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I believe that during the last financial crisis, macroeconomists (and I include myself amongthem) failed the country, and indeed the world. In September 2008, central bankers werein desperate need of a playbook that offered a systematic plan of attack to deal with fast-evolving circumstances. Macroeconomics should have been able to provide that playbook.It could not. Of course, from a longer view, macroeconomists let policymakers down muchearlier, because they did not provide policymakers with rules to avoid the circumstancesthat led to the global financial meltdown.

Because of this failure, macroeconomics and its practitioners have received a greatdeal of pointed criticism both during and after the crisis. Some of this criticism has comefrom policymakers and the media, but much has come from other economists. Of course,macroeconomists have responded with considerable vigor, but the overall debateinevitably leads the general public to wonder: What is the value and applicability ofmacroeconomics as currently practiced?

The answer is that macroeconomics has made important advances in recent years. Thoseadvances—coupled with a rededicated effort following this recent economic episode—position macroeconomics to make useful contributions topolicymaking in the future. In this essay, I want to tell thestory of how macroeconomics got to this point, of what the

Narayana Kocherlakota*

President

* The author thanks Cristina Arellano, Harold Cole, Gauti Eggertsson, Barbara McCutcheon, Lee Ohanian, Kjetil Storesletten,and Kei-Mu Yi for their valuable input.

Modern MacroeconomicModels as Tools

for Economic Policy

key questions are that still vex the science, and ofwhy I am hopeful that macroeconomics is poised tobenefit policymakers going forward.

According to the media, the defining struggle ofmacroeconomics is between people: those wholike government and those who don’t. In my essay,the defining struggle in macroeconomics isbetween people and technology. Macroeconomiststry to determine the answers to questions aboutentire economies. These questions really concernthe outcomes of large-scale experiments, but thereis no sensible way to perform such experiments innational or global laboratories. Instead, macro-economists must conduct their experiments insideeconomic models that are highly stylized and sim-plified versions of reality. I will show that macro-economists always leave many possibly importantfeatures of the world out of their models. It mayseem to outside observers that macroeconomistsmake these omissions out of choice. Far moreoften, though, macroeconomists abstract fromaspects of reality because they must. At any givenpoint in time, there are significant conceptual andcomputational limitations that restrict whatmacroeconomists can do. The evolution of thefield is about the eroding of these barriers.

OUTLINEThis essay describes the current state of macroeco-nomic modeling and its relationship to the world ofpolicymaking. Modern macro models can be tracedback to a revolution that began in the 1980s inresponse to a powerful critique authored by Robert

Lucas (1976). The revolution has led to the use ofmodels that share five key features:

a. They specify budget constraints for households,technologies for firms, and resource constraintsfor the overall economy.

b. They specify household preferences and firmobjectives.

c. They assume forward-looking behavior forfirms and households.

d. They include the shocks that firms andhouseholds face.

e. They are models of the entire macroeconomy.

The original modern macro models developed in the1980s implied that there was little role for govern-ment stabilization. However, since then, there havebeen enormous innovations in the availability ofhousehold-level and firm-level data, in computingtechnology, and in theoretical reasoning. Theseadvances mean that current models can have featuresthat had to be excluded in the 1980s. It is commonnow, for example, to use models in which firms canonly adjust their prices and wages infrequently. Inother widely used models, firms or households areunable to fully insure against shocks, such as loss ofmarket share or employment, and face restrictions ontheir abilities to borrow. Unlike the models of the1980s, these newer models do imply that governmentstabilization policy can be useful. However, as I willshow, the desired policies are very different fromthose implied by the models of the 1960s or 1970s.

As noted above, despite advances in macroeco-nomics, there is much left to accomplish. I highlight

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three particular weaknesses of current macro mod-els. First, few, if any, models treat financial, pricing,and labor market frictions jointly. Second, even inmacro models that contain financial market fric-tions, the treatment of banks and other financialinstitutions is quite crude. Finally, and most trou-bling, macro models are driven by patently unreal-istic shocks. These deficiencies were largely—andprobably rightly—ignored during the “GreatModeration” period of 1982–2007, when there wereonly two small recessions in the United States. Theweaknesses need to be addressed in the wake ofmore recent events.

Finally, I turn to the policy world. The evolutionof macroeconomic models had relatively little effecton policymaking until the middle part of thisdecade.1 At that point, many central banks began touse modern macroeconomic models with pricerigidities for forecasting and policy evaluation. Thisstep is a highly desirable one. However, as far as I amaware, no central bank is using a model in whichheterogeneity among agents or firms plays a promi-nent role. I discuss why this omission strikes me asimportant.

MODERN MACRO MODELSI begin by laying out the basic ingredients of modernmacro models. I discuss the freshwater-saltwaterdivide of the 1980s. I argue that this division hasbeen eradicated, in large part by better computers.

The Five IngredientsThe macro models used in the 1960s and 1970s werebased on large numbers of interlocking demand andsupply relationships estimated using various kinds ofdata. In his powerful critique, Lucas demonstratedthat the demand and supply relationships estimatedusing data generated from one macroeconomic poli-cy regime would necessarily change when the policyregime changed. Hence, such estimated relation-ships, while useful for forecasting when the macropolicy regime was kept fixed, could not be of use inevaluating the impact of policy regime changes.

How can macroeconomists get around the Lucascritique? The key is to build models that are specif-ically based on the aspects of the economy that theyall agree are beyond the control of the government.Thus, the Lucas critique says that if the FederalReserve alters its interest rate rule, the estimatedrelationship between investment and interest ratesmust change. However, this relationship is ultimatelygrounded in more fundamental features of theeconomy, such as the technology of capital accumu-lation and people’s preferences for consumptiontoday versus in the future. If the Federal Reservechanges its rule, people’s preferences and firms’technologies don’t change. Models that are ground-ed in these more fundamental (sometimes calledstructural) features of the economy can do a betterjob of figuring out the impact of a change in FederalReserve policy.

1 To be clear: Policymakers did learn some important qualitative lessons from modern macro. Thus, in the wake of FinnKydland and Edward Prescott (1977), there was a much more widespread appreciation of the value of rules relative to dis-cretion. However, policymakers continued to use largely outdated models for assessing the quantitative impact of policychanges.

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Beginning in the 1980s, this argument (and otherforces) led to the growing use of what I will term“modern macro” models. As I outlined earlier, mod-ern macro models have five key features. First, theymust include resource constraints and budget con-straints. Resource constraints show how the mem-bers of society can use costly inputs like labor andcapital to create goods. Budget constraints dictatethat no entity can increase its spending withoutincreasing its revenue (either now or in the future).These constraints prevent anyone in the economy(including the government) from creating some-thing from nothing.

Second, the models must include an explicitdescription of individual preferences and firmobjectives. Without such a description, as discussedabove, the models are subject to the Lucas critique.

Third, the models generally feature forward-look-ing behavior. Macroeconomists all agree that house-holds’ and firms’ actions today depend on theirexpectations of the future. Thus, households thatexpect better times in the future will try to borrow.Their demand for loans will drive up interest rates. Ananalyst who ignored these expectations would not beable to understand the behavior of interest rates.

In most macro models, households and firmshave what are called rational expectations. This termmeans that they form forecasts about the future as ifthey were statisticians. It does not mean that house-holds and firms in the model are always—or ever—right about the future. However, it does mean thathouseholds and firms cannot make better forecastsgiven their available information.

Using rational expectations has been attractive tomacroeconomists (and others) because it provides asimple and unified way to approach the modeling offorward-looking behavior in a wide range of set-tings. However, it is also clearly unrealistic. Long-standing research agendas by prominent membersof the profession (Christopher Sims and ThomasSargent, among others) explore the consequences ofrelaxing the assumption. Doing so has proven chal-lenging both conceptually and computationally.

Forward-looking households and firms want totake account of the risks that might affect them. Forthis reason, the fourth key ingredient of modernmacro models is that they are explicit about theshocks that affect the economy. For example, mostmacro models assume that the rate of technologicalprogress is random. Expectations about this variablematter: Households will work harder and firms investmore if they expect rapid technological progress.

Finally, just like old macro models, modernmacro models are designed to be mathematical for-malizations of the entire economy. This ambitiousapproach is frustrating for many outside the field.Many economists like verbal intuitions as a way toconvey understanding. Verbal intuition can be help-ful in understanding bits and pieces of macro mod-els. However, it is almost always misleading abouthow they fit together. It is exactly the imprecisionand incompleteness of verbal intuition that forcesmacroeconomists to include the entire economy intheir models.

When these five ingredients are put together, theresult is what are often termed dynamic stochastic

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general equilibrium (DSGE) macro models. Dynamicrefers to the forward-looking behavior of householdsand firms. Stochastic refers to the inclusion ofshocks. General refers to the inclusion of the entireeconomy. Finally, equilibrium refers to the inclusionof explicit constraints and objectives for the house-holds and firms.

Historical Digression:Freshwater versus SaltwaterThe switch to modern macro models led to a fiercecontroversy within the field in the 1980s. Users ofthe new models (called “freshwater” economistsbecause their universities were located on lakes andrivers) brought a new methodology. But they alsohad a surprising substantive finding to offer. Theyargued that a large fraction of aggregate fluctuationscould be understood as an efficient response toshocks that affected the entire economy. As such,most, if not all, government stabilization policy wasinefficient.

The intuition of the result seemed especiallyclear in the wake of the oil crisis of the 1970s.Suppose a country has no oil, but it needs oil toproduce goods. If the price of oil goes up, then it iseconomically efficient for people in the economy towork less and produce less output. Faced with thisshock, the government of the oil-importing coun-try could generate more output in a number ofways. It could buy oil from overseas and resell it ata lower domestic price. Alternatively, it could hirethe freed-up workers at high wages to producepublic goods. However, both of these options

require the government to raise taxes. In the mod-els of the freshwater camp, the benefits of the stim-ulus are outweighed by the costs of the taxes. Therecession generated by the increase in the oil priceis efficient.

Scholars in the opposing (“saltwater”) campargued that in a large economy like the UnitedStates, it is implausible for the fluctuations in the effi-cient level of aggregate output to be as large as thefluctuations in the observed level of output. Theypointed especially to downturns like the GreatDepression as being obvious counterexamples.

The divide between freshwater and saltwatereconomists lives on in newspaper columns and theblogosphere. (More troubling, it may also live on inthe minds of at least some policymakers.) However,the freshwater-saltwater debate has largely vanishedin the academe.

My own idiosyncratic view is that the divisionwas a consequence of the limited computing tech-nologies and techniques that were available in the1980s. To solve a generic macro model, a vast arrayof time- and state-dependent quantities and pricesmust be computed. These quantities and pricesinteract in potentially complex ways, and so theproblem can be quite daunting.

However, this complicated interaction simpli-fies greatly if the model is such that its impliedquantities maximize a measure of social welfare.Given the primitive state of computational tools,most researchers could only solve models of thiskind. But—almost coincidentally—in these mod-els, all government interventions (including all

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forms of stabilization policy) are undesirable.With the advent of better computers, better the-

ory, and better programming, it is possible to solvea much wider class of modern macro models. As aresult, the freshwater-saltwater divide has disap-peared. Both camps have won (and I guess lost).On the one hand, the freshwater camp won interms of its modeling methodology. Substantively,too, there is a general recognition that some non-trivial fraction of aggregate fluctuations is actuallyefficient in nature.

On the other hand, the saltwater camp has alsowon, because it is generally agreed that some formsof stabilization policy are useful. As I will show,though, these stabilization policies take a differentform from that implied by the older models (fromthe 1960s and 1970s).

STATE OF MODERN MACROIn this section, I discuss some of the successes ofmodern macro. I point to some deficiencies in thecurrent state of knowledge and discuss what I per-ceive as useful steps forward.

SuccessesIn the macro models of the 1980s, all mutually ben-eficial trades occur without delay. This assumptionof frictionless exchange made solving these modelseasy. However, it also made the models less com-pelling. To a large extent, the progress in macro inthe past 25 years has been about being able to solvemodels that incorporate more realistic versions ofthe exchange process. This evolution has taken place

in many ways, but I will focus on two that I see asparticularly important.

Pricing Frictions:The New Keynesian SynthesisIf the Federal Reserve injects a lot of money into theeconomy, then there is more money chasing fewergoods. This extra money puts upward pressure onprices. If all firms changed prices continuously, thenthis upward pressure would manifest itself in animmediate jump in the price level. But this immedi-ate jump would have little effect on the economy.Essentially, such a change would be like a simplechange of units (akin to recalculating distances ininches instead of feet).

In the real world, though, firms change pricesonly infrequently. It is impossible for the increase inmoney to generate an immediate jump in the pricelevel. Instead, since most prices remain fixed, theextra money generates more demand on the part ofhouseholds and in that way generates more produc-tion. Eventually, prices adjust, and these effects ondemand and production vanish. But infrequentprice adjustment means that monetary policy canhave short-run effects on real output.

Because of these considerations, many modernmacro models are centered on infrequent price andwage adjustments. These models are often calledsticky price or New Keynesian models. They providea foundation for a coherent normative and positiveanalysis of monetary policy in the face of shocks.This analysis has led to new and important insights.It is true that, as in the models of the 1960s and

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1970s, monetary policymakers in New Keynesianmodels are trying to minimize output gaps withoutgenerating too much volatility in inflation. However,in the models of the 1960s and 1970s, output gaprefers to the deviation between observed output andsome measure of potential output that is growing ata roughly constant rate. In contrast, in modernsticky price models, output gap refers to the devia-tions between observed output and efficient output.The modern models specifically allow for the possi-bility that efficient output may move down inresponse to adverse shocks. This difference in for-mulation can lead to strikingly different policyimplications.

FINANCIAL MARKET FRICTIONSThe modern macro models of the 1980s and theNew Keynesian models either implicitly or explicitlyassume that firms and households can fully capital-ize all future incomes through loan or bond markets.The models also assume that firms and householdscan buy insurance against all possible forms of risk.This assumption of a frictionless financial market isclearly unrealistic.

Over the past 25 years, a great deal of work hasused models that incorporate financial market fric-tions. Most of these models cannot be solved reliablyusing graphical techniques or pencil and paper. As aconsequence, progress is closely tied to advances incomputational speed.

Why are these models so hard to solve? The keydifficulty is that, within these models, the distribu-tion of financial wealth evolves over time. Suppose,

for example, that a worker loses his or her job. If theworker were fully insured against this outcome, theworker’s wealth would not be affected by this loss.However, in a model with only partial insurance, theworker will run down his or her savings to getthrough this unemployment spell. The worker’sfinancial wealth will be lower as a result of beingunemployed.

In this fashion, workers with different histories ofunemployment will have different financial wealth.Aggregate shocks (booms or busts) will influence thedistribution of financial wealth. In turn, as the wealthdistribution changes over time, it feeds back in com-plex ways into aggregate economic outcomes.

From a policy perspective, these models lead toa new and better understanding of the costs of eco-nomic downturns. For example, consider the latestrecession. During the four quarters from June 2008through June 2009, per capita gross domestic prod-uct in the United States fell by roughly 4 percent. Ina model with no asset market frictions, all peopleshare this proportionate loss evenly and all lose twoweeks’ pay. Such a loss is certainly noticeable.However, I would argue that it is not a huge loss.Put it this way: This scale of loss means everyone inthe United States ends up being paid in June 2009the same (inflation-adjusted) amount that theymade in June 2006.

However, the models with asset market frictions(combined with the right kind of measurement frommicroeconomic data) make clear why the aboveanalysis is incomplete. During downturns, the loss ofincome is not spread evenly across all households,

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To a large extent,the progress inmacro models in thepast 25 years hasbeen about beingable to solve models

that incorporatemore realisticversions of theexchange process.This evolution hastaken place inmany ways.

Successes

because some people lose their jobs and others don’t.Because of financial market frictions, the insuranceagainst these outcomes is far from perfect (despitethe presence of government-provided unemploy-ment insurance). As a result, the fall in GDP fromJune 2008 to June 2009 does not represent a 4 per-cent loss of income for everyone. Instead, the aggre-gate downturn confronts many people with a dis-turbing game of chance that offers them some prob-ability of losing an enormous amount of income (asmuch as 50 percent or more). It is this extra risk thatmakes aggregate downturns so troubling to people,not the average loss.

This way of thinking about recessions changesone’s views about the appropriate policy responses.Good social insurance (like extended unemploy-ment benefits) becomes essential. Using GDPgrowth rates as a way to measure recession or recov-ery seems strained. Instead, unemployment ratesbecome a useful (albeit imperfect) way to measurethe concentration of aggregate shocks.

THE PROBLEMSI have highlighted the successes of macro modelingover the past 25 years. However, there are some dis-tinct areas of concern. I will highlight three.

Piecemeal ApproachI have discussed how macroeconomists have addedfinancial frictions and pricing frictions into theirmodels. They have added a host of other frictions(perhaps most notably labor market frictions thatrequire people to spend time to find jobs). However,

modelers have generally added frictions one at atime. Thus, macro models with pricing frictions donot have financial frictions, and neither kind ofmacro model has labor market frictions.

This piecemeal approach is again largely attribut-able to computational limitations. As I have dis-cussed above, it is hard to compute macro modelswith financial frictions. It does not become easier tocompute models with both labor market frictionsand financial frictions. But the recent crisis has notbeen purely financial in nature: Remarkable eventshave taken place in both labor markets and assetmarkets. It seems imperative to study the jointimpact of multiple frictions.

Finance and BankingAs I have discussed, many modern macro modelsincorporate financial market frictions. However,these models generally allow households and firmsto trade one or two financial assets in a single mar-ket. They do not capture an intermediate messyreality in which market participants can trade mul-tiple assets in a wide array of somewhat segmentedmarkets. As a consequence, the models do notreveal much about the benefits of the massiveamount of daily or quarterly reallocations of wealthwithin financial markets. The models also say noth-ing about the relevant costs and benefits of resultingfluctuations in financial structure (across bankloans, corporate debt, and equity).

Macroeconomists abstracted from these featuresof financial markets for two reasons. First, prior toDecember 2007, such details seemed largely irrele-

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It is hard to compute macro models with financial frictions.It does not become easier to compute models with both labormarket frictions and financial frictions.

The models do not capture an intermediate messy reality in whichmarket participants can trade multiple assets in a wide array ofsomewhat segmented markets. As a consequence, the models donot reveal much about the benefits of the massive amount of dailyor quarterly reallocations of wealth within financial markets.

The difficulty in macroeconomics is that virtually every variableis endogenous, but the macroeconomy has to be hit by somekind of exogenously specified shocks if the endogenous variablesare to move.

Concerns

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vant to understanding post-World War II businesscycle fluctuations in the United States (althoughmaybe not in other countries, such as Japan). Thisargument is certainly less compelling today.

Second, embedding such features in modernmacro models is difficult. There are many economictheories of high-frequency asset trading and corporatestructure. Generally, these theories rely on some mar-ket participants having private information about keyeconomic attributes, such as future asset payoffs orfirm prospects. This kind of private information ishard to incorporate into the kind of dynamic econom-ic models used by macroeconomists. Nonetheless, Iam sure that there will be a lot of work taking up thischallenge in the months and years to come.

SHO CKSWhy does an economy have business cycles? Whydo asset prices move around so much? At this stage,macroeconomics has little to offer by way of answersto these questions. The difficulty in macroeconom-ics is that virtually every variable is endogenous, butthe macroeconomy has to be hit by some kind ofexogenously specified shocks if the endogenousvariables are to move.2

The sources of disturbances in macroeconomicmodels are (to my taste) patently unrealistic.Perhaps most famously, most models in macroeco-

nomics rely on some form of large quarterly move-ments in the technological frontier (usuallyadvances, but sometimes not). Some models havecollective shocks to workers’ willingness to work.Other models have large quarterly shocks to thedepreciation rate in the capital stock (in order togenerate high asset price volatilities). To my mind,these collective shocks to preferences and technolo-gy are problematic. Why should everyone want towork less in the fourth quarter of 2009? What exact-ly caused a widespread decline in technological effi-ciency in the 1930s? Macroeconomists use thesenotions of shocks only as convenient shortcuts togenerate the requisite levels of volatility in endoge-nous variables.

Of course, macroeconomists will always needaggregate shocks of some kind in macro models.However, I believe that they are handicapping them-selves by only looking at shocks to fundamentals likepreferences and technology. Phenomena like creditmarket crunches or asset market bubbles rely onself-fulfilling beliefs about what others will do. Forexample, during an asset market bubble, a giventrader is willing to pay more for an asset onlybecause the trader believes that others will pay more.Macroeconomists need to do more to explore mod-els that allow for the possibility of aggregate shocksto these kinds of self-fulfilling beliefs.

Any economic model or theory describes how some variables (called endogenous) respond to other variables (calledexogenous). Whether a variable is exogenous or endogenous depends on the model and the context. For example, if amodel is trying to explain the behavior of auto purchases on the part of an individual consumer, it is reasonable to treatcar prices as exogenous, because the consumer cannot affect car prices. However, if the model is trying to explain thebehavior of total auto purchases, it cannot treat car prices as endogenous. In macroeconomics, all variables seem like theyshould be endogenous (except maybe the weather!).

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MODERN MACROECONOMICSAND ECONOMIC POLICYThe modernization of macroeconomics took placerapidly in academia. By the mid-1990s, virtuallyanyone getting a Ph.D. in macroeconomics in theUnited States was using modern macro models. Thesituation was quite different in economic policy-making. Until late in the last millennium, both mon-etary and fiscal policymakers used the old-stylemacro models of the 1960s and 1970s for both fore-casting and policy evaluation.

There were a number of reasons for this slow dif-fusion of methods and models. My own belief is thatthe most important issue was that of statistical fit.The models of the 1960s and 1970s were based onestimated supply and demand relationships, and sowere specifically designed to fit the existing datawell. In contrast, modern macro models of seven oreight endogenous variables typically had only one ortwo shocks. By any statistical measure, such a modelwould imply an excessive amount of correlationamong the endogenous variables. In this sense, itmight seem that the modern models were specifical-ly designed to fit the data badly. The lack of fit gavepolicymakers cause for concern.

In the early 2000s, though, this problem of fit dis-appeared for modern macro models with stickyprices. Using novel Bayesian estimation methods,Frank Smets and Raf Wouters (2003) demonstratedthat a sufficiently rich New Keynesian model couldfit European data well. Their finding, along with

similar work by other economists, has led to wide-spread adoption of New Keynesian models for poli-cy analysis and forecasting by central banks aroundthe world.

Personally, I believe that statistical fit is overem-phasized as a criterion for macro models. As a poli-cymaker, I want to use models to help evaluate theeffects of out-of-sample changes in policies. A modelthat is designed to fit every wiggle of the existing datawell is almost guaranteed to do worse at this taskthan a model that does not.3 Despite this misgiving, Iam delighted to see the diffusion of New Keynesianmodels into monetary policymaking. Regardless ofhow they fit or don’t fit the data, they incorporatemany of the trade-offs and tensions relevant forcentral banks.

In the preceding section, I have emphasized thedevelopment of macro models with financial mar-ket frictions, such as borrowing constraints or lim-ited insurance. As far as I am aware, these modelsare not widely used for macro policy analysis. Thispractice should change. From August 2007 throughlate 2008, credit markets tightened (in the sense thatspreads spiked and trading volume fell). Thesechanges led—at least in a statistical sense—to sharpdeclines in output. It seems clear to me that under-standing these changes in spreads and their connec-tion to output declines can only be done via modelswith financial market frictions. Such models wouldprovide their users with explicit guidance aboutappropriate interventions into financial markets.4

3 See, for example, Narayana Kocherlakota (2007) and V. V. Chari, Patrick Kehoe, and Ellen McGrattan (2009).

Understandingchanges in spreadsand their connectionto output declinescan only be done viamodels with financialmarket frictions.

Such models wouldprovide their userswith explicit guidanceabout appropriateinterventions intofinancial markets.

Economic Policy

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A CONCLUSION AB OUTCOMMUNICATIONMacroeconomics has made a lot of progress, and Ibelieve a great deal more is yet to come. But thatprogress serves little purpose if nobody knows aboutit. Communication between academic macroecono-mists and policymakers needs to improve. There aretwo related problems. First, by and large, journalistsand policymakers—and by extension the U.S. pub-lic—think about macroeconomics using the basical-ly abandoned frameworks of the 1960s and 1970s.Macroeconomists have failed to communicate theirnew discoveries and understanding to policymakersor to the world. Indeed, I often think that macro-economists have failed to even communicate suc-cessfully with fellow economists.

Second, macroeconomists have to be moreresponsive to the needs of policymakers. During2007–09, macroeconomists undertook relatively lit-tle model-based analysis of policy. Any discussions ofpolicy tended to be based on purely verbal intuitionsor crude correlations as opposed to tight modeling.

My goal as president of the Federal Reserve Bankof Minneapolis is to help on both of these dimen-

sions. The seventh floor of the Federal Reserve Bankof Minneapolis is one of the most exciting macroresearch environments in the country. As president,I plan to learn from our staff, consultants, and visi-tors. I view a huge part of my job as translating mylessons both into plain language and into concretepolicy decisions.

At the same time, I want to communicate in theother direction. Currently, the Federal ReserveSystem and other parts of the U.S. government arefacing critical policy decisions. I view a key part ofmy job to be setting these policy problems beforeour research staff and the academic macro commu-nity as a whole. Of course, I do not know whatanswers they will generate, but I am sure that theywill be informative and useful.

In other words, it is my conviction that theFederal Reserve Bank of Minneapolis can serve as acrucial nexus between scientific advances withinthe academe and the needed changes in macroeco-nomic policymaking. Indeed, this bank has a longhistory of doing just that. It was here that JohnBryant and Neil Wallace (1978) illustrated the tick-ing time bomb embedded in deposit insurance. It

4 In terms of fiscal policy (especially short-term fiscal policy), modern macro modeling seems to have had little impact. The dis-cussion about the fiscal stimulus in January 2009 is highly revealing along these lines. An argument certainly could be made forthe stimulus plan using the logic of New Keynesian or heterogeneous agent models. However, most, if not all, of the motivationfor the fiscal stimulus was based largely on the long-discarded models of the 1960s and 1970s. Within a New Keynesian model,policy affects output through the real interest rate. Typically, given that prices are sticky, the monetary authority can lower thereal interest rate and stimulate output by lowering a target nominal interest rate. However, this approach no longer works if thetarget nominal interest rate is zero. At this point, as Gauti Eggertsson (2009) argues, fiscal policy can be used to stimulate out-put instead. Increasing current government spending leads households to expect an increase in inflation (to help pay off theresulting debt). Given a fixed nominal interest rate of zero, the rise in expected inflation generates a stimulating fall in the realinterest rate. Eggertsson’s argument is correct theoretically and may well be empirically relevant. However, the usual justifica-tion for the January 2009 fiscal stimulus said little about its impact on expected inflation.

I plan to learn from ourstaff, consultants, and visitors.I view a huge part of my jobas translating my lessons bothinto plain language and intoconcrete policy decisions.

I view a key part of my jobto be setting these policyproblems before our researchstaff and the academic macrocommunity as a whole.

Communication

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was here that Gary Stern and Ron Feldman (2004)warned of that same ticking time bomb in the gov-ernment’s implicit guarantees to large financialinstitutions. And it was here that Thomas Sargentand Neil Wallace (1985) underscored the joint roleof fiscal and monetary discipline in restraininginflation.

We (at the Minneapolis Fed) have already takena concrete step in creating this communicationchannel. We have begun a series of ad hoc policypapers on issues relating to current policy ques-tions, accessible on the bank’s Web site at min-neapolisfed.org. These papers, as well as other work

featured in this magazine and on our Web site, willdescribe not only our efforts to better understandconditions surrounding such events as the recentfinancial crisis, but also our prescriptions for avoid-ing and/or addressing them in the future. My pred-ecessor, Gary Stern, spent nearly a quarter centuryas president. Outside the bank, a sculpture com-memorates his term. The sculpture rightly laudsGary’s “commitment to ideas and to the disciplineof careful reasoning.” I view my mission to serve asa liaison between the worlds of modern macroeco-nomics and policymaking as a natural way to carryon Gary’s work.

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