Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94

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Inflation scares lead to bad economic outcomes because the rise in inflation expectations leads not only to higher actual inflation but also to monetary policy tightening to get inflation back under control that often results in large declines in economic activity. Commitment to a nominal anchor is therefore a crucial element in the successful management of expectations; and it is a key feature of recent theory on optimal monetary policy, referred to as the new-neoclassical or new-Keynesian synthesis Goodfriend and King, ; Clarida, Gali, and Gertler, ; Woodford, Research that outlined how asymmetric information could impede the efficient functioning of the financial system Akerlof, ; Myers and Majluf, ; and Greenwald, Stiglitz, and Weiss, suggests an important link between business cycle fluctuations and financial frictions.

When shocks to the financial system increase information asymmetry so that financial frictions increase dramatically, financial instability results, and the financial system is no longer able to channel funds to those with productive investment opportunities, with the result that the economy can experience a severe economic downturn Mishkin, The rediscovery of Irving Fisher's paper on the Great Depression led to the recognition that financial instability played a central role in the collapse of economic activity during that period Mishkin, ; Bernanke, ; and the survey in Calomiris, , and it has spawned a large literature on the role of financial frictions in business cycle fluctuations e.

Indeed, it is now well understood that the most severe business cycle downturns are always associated with financial instability, not only in advanced countries but also in emerging-market countries Mishkin, , Minimizing output fluctuations thus requires that monetary policy factors in the impact of financial frictions on economic activity. Scientific principles are all well and good, but they have to be applied in a practical way to produce good policies.

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The scientific principles from physics or biology provide important guidance for real-world projects, but it is with the applied fields of engineering and medicine that we build bridges and cure patients. Within economics, it is also important to delineate the use of scientific principles in policymaking, as this type of categorization helps us understand where progress has been made and where further progress is most needed.

I will categorize the applied science of monetary policy as those aspects that involve systematic, or algorithmic, methods such as the development of econometric models. Other, more judgmental aspects of policymaking are what I will call the "art" of policymaking. So, how have the basic scientific principles outlined above been used algorithmically?

I focus particularly on the U.

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Early Keynesian econometric models of the macroeconomy did not give monetary policy a prominent role for example, Tinbergen, ; Adelman and Adelman, ; Klein, In contrast, the policy-oriented models developed in the ssuch as the MIT-Penn-SSRC MPS model, developed by Franco Modigliani and collaborators and used as the workhorse model for policy analysis at the Federal Reserve until incorporated a very important role for monetary policy, broadly similar to the main channels of the monetary policy transmission mechanism that are embedded in the current generation of models.

Very early versions of the MPS model did display a long-run tradeoff between unemployment and inflation, as the principle that there should be no long-run tradeoff took some time to be accepted e. By the early s, the principle of no long-run tradeoff was fully ensconced in the MPS model by the adoption of an accelerationist Phillips curve Pierce and Enzler, ; Brayton and others, The recognition in their models that lower unemployment could not be bought by accepting higher inflation was a factor driving central banks to adopt anti-inflationary policies by the s.

Although accelerationist Phillips curves became standard in macroeconometric models used at central banks like the MPS model through the s, expectational elements were still largely missing. The next generation of models emphasized the importance of expectations. Policy simulations to help guide monetary policy decisions, such as those that are shown to the Federal Open Market Committee FOMC , explicitly emphasize assumptions about future expectations and how they are formed.

Policymakers have thus come to recognize that their decisions about policy involve not only the current policy setting but also how they may be thinking about future policy settings. The focus on optimizing economic agents coming out of the rational expectations revolution has led to modeling efforts at central banks that not only make use of rational expectations, but that are also grounded on sounder microfoundations.

Specifically, these models build on two recent literatures, real business cycle theory e.

In contrast to older Keynesian macro modeling, new-Keynesian theory provides microfoundations for Keynesian concepts such as nominal rigidities, the non-neutrality of money, and the inefficiency of business cycle fluctuations by deriving them from optimizing behavior. The real business cycle approach makes use of stochastic general equilibrium growth models with representative, optimizing agents.

The resulting new class of models, in which new-Keynesian features such as nominal rigidities and monopolistic competition are added to the frictionless real business models, have become known as dynamic stochastic general equilibrium DSGE models. Simple versions of such models have already provided a framework in which to think about key aspects of monetary policy designinsights perhaps best illustrated in the Woodford discussion of policy issues in the now-textbook, three-equation new-Keynesian model.

Larger, more empirically-motivated DSGE models are now in their early stages of development and are beginning to be used for policy analysis at central banks e. There are two very important implications from policy analysis with DSGE models, as emphasized by Gali and Gertler forthcoming : First, "monetary transmission depends critically on private sector expectations of the future path of the central bank's policy instrument.

The basic logic of the Taylor principlethat is, raising nominal interest rates more than one-for-one in response to an increase in inflationwas developed in conjunction with the analysis of Taylor's multicountry model and other macroeconometric models Taylor, a,b; Bryant, Hooper, and Mann, However, although the Taylor principle is a necessary condition for good monetary policy outcomes, it is not sufficient.

Central bankers require knowledge about how much difference the Taylor principle makes to monetary policy outcomes. They also require an understanding of how much greater than one the response of nominal interest rates should be to increases in inflation and also need to know how the policy rate should respond to other variables. Studying the performance of different rules in macroeconometric models has become a major enterprise at central banks, and the conclusion is that the Taylor principle is indeed very important.

Analysis of policy rules in macroeconometric models that are not fully based on optimizing agents has been very extensive e. The second principle, and the sixth through the eighth principles - which emphasize the benefits of price stability and the importance of the time-inconsistency problem, central bank independence and a commitment to a nominal anchor - have important applications to the design of monetary policy institutions.

The argument that independent central banks perform better and are better able to resist the pressures for overly expansionary monetary policy arising from the time-inconsistency problem has led to a remarkable trend toward increasing central bank independence. Before the s, only a few central banks were highly independent, most notably the Bundesbank, the Swiss National Bank, and, to a somewhat lesser extent, the Federal Reserve. Now almost all central banks in advanced countries and many in emerging-market countries have central banks with a level of independence on par with or exceeding that of the Federal Reserve.

In the s, greater independence was granted to central banks in such diverse countries as Japan, New Zealand, South Korea, Sweden, the United Kingdom, and those in the euro zone. The increasing recognition of the time-inconsistency problem and the role of a nominal anchor in producing better economic outcomes has been an important impetus behind increasing central banks' commitments to nominal anchors.

One resulting dramatic development in recent years has been a new monetary policy strategy, inflation targetingthe public announcement of medium-term numerical targets for inflation with commitment and accountability to achieve this target, along with increased transparency of the monetary policy strategy through communication with the public Bernanke and Mishkin, There has been a remarkable trend toward inflation targeting, which was adopted first by New Zealand in March , and has since been adopted by an additional 23 countries Rose, The evidence, is in general quite favorable to inflation targeting, although countries that have adopted inflation targeting have not improved their monetary policy performance beyond that of nontargeters in industrial countries that have had successful monetary policy e.

And, in contrast to other monetary policy regimes, no country with its own currency that has adopted inflation targeting has been forced to abandon it. The scientific principle that financial frictions matter to economic fluctuations has led to increased attention at central banks to concerns about financial stability.


Many central banks now publish so-called Financial Stability reports, which examine vulnerabilities to the financial system that could have negative consequences for economic activity in the future. Other central banks are involved in prudential regulation and supervision of the financial system to reduce excessive risk-taking that could lead to financial instability. Central banks also have designed their lending facilities to improve their ability to function as a lender of last resort, so they can provide liquidity quickly to the financial system in case of financial disruptions.

I have argued that there have been major advances in the science of monetary policy in recent years, both in terms of basic scientific principles and applications of these principles to the real world of monetary policymaking. Monetary policy has indeed become more of a science. There are, however, serious limitations to the science of monetary policy. There are several reasons why judgment will always be an important element in the conduct of monetary policy. First, models are able to make use of only a small fraction of the potentially valuable information that tells us about the complexity of the economy.

For example, there are very high frequency datamonthly, weekly, and dailythat are not incorporated into macroeconometric models, which are usually estimated on quarterly data. These high-frequency data can often be very informative about the near-term dynamics of the economy and are used judgmentally by central-bank forecasters e. Second, information that can be very useful in forecasting the economy or deciding whether a particular model makes sense is often anecdotal and is thus not easily quantifiable.

The Federal Reserve makes extensive use of anecdotal information in producing its forecasts. The staff at the Board and the Federal Reserve Banks monitor a huge amount of anecdotal information, and such information is discussed extensively in the publicly released Beige Book, which reports information from contacts in the Federal Reserve Districts, and by the participants in FOMC meetings.

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Third, although monetary policy makers make extensive use of models in both forecasting and evaluating different policies, they are never sure that one model is the correct one. Active, and sometimes bitter, debates about which modeling approaches are the right ones are ongoing in macroeconomics, and there often is not a consensus on the best model.

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As a result, central banks must express some degree of humility regarding their knowledge of the structural relationships that determine activity and prices. This humility is readily apparent in the practice at central banks, which involves looking at many different modelsstructural, reduced-form, general equilibrium and partial equilibrium, and continually using judgment to decide which models are most informative. Fourth, the economy does not stand still but, rather, changes over time. Economic relationships are thus unlikely to remain stable, and it is not always clear how these relationships are changing.

Fifth, as part of managing expectations, monetary policy makers communicate with economic agents who are not automatons but instead process information in complex ways. Subtle changes can make a big difference in the effectiveness of communication strategiesi. Although, for the reasons outlined above, judgment will always be a necessary element of monetary policy, good decisions require that judgment be disciplinednot too ad hocand be well informed by the science of monetary policy.

As Blinder , p. The last scientific principle discussed in the paper's first section emphasizes the link between financial frictions and the business cycle, but it is unfortunately quite hard to model the role of these frictions in a general equilibrium, macroeconometric model. The late s saw a boom and then a major bust in the commercial real estate market leading to huge loan losses that caused a substantial fall in capital at depository institutions banks. At the same time, regulators were raising bank capital requirements to ensure compliance with the Basel Accord. The resulting capital shortfalls meant that banks had to either raise new capital or restrict their asset growth by cutting back on lending.

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Because of their weak condition, banks could not raise much new capital, so they chose the latter course. The resulting slowdown in the growth of credit was unprecedented in the post-World War II era Reifschneider, Stockton, and Wilcox, Because banks have informational advantages in making certain loans e. Although the large-scale macromodel then in use at the Federal Reserve Board did not explicitly have financial frictions in its equations, officials at the Federal Reserve were aware that these frictions could be very important and were concerned that they might be playing a critical role at that juncture.

In part reflecting this concern, many Fed economists were actively engaged in research on the impact of bank credit on economic activity. This research, together with anecdotal reports that businesses were finding themselves credit constrained and survey information indicating that bank credit standards were being tightened, gave rise to the view among Federal Reserve policymakers that the capital crunch at banks was noticeably constraining credit flows and hence spending by households and firms.

Indeed, Federal Reserve Chairman Alan Greenspan suggested that financial conditions in the earlys was holding back activity like a "mile per hour headwind," and in that period the FOMC reduced the federal funds rate to levels well below that suggested by the Taylor rule e. Indeed, the recovery from the recession was very slow, and the Fed kept the federal funds rate at 3 percent which, with an inflation rate of around 3 percent, implied a real rate of zero until February of a very accommodative policy stance.

The Fed's expansionary policy stance at the time has in hindsight been judged as very successful, with the economy finally recovering and inflation remaining contained. By the beginning of , the unemployment rate had declined to 5. The forecast of a 5 percent unemployment rate was well below most estimates of the NAIRU nonaccelerating inflation rate of unemployment. As a result, the staff forecast was for a rise in inflation Svensson and Tetlow, The staff forecast and the recommendation in the February Bluebook suggested that a period of monetary policy tightening would be needed to "forestall a continuous rise in core inflation" Federal Reserve Board, , p.

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Although the FOMC did raise the federal funds rate in March , it desisted from raising rates further; in fact, the FOMC reduced the federal funds rate in the fall of after the episode involving the Long-Term Capital Management hedge fund and the Russian-bond meltdown. Despite an unemployment rate continually below estimates of the NAIRU, the outcome was not the acceleration that the Board staff's models predicted Svensson and Tetlow, ; Tetlow and Ironside, but instead a decline in the inflation rate. Why did the FOMC hold off and not raise rates in the face of economic growth that was forecasted to be far in excess of potential growtha decision that, ex post, appears to have resulted in desirable outcomes for inflation and employment?

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The answer is that Fed Chairman Greenspan guessed correctly that something unusual was going on with productivity. For example, he was hearing from businesspeople that new information technologies were transforming their businesses, making it easier for them to raise productivity. He was also a big fan of the historical work by Paul David , which suggested that new technological innovations often took years to produce accelerations in productivity in the overall economy Meyer, Chairman Greenspan was led to the conclusion that the trend in productivity growth was accelerating, a conclusion that the Board staff's forecast did not come to fully accept until late Svensson and Tetlow, Moreover, he appeared to be convinced that the acceleration in productivity would cap inflationary pressures, implying that inflation would not accelerate even with rapid economic growth.

The types of information used to foresee the effects of a productivity acceleration are inherently difficult to incorporate into formal models. This is obvious with respect to the anecdotes I have mentioned. But even the systematic data available at the time required the use of judgment. For example, part of the story of the late s reflected the different signals being sent by real-time measures of gross domestic product and gross domestic incomeor at least the component of the latter produced by nonfinancial corporations, which is perhaps better measured Corrado and Slifman, and provided some advance signal of the productivity acceleration.


Of course, these two measuresGDP and GDIare the same in our formal models, and only a judgmental filtering of the information content in each can be useful in real time. Good judgment benefits not only from a good feel for the data and the successful processing of anecdotal information but also from the use of scientific models, and the lates episode is no exception. Their simulations produced several results that were consistent with what seemed to be happening. An acceleration of productivity would raise profits and the value of equities, which would boost aggregate demand because higher stock values would stimulate business investment and boost consumer spending through wealth effects.

Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94 Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94
Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94 Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94
Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94 Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94
Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94 Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94
Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94 Monetary Transmission and Financial Indexation - Evidence from the Chilean Economy: 94

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