.3). Let me put it differently. Most of the interesting action in monetary policy in Europe over the last 20 years is the convergence process seen in figure 4. The authors sweep all that away by an add‐on to the UIP condition, which, however, has no further implications for aggregate dynamics. Next, they then seek to study how the changes in monetary policy from the pre‐euro regime to the post‐euro regime have affected macroeconomic variables. Shouldn’t one worry a bit that the baby has already been thrown out with the bath water? There is something really interesting happening here: it is the major big thing in the transition to the euro. We cannot quite put it into the theory, so thus let us ignore it? Shove it into a random shock, leaving everything else unchanged?I can see the desperation of the authors here, and I laud them for their frankness. Figure 4 is hard to explain within this theory. It is my guess too that it has a lot to do with perceptions of risk and updating the probabilities of membership in the European Monetary Union (EMU). So, having gotten so far in setting up this beautiful model and all, I understand that the quick fix of declaring it to be completely uninteresting and tangential was a way to proceed with the rest. But here is a memo to subsequent research: forget about the rest and instead put this at center stage, to understand the role of changing monetary policy in Europe!The authors instead plug in reaction coefficients of monetary policy, which are not obtained from the previous empirical exercise and not obtained from estimating the structural model, but instead from another empirical exercise described in Section V.B.2. One has to wonder whether this is consistent with the initial FAVAR approach or with the structural model at hand. Anyway, given that they use different coefficients before and after EMU, they find different quantitative results of their model. This is what the main comparison of pre‐euro and post‐euro in the paper rests on. Perhaps a more serious subsample stability test, using the structural DSGE model for estimation rather than an auxiliary model (or, at least, a with the of would be more to the empirical approach of the first of the paper is rather and more than may appear at For example, the identification assumption that all other variables do not to monetary policy shocks within the is But that essentially just from an of is the difference between a monetary policy that is happening at the of a and a monetary policy happening at the of the It just on the artificial way the time is up into periods. In figure one could read the impulse responses by moving them to the by one and having all variables within the to the monetary policy shock. it would be interesting to the monetary policy shocks as identified by the DSGE model to the monetary policy shocks as identified by the this a good model to study the impact of monetary policy in Note that there is no here. There is no that could get in from There is no worry about less when interest rates because there is no and no in the model to the of European and the many by policy and the are also essentially only that monetary policy about is due to the of for the model, and here are no different from But exchange rates substantially more than post‐euro inflation If are the main monetary policy perhaps be much more on the that the exchange rate from to the in the of than the created by some not being to but others differently, is the main of that it cannot its for in is it more important to them that their as they are to at wage which while the value of the And if that is the more important how much might that have a role in the transition to the for monetary policy, as a currency and for monetary policy sum, the model here is at the current of quantitative research on monetary policy. But I am that several of the most interesting which really matter for monetary policy and really matter for the transition to the EMU, have been out before the analysis has even And if so, then the problems with this approach are severe We then to different to the main of monetary Boivin et al.’s I really some may The paper is a analysis at the current of research and among the best that one can find on the at hand, building on the best I the authors for what they have this is no I am that the approach is and that can serious monetary policy discussions on this I fear that severe problems remain and that the route taken here is not yet convincing enough for others to follow. I have fear that the approach taken and the evidence presented by the authors are quite consistent with a world in which there is no comovement among the series at they are probably perfectly consistent with a world in which only very few factors matter at the European scale, but explaining considerably less than what the authors make us believe. And without such comovement or too little variation explained by too few factors, the approach has severe if there are factors, the monetary policy shocks may be the price and the sharp reaction of compared to the reaction of needs to the deus ex of shocks to UIP in to explain what be the key of monetary policy in Europe, convergence of interest rates (see fig. other key of central to monetary policy no role in the And if so, then the problems with this approach are severe We then to different to the main of monetary there is a between the and the this is for Whether these are fatal flaws or whether all this can be repaired or whether everything is all right after all is something that future research urgently needs to clarify before this approach should be put to wider and Harald the of Monetary Policy A FAVAR with University and University of in Boivin, and Monetary A (FAVAR) of in on Policy in Gertler, and and Monetary In of Monetary and University of Chicago in Are the of a to Monetary from an of Monetary in Previous articleNext article by by the of are reported from by the of the following articles this of the of large factor with factors, of The Monetary

The Global Financial Crisis (GFC) of 2007-2008 was a watershed moment that reshaped the landscape of monetary policy and financial regulation worldwide. This research paper delves into the pivotal role of monetary policy in fostering financial stability, drawing lessons from the tumultuous events of the GFC. Through an extensive review of literature and empirical analysis, this paper examines the various monetary policy tools employed by central banks to mitigate systemic risks and promote stability in the aftermath of the crisis. The paper begins by elucidating the complex interplay between monetary policy, financial markets, and systemic risk, emphasizing the importance of maintaining a delicate balance between price stability and financial stability. It explores the conventional and unconventional measures adopted by central banks during the crisis, including interest rate adjustments, liquidity provision, and asset purchase programs, and assesses their effectiveness in stabilizing financial markets and averting systemic collapse. Furthermore, the paper evaluates the evolution of monetary policy frameworks in response to the lessons learned from the GFC, such as the adoption of macroprudential tools and the reassessment of inflation targeting regimes. It analyzes the challenges and trade-offs faced by policymakers in pursuing dual objectives of price and financial stability, highlighting the need for a holistic approach to monetary policy formulation. Moreover, this paper examines the implications of post-crisis monetary policy measures on financial markets, asset prices, and economic activity, addressing concerns regarding moral hazard, excessive risk-taking, and asset price bubbles. It also discusses the role of regulatory reforms in strengthening the resilience of the financial system and enhancing the effectiveness of monetary policy transmission mechanisms.

We consider the optimality of various institutional arrangements for agencies that conduct macro-prudential regulation and monetary policy. When a central bank is in charge of price and financial stability, a new time inconsistency problem may arise. Ex-ante, the central bank chooses the socially optimal level of inflation. Ex-post, however, the central bank chooses inflation above the social optimum to reduce the real value of private debt. This inefficient outcome arises when macro-prudential policies cannot be adjusted as frequently as monetary. Importantly, this result arises even when the central bank is politically independent. We then consider the role of political pressures in the spirit of Barro and Gordon (1983). We show that if either the macro-prudential regulator or the central bank (or both) are not politically independent, separation of price and financial stability objectives does not deliver the social optimum. JEL Classification Numbers:C61, E21, G13

We consider the optimality of various institutional arrangements for agencies that conduct macro-prudential regulation and monetary policy. When a central bank is in charge of price and financial stability, a new time inconsistency problem may arise. Ex-ante, the central bank chooses the socially optimal level of inflation. Ex-post, however, the central bank chooses inflation above the social optimum to reduce the real value of private debt. This inefficient outcome arises when macro-prudential policies cannot be adjusted as frequently as monetary. Importantly, this result arises even when the central bank is politically independent. We then consider the role of political pressures in the spirit of Barro and Gordon (1983). We show that if either the macro-prudential regulator or the central bank (or both) are not politically independent, separation of price and financial stability objectives does not deliver the social optimum.

We consider the optimality of various institutional arrangements for agencies that conduct macro-prudential regulation and monetary policy. When a central bank is in charge of price and financial stability, a new time inconsistency problem may arise. Ex-ante, the central bank chooses the socially optimal level of inflation. Ex-post, however, the central bank chooses inflation above the social optimum to reduce the real value of private debt. This inefficient outcome arises when macro-prudential policies cannot be adjusted as frequently as monetary. Importantly, this result arises even when the central bank is politically independent. We then consider the role of political pressures in the spirit of Barro and Gordon (1983). We show that if either the macro-prudential regulator or the central bank (or both) are not politically independent, separation of price and financial stability objectives does not deliver the social optimum.

Over the past 30 years, monetary and macroprudential policy in Colombia evolved towards the pursuit of a low and credible inflation target and a stable financial system. The protracted inflation that began in the early seventies was defeated at the turn of the century with the help of the new framework for monetary policy formulation, inflation targeting. In the field of macroprudential policy, the financial crisis of the late nineties led to important institutional developments in the formulation and coordination of macroprudential policy, as well as in the assessment of systemic risk. Along with these developments, important lessons have been learnt. One is that, to preserve macroeconomic stability, the price stability objective must be complemented with the financial stability objective, as well as with macroprudential policy. Another lesson is that the new institutional framework for monetary policy formulation helped Banco de la República overcome 25 years of inflation, then called moderate inflation. The challenges for the future include to continue preserving price and financial stability, strengthening the role of the Banco de la República in macroprudential policy, and to continue strengthening the channels of international coordination and cooperation in macroprudential policy.

This paper studies the effect of wage rigidities on the transmission of fiscal and monetary policy shocks. We calculate downward wage rigidities across U.S. states using the Current Population Survey. These estimates are used to explain differences in the state-level economic effects of identical national shocks in interest rates and taxes. In line with the role of sticky wages in New Keynesian models, we find that contractionary monetary policy and tax shocks increase unemployment and decrease economic activity in rigid states considerably more than in flexible states. We also find larger and more persistent effects of monetary and tax policy shocks for states where the ratio between minimum and median wage is higher and for states that do not have right-to-work legislation.

PurposeThis paper analyzes real and welfare effects of a permanent change in inflation rate, focusing on macroprudential policy’ role and its interaction with monetary policy.Design/methodology/approachWhile investigating disinflation costs, the authors simulate a medium-scale dynamic general equilibrium model with borrowing constraints, credit frictions and macroprudential authority.FindingsProviding discussions on different policy scenarios in a context where still it is expected high inflation, there are three key contributions. First, when macroprudential authority actively operates to improve financial stability, losses caused by disinflation are limited. Second, a Taylor rule directly responding to financial variables might entail a trade-off between price and financial stability objectives, by increasing disinflation costs. Third, disinflation is welfare improving for savers, while costly for borrowers and banks. Indeed, while savers benefit from policies reducing price stickiness distortion, borrowers are worried about credit frictions, coming from collateral constraint.Practical implicationsThe paper suggests threefold policy implications: the macroprudential authority should actively intervene during a disinflation process to minimize costs and financial instability deriving from it; policymakers should implement a disinflationary policy stabilizing also output; the central bank and the macroprudential regulator should pursue financial and price stability goals, separately.Originality/valueThis paper is the first attempt to study effects of a permanent inflation target reduction in focusing on the macroprudential policy’ role.

Toward a “New” Inflation-Targeting Framework:The Case of Uruguay Matías Escudero (bio), Martín Gonzalez-Rozada (bio), and Martín Solá (bio) Empirical studies in the late 1980s, suggesting that monetary policy might influence the short-run dynamics of the real economy, contributed to the widespread use of inflation-targeting policy rules by central banks. More recent research on monetary economics provide a theoretical framework for the implementation of such rules. For example, Taylor (1993) recommends the use of a simple interest rate rule that is a function of inflation and the output gap. Nowadays it is standard to use the dynamic stochastic general equilibrium (DSGE) model and New Keynesian models to evaluate the effects of Federal Reserve policies. The success of alternative policy rules is usually assessed in terms of the short-run dynamics of the relevant macroeconomic variables. Many central banks use the reference interest rate as a conventional instrument to signal to the public changes in the monetary policy stance. In this way they attempt to achieve the convergence of inflation, and its expectation, upon a given target. Recently several central banks in Latin American countries (LAC) adopted stabilization policies using conventional and unconventional tools to meet their inflationary or financial stability objectives. Among the unconventional tools are reserve requirements (see Glocker and Towbin, 2012, and the references there for several emerging countries outside Latin America; for LACs, see Carvalho and Acevedo, 2008; Ocampo and Tovar, 2003; Ribeira and Barbosa, 2005; [End Page 89] Vargas and others, 2010). In highly dollarized LAC, changes in reserve requirements have been used as a macroeconomic prudential tool, with the main objective of accumulating liquidity being to address financial stress (see León and Quispe, 2010; Vargas and Cardozo, 2012; Tovar, Garcia-Escribano, and Vera Martin, 2012; Carrera and Vega, 2012) and to complement the use of the reference interest rate in order to achieve the inflation target objective (see Comunicados del COPOM, 2007–12, for Uruguay; Glocker and Towbin, 2012). However, most of the literature on the use of reserve requirements in LAC is not only empirical but is mostly focused on the impact of these requirements on interest spreads and bank profits. The main conclusion from those studies is that an increase in reserve requirements induces an increment in interest rate spreads and a fall in bank profits. An increment in reserve requirements acts as a tax on the banks and widens the spread between lending and deposit rates (see Glocker and Towbin, 2012). Monetary policy shocks typically generate a short-run fall in inflation through a contractionary effect on economic activity. Furthermore, in highly dollarized economies, these shocks may also have undesirable effects on the foreign exchange market (Montoro and Moreno, 2011). Therefore, the use of reserve requirements may be an important unconventional monetary policy tool, since it could help to achieve the inflationary target without having major effects on the exchange rate market (that is, without attracting capital inflows), and it may also reduce the negative impact of the increase in interest rates on output. Nevertheless, the short-run effects of this type of policy are not obvious, since it depends on, among other things, the combination of instruments chosen to achieve the target and the type of target under consideration. The main objective of this paper is to describe the impact of using conventional and unconventional tools to meet inflationary or financial stability objectives in a dollarized economy. This paper explores, for the Uruguayan economy, the impact of these policies, using a relatively standard model of a small open economy with sticky prices, financial frictions, and a banking sector that is subject to legal reserve requirements.1 The three main findings of the paper are as follows. One, reserve requirements can be used to achieve the inflationary objectives of the central bank. [End Page 90] However, reducing inflation using this instrument also produces a real appreciation of the Uruguayan peso. Two, when the central bank uses the monetary policy rate as an instrument, the effect of the reserve requirements is to reduce the negative impact on consumption, investment, and output of an eventual increase in the interest rate. Nevertheless, the quantitative results in terms...

This study adds to a recent and growing literature that assesses the effects of macroprudential policy. We compare the effects of monetary policy and loan-to-value ratio shocks for Korea, an inflation-targeting economy and an active user of loan-to-value limits. We identify shocks using sign restricted structural VARs and rely on a recent approach within this method to conduct structural inference. This study finds that both monetary policy and loan-to-value ratio shocks have effects during the period that our sign restrictions applies on different measures of credit, i.e., real bank credit, real total credit and real household credit, as well as on real output, real consumption and real investment. We find though that loan-to-value ratio shocks have negligible effects on the price level. Both shocks, however, have non-negligible effects on real house prices, evidence that go beyond the period of the imposed sign restrictions. These findings indicate that for the period covered by this study, limits on loan-to-value achieved their financial stability objectives in Korea in terms of limiting credit and house price appreciation under an inflation-targeting regime. Furthermore, it attained these objectives without posing any threat to its price stability objective. Overall, these findings suggest that limits on loan-to-value have important aggregate consequences despite it being a sectoral, targeted policy instrument.

Monetary policy can achieve average inflation equal to a given inflation target and, at best, a good compromise between inflation variability and output-gap variability. Monetary policy cannot completely stabilize either inflation or the output gap. Increased credibility in the form of inflation expectations anchored on the inflation target will reduce the variability of inflation and the output gap. Central banks can improve transparency and accountability by specifying not only an inflation target but also the dislike of output-gap variability relative to inflation variability. Central banks can best achieve both the long-run inflation target and the best compromise between inflation and output-gap stability by engaging in forecast targeting,' where the bank selects the feasible combination of inflation and output-gap projections that minimize the loss function and the corresponding instrument-rate plan and sets the instrument-rate accordingly. Forecast targeting implies that the instrument responds to all information that significantly affects the projections of inflation and the output gap. Therefore it cannot be expressed in terms of a simple instrument rule, like a Taylor rule. The objective of financial stability, including a well-functioning payment system, can conveniently be considered as a restriction on monetary policy that does not bind in normal times, but does bind in times of financial crises. By producing and publishing Financial Stability Reports with indicators of financial stability, the central bank can monitor the degree of financial stability and issue warnings to concerned agents and authorities in due time and this way avoid deteriorating financial stability. Forecast targeting implies that asset-price developments and potential asset-price bubbles are taken into account and responded to the extent that they are deemed to affect the projections of the target variables, inflation and the output gap. In most cases, it will be difficult to make precise judgments, though, especially to identify bubbles with reasonable certainty. The zero bound, liquidity traps and risks of deflation are serious concerns for a monetary policy aimed at low inflation. Forecast targeting with a symmetric positive inflation target keeps the risk of the zero bound, liquidity traps and deflation small. Prudent central banks may want to prepare in advance contingency plans for situations when a series of bad shocks substantially increases the risk