essay on macroeconomic modeling

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Modern macroeconomic models as tools for economic policy.

May 4, 2010

Article Highlights

Macro models evolve over time

They endure successes and shortcomings

Communication between macroeconomists and policymakers must improve

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

I believe that during the last financial crisis, macroeconomists (and I include myself among them) failed the country, and indeed the world. In September 2008, central bankers were in 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 much earlier, because they did not provide policymakers with rules to avoid the circumstances that led to the global financial meltdown.

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

The answer is that macroeconomics has made important advances in recent years. Those advances—coupled with a rededicated effort following this recent economic episode— position macroeconomics to make useful contributions to policymaking in the future. In this essay, I want to tell the story of how macroeconomics got to this point, of what the key questions are that still vex the science, and of why I am hopeful that macroeconomics is poised to benefit policymakers going forward.

According to the media, the defining struggle of macroeconomics is between people: those who like government and those who don’t. In my essay, the defining struggle in macroeconomics is between people and technology. Macroeconomists try to determine the answers to questions about entire economies. These questions really concern the outcomes of large-scale experiments, but there is no sensible way to perform such experiments in national or global laboratories. Instead, macroeconomists must conduct their experiments inside economic models that are highly stylized and simplified versions of reality. I will show that macroeconomists always leave many possibly important features of the world out of their models. It may seem to outside observers that macroeconomists make these omissions out of choice. Far more often, though, macroeconomists abstract from aspects of reality because they must. At any given point in time, there are significant conceptual and computational limitations that restrict what macroeconomists can do. The evolution of the field is about the eroding of these barriers.

This essay describes the current state of macroeconomic modeling and its relationship to the world of policymaking. Modern macro models can be traced back to a revolution that began in the 1980s in response to a powerful critique authored by Robert Lucas (1976). The revolution has led to the use of models that share five key features:

• They specify budget constraints for households, technologies for firms, and resource constraints for the overall economy.
• They specify household preferences and firm objectives.
• They assume forward-looking behavior for firms and households.
• They include the shocks that firms and households face.
• They are models of the entire macroeconomy.

The original modern macro models developed in the 1980s implied that there was little role for government stabilization. However, since then, there have been enormous innovations in the availability of household-level and firm-level data, in computing technology, and in theoretical reasoning. These advances mean that current models can have features that had to be excluded in the 1980s. It is common now, for example, to use models in which firms can only adjust their prices and wages infrequently. In other widely used models, firms or households are unable to fully insure against shocks, such as loss of market share or employment, and face restrictions on their abilities to borrow. Unlike the models of the 1980s, these newer models do imply that government stabilization policy can be useful. However, as I will show, the desired policies are very different from those implied by the models of the 1960s or 1970s.

As noted above, despite advances in macroeconomics, there is much left to accomplish. I highlight three particular weaknesses of current macro models. First, few, if any, models treat financial, pricing, and labor market frictions jointly. Second, even in macro models that contain financial market frictions, the treatment of banks and other financial institutions is quite crude. Finally, and most troubling, macro models are driven by patently unrealistic shocks. These deficiencies were largely—and probably rightly—ignored during the “Great Moderation” period of 1982–2007, when there were only two small recessions in the United States. The weaknesses need to be addressed in the wake of more recent events.

Finally, I turn to the policy world. The evolution of macroeconomic models had relatively little effect on policymaking until the middle part of this decade. 1 At that point, many central banks began to use modern macroeconomic models with price rigidities for forecasting and policy evaluation. This step is a highly desirable one. However, as far as I am aware, no central bank is using a model in which heterogeneity among agents or firms plays a prominent role. I discuss why this omission strikes me as important.

Modern Macro Models

I begin by laying out the basic ingredients of modern macro models. I discuss the freshwater-saltwater divide of the 1980s. I argue that this division has been eradicated, in large part by better computers.

The Five Ingredients

The macro models used in the 1960s and 1970s were based on large numbers of interlocking demand and supply relationships estimated using various kinds of data. In his powerful critique, Lucas demonstrated that the demand and supply relationships estimated using data generated from one macroeconomic policy regime would necessarily change when the policy regime changed. Hence, such estimated relationships, while useful for forecasting when the macro policy regime was kept fixed, could not be of use in evaluating the impact of policy regime changes.

How can macroeconomists get around the Lucas critique? The key is to build models that are specifically based on the aspects of the economy that they all agree are beyond the control of the government. Thus, the Lucas critique says that if the Federal Reserve alters its interest rate rule, the estimated relationship between investment and interest rates must change. However, this relationship is ultimately grounded in more fundamental features of the economy, such as the technology of capital accumulation and people’s preferences for consumption today versus in the future. If the Federal Reserve changes its rule, people’s preferences and firms’ technologies don’t change. Models that are grounded in these more fundamental (sometimes called structural ) features of the economy can do a better job of figuring out the impact of a change in Federal Reserve policy.

Beginning in the 1980s, this argument (and other forces) led to the growing use of what I will term “modern macro” models. As I outlined earlier, modern macro models have five key features. First, they must include resource constraints and budget constraints. Resource constraints show how the members of society can use costly inputs like labor and capital to create goods. Budget constraints dictate that no entity can increase its spending without increasing its revenue (either now or in the future). These constraints prevent anyone in the economy (including the government) from creating something from nothing.

Second, the models must include an explicit description of individual preferences and firm objectives. Without such a description, as discussed above, the models are subject to the Lucas critique.

Third, the models generally feature forward-looking behavior. Macroeconomists all agree that households’ and firms’ actions today depend on their expectations of the future. Thus, households that expect better times in the future will try to borrow. Their demand for loans will drive up interest rates. An analyst who ignored these expectations would not be able to understand the behavior of interest rates.

In most macro models, households and firms have what are called rational expectations. This term means that they form forecasts about the future as if they were statisticians. It does not mean that households and firms in the model are always—or ever—right about the future. However, it does mean that households and firms cannot make better forecasts given their available information.

Using rational expectations has been attractive to macroeconomists (and others) because it provides a simple and unified way to approach the modeling of forward-looking behavior in a wide range of settings. However, it is also clearly unrealistic. Long-standing research agendas by prominent members of the profession (Christopher Sims and Thomas Sargent, among others) explore the consequences of relaxing the assumption. Doing so has proven challenging both conceptually and computationally.

Forward-looking households and firms want to take account of the risks that might affect them. For this reason, the fourth key ingredient of modern macro models is that they are explicit about the shocks that affect the economy. For example, most macro models assume that the rate of technological progress is random. Expectations about this variable matter: Households will work harder and firms invest more if they expect rapid technological progress.

Finally, just like old macro models, modern macro models are designed to be mathematical formalizations of the entire economy. This ambitious approach is frustrating for many outside the field. Many economists like verbal intuitions as a way to convey understanding. Verbal intuition can be helpful in understanding bits and pieces of macro models. However, it is almost always misleading about how they fit together. It is exactly the imprecision and incompleteness of verbal intuition that forces macroeconomists to include the entire economy in their models.

When these five ingredients are put together, the result is what are often termed dynamic stochastic general equilibrium (DSGE) macro models. Dynamic refers to the forward-looking behavior of households and firms. Stochastic refers to the inclusion of shocks. General refers to the inclusion of the entire economy. Finally, equilibrium refers to the inclusion of explicit constraints and objectives for the households and firms.

Historical Digression: Freshwater versus Saltwater

The switch to modern macro models led to a fierce controversy within the field in the 1980s. Users of the new models (called “freshwater” economists because their universities were located on lakes and rivers) brought a new methodology. But they also had a surprising substantive finding to offer. They argued that a large fraction of aggregate fluctuations could be understood as an efficient response to shocks that affected the entire economy. As such, most, if not all, government stabilization policy was inefficient.

The intuition of the result seemed especially clear in the wake of the oil crisis of the 1970s. Suppose a country has no oil, but it needs oil to produce goods. If the price of oil goes up, then it is economically efficient for people in the economy to work less and produce less output. Faced with this shock, the government of the oil-importing country could generate more output in a number of ways. It could buy oil from overseas and resell it at a lower domestic price. Alternatively, it could hire the freed-up workers at high wages to produce public goods. However, both of these options require the government to raise taxes. In the models of the freshwater camp, the benefits of the stimulus are outweighed by the costs of the taxes. The recession generated by the increase in the oil price is efficient.

Scholars in the opposing (“saltwater”) camp argued that in a large economy like the United States, it is implausible for the fluctuations in the efficient level of aggregate output to be as large as the fluctuations in the observed level of output. They pointed especially to downturns like the Great Depression as being obvious counterexamples.

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

My own idiosyncratic view is that the division was a consequence of the limited computing technologies and techniques that were available in the 1980s. To solve a generic macro model, a vast array of time- and state-dependent quantities and prices must be computed. These quantities and prices interact in potentially complex ways, and so the problem can be quite daunting.

However, this complicated interaction simplifies greatly if the model is such that its implied quantities maximize a measure of social welfare. Given the primitive state of computational tools, most researchers could only solve models of this kind. But—almost coincidentally—in these models, all government interventions (including all forms of stabilization policy) are undesirable.

With the advent of better computers, better theory, and better programming, it is possible to solve a much wider class of modern macro models. As a result, the freshwater-saltwater divide has disappeared. Both camps have won (and I guess lost). On the one hand, the freshwater camp won in terms of its modeling methodology. Substantively, too, there is a general recognition that some nontrivial fraction of aggregate fluctuations is actually efficient in nature.

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

STATE OF MODERN MACRO

In this section, I discuss some of the successes of modern macro. I point to some deficiencies in the current state of knowledge and discuss what I perceive as useful steps forward.

In the macro models of the 1980s, all mutually beneficial trades occur without delay. This assumption of frictionless exchange made solving these models easy. However, it also made the models less compelling. To a large extent, the progress in macro in the past 25 years has been about being able to solve models that incorporate more realistic versions of the exchange process. This evolution has taken place in many ways, but I will focus on two that I see as particularly important.

To a large extent, the progress in macro models in the past 25 years has been about being able to solve models that incorporate more realistic versions of the exchange process. This evolution has taken place in many ways.

Pricing Frictions: The New Keynesian Synthesis

If the Federal Reserve injects a lot of money into the economy, then there is more money chasing fewer goods. This extra money puts upward pressure on prices. If all firms changed prices continuously, then this upward pressure would manifest itself in an immediate jump in the price level. But this immediate jump would have little effect on the economy. Essentially, such a change would be like a simple change of units (akin to recalculating distances in inches instead of feet).

In the real world, though, firms change prices only infrequently. It is impossible for the increase in money to generate an immediate jump in the price level. Instead, since most prices remain fixed, the extra money generates more demand on the part of households and in that way generates more production. Eventually, prices adjust, and these effects on demand and production vanish. But infrequent price adjustment means that monetary policy can have short-run effects on real output.

Because of these considerations, many modern macro models are centered on infrequent price and wage adjustments. These models are often called sticky price or New Keynesian models. They provide a foundation for a coherent normative and positive analysis 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 1970s, monetary policymakers in New Keynesian models are trying to minimize output gaps without generating too much volatility in inflation. However, in the models of the 1960s and 1970s, output gap refers to the deviation between observed output and some measure of potential output that is growing at a roughly constant rate. In contrast, in modern sticky price models, output gap refers to the deviations between observed output and efficient output. The modern models specifically allow for the possibility that efficient output may move down in response to adverse shocks. This difference in formulation can lead to strikingly different policy implications.

FINANCIAL MARKET FRICTIONS

The modern macro models of the 1980s and the New Keynesian models either implicitly or explicitly assume that firms and households can fully capitalize all future incomes through loan or bond markets. The models also assume that firms and households can buy insurance against all possible forms of risk. This assumption of a frictionless financial market is clearly unrealistic.

Over the past 25 years, a great deal of work has used models that incorporate financial market frictions. Most of these models cannot be solved reliably using graphical techniques or pencil and paper. As a consequence, progress is closely tied to advances in computational speed.

Why are these models so hard to solve? The key difficulty is that, within these models, the distribution of financial wealth evolves over time. Suppose, for example, that a worker loses his or her job. If the worker were fully insured against this outcome, the worker’s wealth would not be affected by this loss. However, in a model with only partial insurance, the worker will run down his or her savings to get through this unemployment spell. The worker’s financial wealth will be lower as a result of being unemployed.

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

From a policy perspective, these models lead to a new and better understanding of the costs of economic downturns. For example, consider the latest recession. During the four quarters from June 2008 through June 2009, per capita gross domestic product in the United States fell by roughly 4 percent. In a model with no asset market frictions, all people share this proportionate loss evenly and all lose two weeks’ 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 in the United States ends up being paid in June 2009 the same (inflation-adjusted) amount that they made in June 2006.

However, the models with asset market frictions (combined with the right kind of measurement from microeconomic data) make clear why the above analysis is incomplete. During downturns, the loss of income is not spread evenly across all households, because some people lose their jobs and others don’t. Because of financial market frictions, the insurance against these outcomes is far from perfect (despite the presence of government-provided unemployment insurance). As a result, the fall in GDP from June 2008 to June 2009 does not represent a 4 percent loss of income for everyone. Instead, the aggregate downturn confronts many people with a disturbing game of chance that offers them some probability of losing an enormous amount of income (as much as 50 percent or more). It is this extra risk that makes aggregate downturns so troubling to people, not the average loss.

This way of thinking about recessions changes one’s views about the appropriate policy responses. Good social insurance (like extended unemployment benefits) becomes essential. Using GDP growth rates as a way to measure recession or recovery seems strained. Instead, unemployment rates become a useful (albeit imperfect) way to measure the concentration of aggregate shocks.

THE PROBLEMS

I have highlighted the successes of macro modeling over the past 25 years. However, there are some distinct areas of concern. I will highlight three.

Piecemeal Approach

I have discussed how macroeconomists have added financial frictions and pricing frictions into their models. They have added a host of other frictions (perhaps most notably labor market frictions that require people to spend time to find jobs). However, modelers have generally added frictions one at a time. Thus, macro models with pricing frictions do not have financial frictions, and neither kind of macro model has labor market frictions.

This piecemeal approach is again largely attributable to computational limitations. As I have discussed above, it is hard to compute macro models with financial frictions. It does not become easier to compute models with both labor market frictions and financial frictions. But the recent crisis has not been purely financial in nature: Remarkable events have taken place in both labor markets and asset markets. It seems imperative to study the joint impact of multiple frictions.

Finance and Banking

As I have discussed, many modern macro models incorporate financial market frictions. However, these models generally allow households and firms to trade one or two financial assets in a single market. They do not capture an intermediate messy reality in which market participants can trade multiple assets in a wide array of somewhat segmented markets. As a consequence, the models do not reveal much about the benefits of the massive amount of daily or quarterly reallocations of wealth within financial markets. The models also say nothing about the relevant costs and benefits of resulting fluctuations in financial structure (across bank loans, corporate debt, and equity).

Macroeconomists abstracted from these features of financial markets for two reasons. First, prior to December 2007, such details seemed largely irrelevant to understanding post-World War II business cycle fluctuations in the United States (although maybe not in other countries, such as Japan). This argument is certainly less compelling today.

Second, embedding such features in modern macro models is difficult. There are many economic theories of high-frequency asset trading and corporate structure. Generally, these theories rely on some market participants having private information about key economic attributes, such as future asset payoffs or firm prospects. This kind of private information is hard to incorporate into the kind of dynamic economic models used by macroeconomists. Nonetheless, I am sure that there will be a lot of work taking up this challenge in the months and years to come.

It is hard to compute macro models with financial frictions. It does not become easier to compute models with both labor market frictions and financial frictions. The models do not capture an intermediate messy reality in which market participants can trade multiple assets in a wide array of somewhat segmented markets. As a consequence, the models do not reveal much about the benefits of the massive amount of daily or quarterly reallocations of wealth within financial markets. The difficulty in macroeconomics is that virtually every variable is endogenous, but the macroeconomy has to be hit by some kind of exogenously specified shocks if the endogenous variables are to move.

Why does an economy have business cycles? Why do asset prices move around so much? At this stage, macroeconomics has little to offer by way of answers to these questions. The difficulty in macroeconomics is that virtually every variable is endogenous, but the macroeconomy has to be hit by some kind of exogenously specified shocks if the endogenous variables are to move. 2

The sources of disturbances in macroeconomic models are (to my taste) patently unrealistic. Perhaps most famously, most models in macroeconomics rely on some form of large quarterly movements in the technological frontier (usually advances, but sometimes not). Some models have collective shocks to workers’ willingness to work. Other models have large quarterly shocks to the depreciation rate in the capital stock (in order to generate high asset price volatilities). To my mind, these collective shocks to preferences and technology are problematic. Why should everyone want to work less in the fourth quarter of 2009? What exactly caused a widespread decline in technological efficiency in the 1930s? Macroeconomists use these notions of shocks only as convenient shortcuts to generate the requisite levels of volatility in endogenous variables.

Of course, macroeconomists will always need aggregate shocks of some kind in macro models. However, I believe that they are handicapping themselves by only looking at shocks to fundamentals like preferences and technology. Phenomena like credit market crunches or asset market bubbles rely on self-fulfilling beliefs about what others will do. For example, during an asset market bubble, a given trader is willing to pay more for an asset only because the trader believes that others will pay more. Macroeconomists need to do more to explore models that allow for the possibility of aggregate shocks to these kinds of self-fulfilling beliefs.

MODERN MACROECONOMICS AND ECONOMIC POLICY

The modernization of macroeconomics took place rapidly in academia. By the mid-1990s, virtually anyone getting a Ph.D. in macroeconomics in the United States was using modern macro models. The situation was quite different in economic policymaking. Until late in the last millennium, both monetary and fiscal policymakers used the old-style macro models of the 1960s and 1970s for both forecasting and policy evaluation.

There were a number of reasons for this slow diffusion of methods and models. My own belief is that the most important issue was that of statistical fit. The models of the 1960s and 1970s were based on estimated supply and demand relationships, and so were specifically designed to fit the existing data well. In contrast, modern macro models of seven or eight endogenous variables typically had only one or two shocks. By any statistical measure, such a model would imply an excessive amount of correlation among the endogenous variables. In this sense, it might seem that the modern models were specifically designed to fit the data badly. The lack of fit gave policymakers cause for concern.

In the early 2000s, though, this problem of fit disappeared for modern macro models with sticky prices. Using novel Bayesian estimation methods, Frank Smets and Raf Wouters (2003) demonstrated that a sufficiently rich New Keynesian model could fit European data well. Their finding, along with similar work by other economists, has led to widespread adoption of New Keynesian models for policy analysis and forecasting by central banks around the world.

Personally, I believe that statistical fit is overemphasized as a criterion for macro models. As a policymaker, I want to use models to help evaluate the effects of out-of-sample changes in policies. A model that is designed to fit every wiggle of the existing data well is almost guaranteed to do worse at this task than a model that does not. 3 Despite this misgiving, I am delighted to see the diffusion of New Keynesian models into monetary policymaking. Regardless of how they fit or don’t fit the data, they incorporate many of the trade-offs and tensions relevant for central banks.

In the preceding section, I have emphasized the development of macro models with financial market frictions, such as borrowing constraints or limited insurance. As far as I am aware, these models are not widely used for macro policy analysis. This practice should change. From August 2007 through late 2008, credit markets tightened (in the sense that spreads spiked and trading volume fell). These changes led—at least in a statistical sense—to sharp declines in output. It seems clear to me that understanding these changes in spreads and their connection to output declines can only be done via models with financial market frictions. Such models would provide their users with explicit guidance about appropriate interventions into financial markets. 4

Understanding changes in spreads and their connection to output declines can only be done via models with financial market frictions. Such models would provide their users with explicit guidance about appropriate interventions into financial markets.

A CONCLUSION ABOUT COMMUNICATION

Macroeconomics has made a lot of progress, and I believe a great deal more is yet to come. But that progress serves little purpose if nobody knows about it. Communication between academic macroeconomists and policymakers needs to improve. There are two related problems. First, by and large, journalists and policymakers—and by extension the U.S. public—think about macroeconomics using the basically abandoned frameworks of the 1960s and 1970s. Macroeconomists have failed to communicate their new discoveries and understanding to policymakers or to the world. Indeed, I often think that macroeconomists have failed to even communicate successfully with fellow economists.

Second, macroeconomists have to be more responsive to the needs of policymakers. During 2007–09, macroeconomists undertook relatively little model-based analysis of policy. Any discussions of policy tended to be based on purely verbal intuitions or crude correlations as opposed to tight modeling.

My goal as president of the Federal Reserve Bank of Minneapolis is to help on both of these dimensions. The seventh floor of the Federal Reserve Bank of Minneapolis is one of the most exciting macro research environments in the country. As president, I plan to learn from our staff, consultants, and visitors. I view a huge part of my job as translating my lessons both into plain language and into concrete policy decisions.

At the same time, I want to communicate in the other direction. Currently, the Federal Reserve System and other parts of the U.S. government are facing critical policy decisions. I view a key part of my job to be setting these policy problems before our research staff and the academic macro community as a whole. Of course, I do not know what answers they will generate, but I am sure that they will be informative and useful.

I plan to learn from our staff, consultants, and visitors. I view a huge part of my job as translating my lessons both into plain language and into concrete policy decisions. I view a key part of my job to be setting these policy problems before our research staff and the academic macro community as a whole.

In other words, it is my conviction that the Federal Reserve Bank of Minneapolis can serve as a crucial nexus between scientific advances within the academe and the needed changes in macroeconomic policymaking. Indeed, this bank has a long history of doing just that. It was here that John Bryant and Neil Wallace (1978) illustrated the ticking time bomb embedded in deposit insurance. It was here that Gary Stern and Ron Feldman (2004) warned of that same ticking time bomb in the government’s implicit guarantees to large financial institutions. And it was here that Thomas Sargent and Neil Wallace (1985) underscored the joint role of fiscal and monetary discipline in restraining inflation.

We (at the Minneapolis Fed) have already taken a concrete step in creating this communication channel. We have begun a series of ad hoc policy papers on issues relating to current policy questions, accessible on the bank’s Web site at minneapolisfed.org. These papers, as well as other work featured in this magazine and on our Web site, will describe not only our efforts to better understand conditions surrounding such events as the recent financial crisis, but also our prescriptions for avoiding and/or addressing them in the future. My predecessor, Gary Stern, spent nearly a quarter century as president. Outside the bank, a sculpture commemorates his term. The sculpture rightly lauds Gary’s “commitment to ideas and to the discipline of careful reasoning.” I view my mission to serve as a liaison between the worlds of modern macroeconomics and policymaking as a natural way to carry on Gary’s work.

Bryant, John, and Neil Wallace. 1978. Open-market operations in a model of regulated, insured intermediaries . Research Department Staff Report 34. Federal Reserve Bank of Minneapolis.

Chari, V. V., Patrick J. Kehoe, and Ellen R. McGrattan. 2009. New Keynesian models: Not yet useful for policy analysis. American Economic Journal: Macroeconomics 1 (January), 242–66.

Eggertsson, Gauti B. 2009. What fiscal policy is effective at zero interest rates? Staff Report 402. Federal Reserve Bank of New York.

Kocherlakota, Narayana R. 2007. Model fit and model selection . Federal Reserve Bank of St. Louis Review 89 (July/August), 349–60.

Kydland, Finn, and Edward C. Prescott. 1977. Rules rather than discretion: The inconsistency of optimal plans. Journal of Political Economy 85 (June), 473–91.

Lucas, Robert E. Jr. 1976. Economic policy evaluation: A critique. Carnegie-Rochester Conference Series on Public Policy 1, 19–46.

Sargent, Thomas J., and Neil Wallace. 1985. Some unpleasant monetarist arithmetic . Federal Reserve Bank of Minneapolis Quarterly Review 9 (Winter), 15–31.

Smets, Frank, and Raf Wouters. 2003. An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association 1 (September), 1123–75.

Stern, Gary H., and Ron J. Feldman. 2004. Too big to fail: The hazards of bank bailouts . Washington, D.C.: Brookings Institution.

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

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

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

4 In terms of fiscal policy (especially short-term fiscal policy), modern macro modeling seems to have had little impact. The discussion about the fiscal stimulus in January 2009 is highly revealing along these lines. An argument certainly could be made for the stimulus plan using the logic of New Keynesian or heterogeneous agent models. However, most, if not all, of the motivation for 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 the real interest rate and stimulate output by lowering a target nominal interest rate. However, this approach no longer works if the target nominal interest rate is zero. At this point, as Gauti Eggertsson (2009) argues, fiscal policy can be used to stimulate output instead. Increasing current government spending leads households to expect an increase in inflation (to help pay off the resulting debt). Given a fixed nominal interest rate of zero, the rise in expected inflation generates a stimulating fall in the real interest rate. Eggertsson’s argument is correct theoretically and may well be empirically relevant. However, the usual justification for the January 2009 fiscal stimulus said little about its impact on expected inflation.

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This dissertation consists of three essays in macroeconomic econometrics. The first essay investigates industry level production functions. Part of the interest in doing this is to contribute to the ongoing improvements in dynamic macroeconomic models which are increasingly disaggregating economies into industrial sectors. This paper provides useful production function parameter values for this endeavour. In addition, the paper shows that there are differences across industry level production functions, so model disaggregation cannot rely on a generic scaled down aggregate production function. Futhermore, evidence of these differences is provided in several ways. First, it is shown that some, but not all, industry level production functions exhibit constant returns to scale. Second, conducted pairwise tests show whether government capital production elasticities are the same for different pairs of industries. In the majority of these tests, the null hypothesis was rejected.

In the second essay, the relevance of wage rigidities for understanding the effect of oil price shocks on output and inflation is examined. The theoretical framework of Blanchard and Gali (2007) is adopted and modified in two important ways. First, an empirically estimated wage adjustment cost function is incorporated following work by Kim and Ruge-Murcia (2009). Second, a realistic monetary policy function is incorporated into the model to be consistent with the current macroeconomic literature. The paper provides evidence that the degree of wage stickiness has little effect on the oil price-macroeconomy relationship. We find that the only way to generate large changes in the variances of output and inflation is to increase the wage adjustment cost by an extreme amount.

The third essay assesses the statistical adequacy of the Cobb-Douglas aggregate production function with public capital as an input. The paper tests the statistical adequacy of the models proposed by Aschauer (1989a) and Tatom (1991) and finds that both models are misspecified. Furthermore, the paper finds that Tatom's model suffers from the same criticism he levels against Aschauer's model, non-stationarity in the data series used to estimate the model. Using Aschauer's framework, a properly specified model is found that models both deterministic heterogeneity and serial autocoreelation. Model results find that public capital is positive and significant. The results are in contrast to a large body of literature that discredits Aschauer's findings claiming his model is incorrect. Finally, an additional specification of the model using the student's t linear regression model is explored to capture potential heteroskedasticity.

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Macroeconomic Models, Forecasting, and Policymaking

• Andrea Pescatori
• Saeed Zaman

Models of the macroeconomy have gotten quite sophisticated, thanks to decades of development and advances in computing power. Such models have also become indispensable tools for monetary policymakers, useful both for forecasting and comparing different policy options. Their failure to predict the recent financial crisis does not negate their use, it only points to some areas that can be improved.

“All models are false but some are useful”

—George Box

Periods of economic and social crisis can easily turn into periods of change for economics as a profession. The dramatic financial crisis we experienced recently has caused economists to question the prevailing assumptions and standard approaches of the field. It is not the first time—the problems of the 1970s and 1930s had a similar effect on economic theory—and it surely will not be the last.

As we come to terms with why the crisis happened and why economists could not prevent or predict it, it is important to understand what was wrong with mainstream doctrine and practice. It is likewise just as important to identify what was working fine. As the old saying goes, let’s not throw the baby out with the bath water.

In this Commentary , we focus on one subset of economic theory and practice, the role of econometric models in the conduct of monetary policy. We review the development of different types of models commonly in use and highlight their successes and failures since the 1950s. In doing so, we also describe some of the common approaches that central banks use for forecasting and evaluating different policy scenarios.

Forecasting and Monetary Policy

Forecasting plays a vital role in the conduct of monetary policy. Policymakers need to predict the future direction of the economy before they can decide which policy to adopt. While, strictly speaking, they do not necessarily need an economic model to discuss where the economy is heading, the use of a model’s forecast has the benefit of elevating that discussion to a scientific and systematic level. Models can be used to test different theories, for example, and they require forecasters to clearly spell out their underlying hypotheses.

But policymakers need forecasting tools that do more than project the likely path of important economic indicators like inflation, output, or unemployment. They need tools that can provide them with policy guidance—tools that help them determine the economic implications of monetary-policy changes. For example, what will the economy look like under the original monetary policy, and what will it look like after the change? For this reason, there has been an effort over the past 40 to 50 years to develop empirical forecasting models that are able to provide policymakers with this kind of guidance. Three broad categories of macroeconomic models have arisen during this time, each with its own strengths and weaknesses: structural, nonstructural, and large-scale models.

Structural models are built using the fundamental principles of economic theory, often at the expense of the model’s ability to predict key macroeconomic variables like GDP, prices, or employment. In other words, economists who build structural models believe that they learn more about economic processes from exploring the intricacies of economic theory than from closely matching incoming data.

Nonstructural models are primarily statistical time-series models—that is, they represent correlations of historical data. They incorporate very little economic structure, and this fact gives them enough flexibility to capture the force of history in the forecasts they generate. They intentionally “fudge” theory in an effort to more closely match economic data. The lack of economic structure makes them less useful in terms of interpreting the forecast, but at the same time, it makes them valuable in producing unconditional forecasts. That means that they generate the expected future paths of economic variables without imposing a path on any particular variable. These unconditional forecasts are typically accurate if the overall monetary policy regime does not change. Since policy regimes change infrequently, most forecasts from nonstructural models are useful.

The third category, large-scale models, is a kind of middle ground between the structural and nonstructural models. Such models are a hybrid; they are like nonstructural models in that they are built from many equations which describe relationships derived from empirical data. They are like structural models in that they also use economic theory, namely to limit the complexity of the equations. They are large, and their size brings pros and cons. One advantage is that relationships can be selected from a huge variety of data series, making it possible to provide a thorough description of the economic condition of interest. For instance, structural models rarely feature variables such as “car sales,” while large-scale models often do. The main disadvantage is their complexity, which poses some limitations to their understanding and use.

Big Models Take Shape

The interest in developing large-scale forecasting models for policy purposes began in the 1960s at a time when Keynesian economic theory was very popular and advances in computer technology made their use feasible. Toward the end of the decade, the Federal Reserve Board developed its first version of a macro model for the U.S. economy called MPS (MIT, University of Pennsylvania, and Social Science Research Council). The Board began to use the model for forecasting and policy analysis in 1970. In the initial version, MPS contained about 60 behavioral equations (equations that describe the behavior of economic variables). At the time, economists thought they had built a structural model. Soon they would find otherwise.

The initial optimism and momentum for building practical economic models was abruptly interrupted in the 1970s, a decade of great inflation and macroeconomic turbulence. The failure of economists to forecast high inflation and unemployment and to successfully address the economic troubles of the period produced a loss of faith in mainstream Keynesian theory and in the models that were the operative arm of that theory.

Disappointment came from realizing that the models that had been developed were not as structural as previously thought. Several flaws were identified, including assumptions about the behavior of prices and the overall modeling approach.

The models’ greatest weakness was that they ignored the role that expectations play in influencing future economic events. The Fed’s and other large-scale models were often used for conditional forecasting exercises, in which variables of interest are forecasted for a chosen monetary policy stance. Comparing scenarios shows the economic implications of different monetary policy stances. But since the models did not incorporate expectations, in particular about monetary and fiscal policies, they did not produce reliable conditional forecasts.

These weaknesses were clearly a drawback when turbulence hit the economy. In fact, when people are making decisions in periods of high uncertainty, they put a lot of emphasis on anticipating what policymakers will do. They can behave differently than they did in the past, which policymakers won’t be able to predict if they’re relying on models that merely capture historical behavior patterns and don’t incorporate expectations.

The Nobel Prize winner Robert Lucas was one of the first economists to point out the pitfalls of underplaying the role of expectations, especially in relation to policy recommendations. He pointed out that the underlying parameters of the prevailing models—the numerical constants embedded in the models that drove the forecasts—were not constant at all. They would change as policy changed or as expectations about policy changed, leaving policy conclusions based on these models completely unreliable. (The argument came to be called the Lucas critique.) The policy failures of the 1970s seemed to bear him out. Lucas called for models with deeper theoretical structures, and the economics profession heard him.

Development led next in two directions, one toward improving the existing large-scale models and the other toward further developing nonstructural forecasting models. The latter effort has led to the widespread use and success of vector auto-regression models (VARs).

The Fed continued to work on its large-scale models. It developed a multicountry model (MCM) to complement the MPS, and in the 1990s it developed a new set of models—FRB/US, FRB/MCM, and FRB/World. These new models still kept most of the underlying structural framework and the equilibrium relationships of the MPS and the MCM, but they also contained explicit specifications of forward-looking expectations and a more sophisticated representation of agents’ decision making. Though they are not truly structural, they are still nevertheless the prime large-scale macro models (with over 250 behavioral equations) currently in use at the Fed. FRB/US is the most comprehensive model of the U.S. economy available anywhere.

The Dawn of DSGE Models

The rational expectations revolution of the 1970s created a temporary disconnect between academia and central banks. Economists at universities started working on developing a modeling framework that did not violate the Lucas critique. Monetary policymakers meanwhile continued to work with existing large-scale models since they were the only available framework for policy analysis. At the same time, they worked on improving those models by incorporating features advocated by Lucas and others, such as forward-looking expectations.

In a curious twist of fate, the disconnect was resolved by the rise of a new set of models, commonly known as DSGE (dynamic stochastic general equilibrium) models. The roots of DSGE models can be traced back to real business cycle theory—a theory that left very little room for monetary policy actions.

Harvard’s Gregory Mankiw explains what DSGE models are in his popular textbook. Paraphrasing, dynamic means the models “trace the path of variables over time” (since the decisions of households and businesses affect not only the current period but future periods as well); stochastic means the models incorporate techniques that account for the possibility of random economic events; and general equilibrium means that each model is built as a whole system and everything within the system depends on everything else (prices determine what people do, but what people do also determines prices).

Research on DSGE models has been going on at a significant pace since the 1980s, but only in the past few years have the models been used seriously for forecasting. While similar to large-scale models, DSGE models are different in that the latter have better microeconomic foundations: Household and firm behavior is modeled from first principles, while equations that relate macroeconomic variables (such as output, consumption, and investment) to each other are determined from the aggregation of the microeconomic equations.

The aggregation follows a strict bottom-up approach that goes from the micro to the macro level. This approach makes DSGE models better-suited to constructing conditional forecasts and comparing different policy scenarios.

DSGE models have a number of other advantages over large-scale models. They avoid the expectations problem that Lucas alerted everyone to. They incorporate a role for monetary policy, making them appealing to central banks. And finally, a technical advantage is that they can make use of the powerful solution methods of nonstructural models, given that their decision rules are usually well approximated by linear rules. The economist Francis Diebold described this aspect of DSGE models as “a marvelous union of modern macroeconomic theory and nonstructural times-series econometrics.”

Model Shortfalls and the Future

Since DSGE models are technically very difficult to solve and analyze, they are much smaller in scale—usually featuring less than a hundred variables. They cannot easily incorporate the large array of high-frequency data usually available to policymakers.

Unfortunately, leaving some variables out may often lead to serious misspecification. For this reason, Princeton economist Christopher Sims characterizes DSGE models as useful story-telling devices that cannot yet replace large-scale models for forecasting purposes. On the other hand, Ben Bernanke, chairman of the Board of Governors of the Federal Reserve System, noted that DSGE models are “increasingly useful for policy analysis” and “likely to play a more significant role in the forecasting process over time.....”

Economic forecasting models have come a long way since the 1970s, both the structural and nonstructural varieties. Most models, however, failed to predict the recent financial crisis. This failure may be partly attributed to the models’ failure to fully incorporate the growing role of the financial sector or the worldwide financial and trade linkages that globalization has generated.

However, while the economics profession is currently trying to address those deficiencies, there is something intrinsic to economics that makes forecasting difficult. Contrary to the natural sciences, the social sciences do not have true invariants that can be used as scientific foundations. There is nothing like a “constant of gravity” in economics, which we can claim is really constant. This happens because the object that is studied and the observer are in continuous interaction, and those sorts of relationships have no easily predictable consequences.

It is unlikely that models will ever provide perfectly accurate forecasts. That is because forecasts are ultimately just another variable in the system, and it is impossible to restrain them from influencing other variables in the system. Once a forecast is revealed, the forecast itself can actually change people’s behavior. In fact, the people who attend most closely to forecasts are the people whose behavior is most likely to affect the future course of the variables forecasted. In the end, while policymakers would prefer better forecasts, policymakers’ ultimate objective is better policy. And the lack of forecasting ability does not prevent models from being useful devices that can help policymakers in making decisions.

In this respect, the contribution that DSGE models have provided is mainly methodological, making them a useful complement to, but not a substitute for, large-scale macroeconomic models or nonstructural VARs. At the same time, they have given academic economists and central bank staff a base for a common language. In this respect, we believe DSGE models have had a success that cannot be judged by their inability to forecast the recent crisis.

Cited Works

• “Inflation Expectations and Inflation Forecasting,” remarks by Chairman Ben S. Bernanke at the Monetary Economics Workshop of the National Bureau of Economic Research Summer Institute, Cambridge, Massachusetts, July 10, 2007.
• “Robustness in the Strategy of Scientific Model Building,” George E.P. Box, 1979. In Robustness in Statistics: Proceedings of a Workshop. New York: Academic Press.
• “The Evolution of Macro Models at the Federal Reserve Board,” by Flint Brayton, Andrew Levin, Ralph Tryon, and John C. Williams. Carnegie-Rochester Conference Series on Public Policy .
• “The Past, Present and Future of Macroeconomic Forecasting,” by Francis X. Diebold, 1998. Journal of Economic Perspectives .
• “The Econometrics of DSGE Models,” Jesus Fernandez-Villaverde, 2009. NBER working paper no. 14677.
• “Computing Power and the Power of Econometrics,” James D. Hamilton, 2006.
• Macroeconomics , Seventh Edition, Gregory Mankiw, 2009. Worth Publishers.
• “Theory Ahead of Business Cycle Measurement,” Edward C. Prescott, 1986. Federal Reserve Bank of Minneapolis, Quarterly Review .
• “Expectations and the Neutrality of Money,” Rober E. Lucas, 1972. Journal of Economic Theory , vol. 4.
• “Comment on Del Negro, Schorfheide, Smets, and Wouters,” Christopher Sims, 2006. Journal of Business and Economic Statistic s.
• “DSGE Models and Central Banks,” Camillo Tovar, 2008. BIS, working paper no. 258.

The views authors express in Economic Commentary are theirs and not necessarily those of the Federal Reserve Bank of Cleveland or the Board of Governors of the Federal Reserve System. The series editor is Tasia Hane. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. This paper and its data are subject to revision; please visit clevelandfed.org  for updates.

Suggested Citation

Pescatori, Andrea, and Saeed Zaman. 2011. “Macroeconomic Models, Forecasting, and Policymaking.” Federal Reserve Bank of Cleveland,  Economic Commentary  2011-19. https://doi.org/10.26509/frbc-ec-201119

1.3 How Economists Use Theories and Models to Understand Economic Issues

Learning objectives.

By the end of this section, you will be able to:

• Interpret a circular flow diagram
• Explain the importance of economic theories and models
• Describe goods and services markets and labor markets

John Maynard Keynes (1883–1946), one of the greatest economists of the twentieth century, pointed out that economics is not just a subject area but also a way of thinking. Keynes ( Figure 1.6 ) famously wrote in the introduction to a fellow economist’s book: “[Economics] is a method rather than a doctrine, an apparatus of the mind, a technique of thinking, which helps its possessor to draw correct conclusions.” In other words, economics teaches you how to think, not what to think.

Watch this video about John Maynard Keynes and his influence on economics.

Economists see the world through a different lens than anthropologists, biologists, classicists, or practitioners of any other discipline. They analyze issues and problems using economic theories that are based on particular assumptions about human behavior. These assumptions tend to be different than the assumptions an anthropologist or psychologist might use. A theory is a simplified representation of how two or more variables interact with each other. The purpose of a theory is to take a complex, real-world issue and simplify it down to its essentials. If done well, this enables the analyst to understand the issue and any problems around it. A good theory is simple enough to understand, while complex enough to capture the key features of the object or situation you are studying.

Sometimes economists use the term model instead of theory. Strictly speaking, a theory is a more abstract representation, while a model is a more applied or empirical representation. We use models to test theories, but for this course we will use the terms interchangeably.

For example, an architect who is planning a major office building will often build a physical model that sits on a tabletop to show how the entire city block will look after the new building is constructed. Companies often build models of their new products, which are more rough and unfinished than the final product, but can still demonstrate how the new product will work.

A good model to start with in economics is the circular flow diagram ( Figure 1.7 ). It pictures the economy as consisting of two groups—households and firms—that interact in two markets: the goods and services market in which firms sell and households buy and the labor market in which households sell labor to business firms or other employees.

Firms produce and sell goods and services to households in the market for goods and services (or product market). Arrow “A” indicates this. Households pay for goods and services, which becomes the revenues to firms. Arrow “B” indicates this. Arrows A and B represent the two sides of the product market. Where do households obtain the income to buy goods and services? They provide the labor and other resources (e.g., land, capital, raw materials) firms need to produce goods and services in the market for inputs (or factors of production). Arrow “C” indicates this. In return, firms pay for the inputs (or resources) they use in the form of wages and other factor payments. Arrow “D” indicates this. Arrows “C” and “D” represent the two sides of the factor market.

Of course, in the real world, there are many different markets for goods and services and markets for many different types of labor. The circular flow diagram simplifies this to make the picture easier to grasp. In the diagram, firms produce goods and services, which they sell to households in return for revenues. The outer circle shows this, and represents the two sides of the product market (for example, the market for goods and services) in which households demand and firms supply. Households sell their labor as workers to firms in return for wages, salaries, and benefits. The inner circle shows this and represents the two sides of the labor market in which households supply and firms demand.

This version of the circular flow model is stripped down to the essentials, but it has enough features to explain how the product and labor markets work in the economy. We could easily add details to this basic model if we wanted to introduce more real-world elements, like financial markets, governments, and interactions with the rest of the globe (imports and exports).

Economists carry a set of theories in their heads like a carpenter carries around a toolkit. When they see an economic issue or problem, they go through the theories they know to see if they can find one that fits. Then they use the theory to derive insights about the issue or problem. Economists express theories as diagrams, graphs, or even as mathematical equations. (Do not worry. In this course, we will mostly use graphs.) Economists do not figure out the answer to the problem first and then draw the graph to illustrate. Rather, they use the graph of the theory to help them figure out the answer. Although at the introductory level, you can sometimes figure out the right answer without applying a model, if you keep studying economics, before too long you will run into issues and problems that you will need to graph to solve. We explain both micro and macroeconomics in terms of theories and models. The most well-known theories are probably those of supply and demand, but you will learn a number of others.

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Macroeconomic Models for Monetary Policy: A Critical Review from a Finance Perspective

We provide a critical review of macroeconomic models used for monetary policy at central banks from a nance perspective. We review the history of monetary policy modeling, survey the core monetary models used by major central banks, and construct an illustrative model for those readers who are unfamiliar with the literature. Within this framework, we highlight several important limitations of current models and methods, including the fact that local-linearization approximations omit important nonlinear dynamics, yielding biased impulse-response analysis and parameter estimates.  We also propose new features for the next generation of macrofinancial policy models, including: a substantial role for a financial sector, the government balance sheet and unconventional monetary policies; heterogeneity, reallocation, and redistribution effects; the macroeconomic impact of large nonlinear risk-premium dynamics; time-varying uncertainty;  financial sector and systemic risks; imperfect product market and markups; and further advances in solution, estimation, and evaluation methods for dynamic quantitative structural models.

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25.1: Major Theories in Macroeconomics

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Keynesian Theory

Keynesian theory posits that aggregate demand will not always meet the supply produced.

Learning objectives

• Explain the main tenets of Keynesian economics

Historical Background

John Maynard Keynes published a book in 1936 called The General Theory of Employment, Interest, and Money , laying the groundwork for his legacy of the Keynesian Theory of Economics. It was an interesting time for economic speculation considering the dramatic adverse effect of the Great Depression. Keynes’s concepts played a role in public economic policy under Roosevelt as well as during World War II, becoming the dominant perspective in Europe following the war.

John Maynard Keynes : John Maynard Keynes came to fame after publishing his economic theories during the Great Depression.

At the time, the primary school of economic thought was that of the classical economists (which is still a popular school of thought today). The central tenet of the classical argument says that supply can always create demand, and that surpluses will result in price reductions to the point of consumption. Put simply, people have infinite needs and the market will self-correct to the aggregate demands and available resources. This implies a hands-of public policy where markets are capable of taking care of themselves.

Keynes positioned his argument in contrast to this idea, stating that markets are imperfect and will not always self correct. Keynes theorized that natural inefficiencies in the market will see goods that are not met with demand. This wasted capital can result in market losses, unemployment, and market inefficiency (this was called ‘general glut’ in the classical model, when aggregate demand does not meet supply). Keynes insisted that markets do need moderate governmental intervention through fiscal policy (government investment in infrastructure) and monetary policy ( interest rates ).

Main Tenets

With this overview in mind, Keynesian Theory generally observes the following concepts:

• Unemployment: Under the classical model, unemployment is often attributed to high and rigid real wages. Keynes argues there is more complexity than that, specifically that societies are highly resistant to wage cuts and furthermore that reducing wages would pose a great threat to an economy. Specifically, cutting wages reduces spending and may result in a downwards spiral.
• Excessive Saving: Keynes’s concept here is somewhat complicated, but in short Keynes notes excessive saving as a threat and prospective cause of economic decline. This is because excessive saving leads to reduced investment and reduced spending, which drives down demand and the potential for consumption. This can be another spiraling issue, as money not being exchanged is actively reducing prospective employment, revenues, and future investments.
• Fiscal Policy: The key concept in fiscal policy for Keynes is ‘counter-cyclical’ fiscal policy, which is the expectation that governments can reduce the negative effects of the natural business cycle. This is, generally, achieved through deficit spending in recessions and suppression of inflation during boom times. Simply put, the government should try to curb the extremes of economic fluctuation through informed fiscal policy.
• The Multiplier Effect: This idea has in many ways already been implied in the atom, but inversely. Consider the unemployment and excessive savings problems, and how they stand to lead to spiraling decline. The other side of that coin is that positive economic situations can spiral upwards. Take for example a government investment in transportation, putting money in the pockets of various individuals who build trains and tracks. These individuals will spend that extra capital, putting money in the hands of other business (and this will continue). This is called the multiplier effect.
• IS-LM: While the IS-LM Model is a complicated byproduct of Keynesian economics, it can be summarized as the relationship between interest rates (y-axis) and the real economic output (x-axis). This is done through analyzing the invest-saving relationship (IS) in contrast to the liquidity preference and money supply relationship (LM), generating an equilibrium where certain interest rates and outputs will be generated.

While Keynesian Theory has been expounded upon significantly over the years, the important takeaway here is that aggregate demand (and thus the amount of supply consumed) is not a perfect system. Instead, demand is affected by various external forces that can create an inefficient market which will in turn affect employment, production, and inflation.

IS-LM Model : In this figure, the IS (Interest – Saving) curve is shifted outward in a way that raises both interest rates (i) and the ‘real’ economy (Y). The implication is that interest rates affect investment levels, and that these investment levels in turn affect the overall economy.

Monetarism focuses on the macroeconomic effects of the supply of money and the role of central banking on an economic system.

• Explain the main tenets of Monetarism

In the rise of monetarism as an ideology, two specific economists were critical contributors. Clark Warburton, in 1945, has been identified as the first thinker to draft an empirically sound argument in favor of monetarism. This was taken more mainstream by Milton Friedman in 1956 in a restatement of the quantity theory of money. The basic premise these two economists were putting forward is that the supply of money and the role of central banking play a critical role in macroeconomics.

The generation of this theory takes into account a combination of Keynesian monetary perspectives and Friedman’s pursuit of price stability. Keynes postulated a demand-driven model for currency; a perspective on printed money that was not beholden to the ‘ gold standard ‘ (or basing economic value off of rare metal). Instead, the amount of money in a given environment should be determined by monetary rules. Friedman originally put forward the idea of a ‘k-percent rule,’ which weighed a variety of economic indicators to determine the appropriate money supply.

Theoretically, the idea is actually quite straight-forward. When the money supply is expanded, individuals will be induced to higher spending. In turn, when the money supply retracted, individuals would limit their budgetary spending accordingly. This would theoretically provide some control over aggregate demand (which is one of the primary areas of disagreement between Keynesian and classical economists).

Monetarism began to deviate more from Keynesian economics however in the 70’s and 80’s, as active implementation and historical reflection began to generate more evidence for the monetarist view. In 1979 for example, Jimmy Carter appointed Paul Volcker as Chief of the Federal Reserve, who in turn utilized the monetarist perspective to control inflation. He eventually created a price stability, providing evidence that the theory was sound. In addition, Milton Friedman and Ann Schwartz analyzed the Great Depression in the context of monetarism as well, identifying a shortage of the money supply as a critical component of the recession.

The 1980s were an interesting transitional period for this perspective, as early in the decade (1980-1983) monetary policies controlling capital were attributed to substantial reductions in inflation (14% to 3%)(see ). However, unemployment and the rise of the use of credit are quoted as two alternatives to money supply control being the primary influence of the boom that followed 1983.

U.S. Inflation Rates : The inflation rates over time in the U.S. represent some of the evidence put forward by monetarist economists, stating that governmental control of the money supply allows for some control over inflation.

Counter Arguments

As these counter arguments in the 1980s began to arise, critics of monetarism became more mainstream. Of the current monetarism critics, the Austrian school of thought is likely the most well-known. The Austrian school of economic thought perceives monetarism as somewhat narrow-minded, not effectively taking into account the subjectivity involved in valuing capital. That is to say that monetarism seems to assume an objective value of capital in an economy, and the subsequent implications on the supply and demand.

Other criticisms revolve around international investment, trade liberalization, and central bank policy. This can be summarized as the effects of globalization, and the interdependence of markets (and consequently currencies). To manipulate money supply there will inherently be effects on other currencies as a result of relativity. This is particularly important in regards to the U.S. currency, which is considered a standard in international markets. Controlling supply and altering value may have effects on a variety of internal economic variables, but it will also have unintended consequences on external variables.

Austrian economic thought is about methodological individualism, or the idea that people will act in meaningful ways which can be analyzed.

• Explain the main tenets of Austrian economics

The Austrian school of economics originated in the 19th century in Vienna, Austria. While there were a variety of famous economists attributed to the early foundations and later expansions of the Austrian economic perspective, Carl Menger, Friedrich von Weiser, and Eugen von Bohm-Bawerk are widely recognized as critical early pioneers. The general perspective of Austrian economic thought is methodological individualism, or the recognition that people will act in meaningful ways which can be analyzed for trends.

Central Tenets

The Austrian school of thought provided enormous value to the economic climate, both as a foundation for future economics and as a deliberate counterpoint to more quantitative analysis. Of the most important ideologies, the following central tenets are:

• Opportunity Cost: This is a concept you are likely already familiar with, and one of the most important ideas in all of business and economics. Essentially, the price of a good must also incorporate the value sacrificed of the next best alternative. Basically each choice a consumer or business makes intrinsically has the cost of not being able to make an alternative choice.
• Capital and Interest: Largely in response to Karl Marx’s labor theories, Austrian economist Bohm-Bawerk identified the building blocks of interest rates and profit are supply and demand alongside time preference. In short, present consumption is more valuable than future consumption (the time value of money).
• Inflation: The idea that prices and wages must rise as a result of increased money supply is inflation (note: this is different that price inflation). Simply put, more money in the system without a higher demand for that money will drive down the relative value of each dollar.
• Business Cycles: The Austrian business cycle theory (ABCT) is the simple observation that the issuance of credit (by banks) creates economic fluctuations that tend to be cyclical (see ). In simple terms, banks will lend out money at rates lower than the risk in which that money will be used. So when businesses fail more often than they succeed, thus losing interest as opposed to accruing it, will struggle to repay their debts. When the banks call in those debts the business cannot pay, creating negative business cycles.
• The Organizing Power of Markets: The idea of this concept is that no one person knows what the appropriate price of a good should be. Instead, markets naturally generate incentives to identify optimal price points. This negates the ideas of socialism common at the time, as communist systems will be unable to identify the appropriate exchange value of each good.

As you can see from the above points, this school of economics is largely about making qualitative observations of the markets. These observations are absolutely critical in understanding the theoretical landscape, but difficult to enact in practice.

Austrian economists are often criticized for ignoring arithmetic or statistical ways to measure and analyze economics. Indeed, Austrian economists do not often place much weight on concepts such as econometrics, experimental economics, and aggregate macroeconomic analysis. In this sense, the Austrian school of thought is something of an outsider relative to other perspectives (i.e. classical, Keynesian, etc.).

Paul Krugman criticized Austrian economics as lacking explicit models of analysis, or essentially a lack of clarity in their approach. This results in inadvertent blind spots. This is a sensible criticism in many ways, as the fundamental idea behind this economic theory is that it is driven by individuals and individuals are not always rational (indeed, they are quite often irrational). As a result of this, Austrian economics often rests on the integration of social sciences (psychology, sociology, etc.) to explain preferences and consumer behavior, which is often counter-intuitive. As a result, it is very difficult to accurately measure and provide tangible proof of the efficacy of Austrian models.

Alternative Views

Neoclassical and neo-Keynesian ideas can be coupled and referred to as the neoclassical synthesis, combining alternative views in economics.

• Summarize neoclassical and Neo-Keynesian economics

The history of different economic schools of thought have consistently generated evolving theories of economics as new data and new perspectives are taken into consideration. The two most well-known schools, classical economics and Keynesian economics, have been adapting to incorporate new information and ideas from one another as well as lesser known schools of economics (Chicago, Austrian, etc.). These different perspectives have motivated economists to generate the neoclassical and neo-Keynesian perspectives. The neoclassical perspective, in conjunction with Keynesian ideas, is referred to as the neoclassical synthesis, which is largely considered the ‘mainstream’ economic perspective.

Neoclassical

In approaching Neoclassical economics, it is most important to keep in mind the following three principles:

• People have rational preferences in the context of options or outcomes that can be identified and associated with a given value (usually monetary). In short, people make smart choices regarding how they spend their money.
• Individuals maximize utility and firms maximize profit. People will try to get the most from their money while corporations will try to invest their time and assets to capture the highest margin.
• People act independently based upon comprehensive and relevant information. People are influenced by rational forces (mostly information and logic), and will make the best personal purchasing decisions based upon this.

A brief timeline of classical to neoclassical perspectives would begin with thought processes put forward by Adam Smith and David Ricardo (alongside many others). The basic idea is that aggregate demand will adjust to supply, and that value theory and distribution will reflect this rational, cost of production model. The next phase was the observation that consumer goods demonstrated a relative value based on utility, which could deviate from consumer to consumer. The final phase, and most central to the advent of the neoclassical perspective, is the introduction of marginalism. Marginalism notes that economic participants make decisions based on marginal utility or margins. For example, a company hiring a new employee will not think of the fixed value of that employee, but instead the marginal value of adding that employee (usually in regards to profitability).

Neo-Keynesian

Neo-Keynesian economics is often confused with ‘New Keynesian’ economics (which attempts to provide microeconomic foundation to Keynesian views, particularly in light of stagflation in the 1970s). Neo-Keynesian economics is actually the formalization and coordination of Keynes’s writings by a number of other economists (most notably John Hicks, Franco Modigliani, and Paul Samuelson). Much of the conceptual value is captured in the previous atoms on Keynesian views, but the substantial value of a few neo-Keynesian ideas is worth reiterating:

• IS/LM Model: This model was put forward by John Hicks in order to capture the inherent relationship between investment and savings (IS) relative to liquidity and the overall money supply (LM) (see ). The implications of this graph pertain to the static representation of monetary policy and the effects on an economic system.
• Phillips Curve: Another important model following Keynes’s publications is the Phillips Curve, put forward by William Phillips in 1958. The idea here was also largely Keynesian, revolving around the relationship between inflation and unemployment (see ).This implies a trade off between inflation rates and the creation of employment, which governments could consider in policy making. Stagflation (economic stagnation and inflation simultaneously) created issues with this however, necessitating New Keynesian ideas (as discussed briefly above).

When learning about these economic perspectives, it is important to understand the value they add to one another and the overall efficacy of all economic theory. Economists are often the product of multiple schools of thought, and don’t fit neatly into one school or another.

• John Maynard Keynes published a book in 1936 called The General Theory of Employment, Interest, and Money, laying the groundwork for his legacy of the Keynesian Theory of Economics.
• Keynes positioned his argument in contrast to this idea, stating that markets are imperfect and will not always self correct.
• Keynes believed that wage reductions in recessions and excessive savings were potential threats to an economy.
• Keynesian theory expects fiscal policy to offset business cycles (employ counter-cyclical strategies).
• Clark Warburton, in 1945, has been identified as the first thinker to draft an empirically sound argument in favor of monetarism. This was taken more mainstream by Milton Friedman in 1956.
• More money in the system results in higher spending and vice verse. This would theoretically provide some control over aggregate demand.
• Historical implementation of monetarism demonstrated some correlation with control over inflation rates and increased economic performance. This could have been a result of other factors however.
• The Austrian school of economic thought perceives monetarism as somewhat narrow-minded, not effectively taking into account the subjectivity involved in valuing capital.
• Due to the globalization of the economy, monetarism may have a negative impact on external economies. This is particularly true of the U.S., whose capital is an international standard.
• The Austrian school of economics is one of the oldest economic perspectives, originating in the 19th century in Vienna.
• Austrian economics is attributed for the identification of opportunity cost, capital and interest, inflation, business cycles and the organizing power of markets.
• Austrian economists do not often place much weight on concepts such as econometrics, experimental economics, and aggregate macroeconomic analysis. In this sense, the Austrian school of thought is something of an outsider relative to other perspectives (i.e. classical, Keynesian, etc. ).
• Paul Krugman criticized Austrian economics as lacking explicit models of analysis, or essentially a lack of clarity in their approach. This results in inadvertent blind spots.
• The history of different economic schools of thought have consistently generated evolving theories of economics as new data and new perspectives are taken into consideration.
• The neoclassical perspective in conjunction with Keynesian ideas is referred to as the neoclassical synthesis, which is largely considered the ‘mainstream’ economic perspective.
• A critical difference between classical and neoclassical perspectives is the introduction of marginalism. Marginalism notes that economic participants make decisions based on marginal utility or margins.
• Neo- Keynesian economics is the formalization and coordination of Keynes’s writings by a number of other economists (most notably John Hicks, Franco Modigliani and Paul Samuelson).
• The important to understand that these economic perspectives add value to one another and the overall efficacy of all economic theory.
• fiscal policy : Government policy that attempts to influence the direction of the economy through changes in government spending or taxes.
• monetary policy : The process of controlling the supply of money in an economy, often conducted by central banks.
• Keynesian : Of or pertaining to an economic theory based on the ideas of John Maynard Keynes, as put forward in his book The General Theory of Employment, Interest, and Money.
• Monetarism : The doctrine that economic systems are controlled by variations in the supply of money.
• gold standard : A monetary system where the value of circulating money is linked to the value of gold.
• Opportunity cost : The cost of any activity measured in terms of the value of the next best alternative forgone (that is not chosen).
• time value of money : The time value of money is the principle that a certain currency amount of money today has a different buying power (value) than the same currency amount of money in the future.
• stagflation : Inflation accompanied by stagnant growth, unemployment or recession.
• static : Unchanging; that cannot or does not change.

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Critical Essay on Modern Macroeconomic Theory

by Frank Hahn and Robert M. Solow

ISBN: 9780262581547

Pub date: August 21, 1997

• Publisher: The MIT Press

208 pp. , 6 x 9 in ,

ISBN: 9780262082419

Pub date: December 8, 1995

• 9780262581547
• Published: August 1997
• Rights: not for sale in Europe or the UK Commonwealth, except Canada
• 9780262082419
• Published: December 1995
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Macroeconomics began as the study of large-scale economic pathologies such as prolonged depression, mass unemployment, and persistent inflation. In the early 1980s, rational expectations and new classical economics dominated macroeconomic theory, with the result that such pathologies can hardly be discussed within the vocabulary of the theory. This essay evolved from the authors' profound disagreement with that trend. It demonstrates not only how the new classical view got macroeconomics wrong, but alsohow to go about doing macroeconomics the right way. Hahn and Solow argue that what was originally offered as a normative model based on perfect foresight and universal perfect competition—useful for predicting what an ideal, omniscient planner should do—has been almost casually transformed into a model for interpreting real macroeconomic behavior, leading to Panglossian economics that does not reflect actual experience. Following an explanation of microeconomic foundations, chapters introduce the basic elements for a better macro model. The model is simple, but combined with the appropriate model of the labor market it can say useful things about the fluctuation of employment, the correlation between wages and employment, and the role for corrective monetary policy.

Frank Hahn, one of Britain's most eminent economists, is Professor of Economics at Cambridge University and author of Equilibrium and Macroeconomics (MIT Press 1985).

Robert M. Solow is Institute Professor of Economics.

Like the great debate between Einstein and Bohr on quantum physics,the debate between Hahn-Solow and Lucas's rational expectationismis a must for all serious students of macro. This is how scientificprogress should be done—by sober analysis rather than cleverrhetoric or frenzied ideology. Paul A. Samuelson, Professor of Economics, M.I.T.

Professors Hahn and Solow pick up the simple general equilibrium models of new classical macroeconomics and run with them. Of course, they head off in directions that are theirs alone. Critics of these models, and enthusiasts, will want to read this book and see how far they get. Paul M. Romer, Professor of Economics, University of Californiaat Berkeley

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Palgrave Handbook of Econometrics pp 851–916 Cite as

Macroeconometric Modeling for Policy

• Gunnar Bårdsen &
• Ragnar Nymoen  

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The first part of this chapter sets out a coherent approach to dynamic macroeconometric modelbuilding; the second part demonstrates the approach through building and evaluating a small econometric model; the final part demonstrates various usages of the model for policy.

• Interest Rate
• Gross Domestic Product
• Monetary Policy
• Real Exchange Rate

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Bårdsen, G., Nymoen, R. (2009). Macroeconometric Modeling for Policy. In: Mills, T.C., Patterson, K. (eds) Palgrave Handbook of Econometrics. Palgrave Macmillan, London. https://doi.org/10.1057/9780230244405_17

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New Modeling App To Assist Farmers With Forecasting

Being able to see into the future would be a handy trick for anybody, but that ability could be indispensable in helping farmers navigate the ups and downs of Mother Nature and markets.

Scientists with  Texas A&M AgriLife Research  in the  Texas A&M College of Agriculture and Life Sciences  are developing a tool that could give agricultural producers a glimpse into the future for planning purposes.

Dr. Raghavan Srinivasan, professor in the Texas A&M Department of Ecology and Conservation Biology  and  Biological and Agricultural Engineering , received more than $750,000 in grant funding from the National Institute of Food and Agriculture and is leading a team to develop an integrated decision support system (IDSS) — a modeling tool that can forecast potential cropping conditions and economic results for producers.

The tool will utilize existing technology, data collection tools and data — including weather, market prices, farm production costs and revenues, water conservation practices and water movement through watersheds — to project scenario-based outcomes for producer operations based on possible fluctuations within those factors.

“It would be an incredibly powerful tool for farmers and agricultural operations to have in their toolbox,” Srinivasan said. “This tool won’t tell us the future, but it will give us the range of potential outcomes based on factors like continuing drought or changes in input costs or commodity market conditions. The goal is to help farmers consider economic, environmental and production challenges together in one place.”

Modeling App Development Underway

This type of tool could be especially valuable as producers in the U.S. and around the world are facing challenges related to increasingly variable weather, including droughts.

The team, which includes researchers Dr. Jean-Claude Bizimana, Dr. Samuel Zapata and Dr. Anthony Baffoe-Bonnie, all with the Texas A&M Department of Agricultural Economics , and a water resource engineering expert from Oregon State University, plans to develop an IDSS, “ECO-HAWQS@Farm,” that tightly couples economic and watershed models within an accessible user interface.

The new application is expected to give producers insights on potential outcomes and help them plan in ways that help mitigate risks, Bizimana said. The project will test the performance of ECO-HAWQS by working with small- and medium-sized farming operations in Texas’ Lower Rio Grande Valley and Oregon’s Umatilla River Basin.

Bizimana said team members are engaging with a “focus group” of growers in the Rio Grande Valley to assess factors like acreage, cropping options and rotations, and the use of conservation practices like cover crops or no-till cropping. Researchers’ discussions with growers will help them set parameters for the new application in ways that provide useful decision support in “real-world” conditions.

The research team is close to having enough feedback from farmers and is ready to input data. The next step will be to engage with a larger pool of farmers in the region with small- to medium-sized operations to expand the model’s datasets, Bizimana said.

Bizimana said the goal is to produce a decision support tool that growers across the country and globe can access and use easily.

“We’ll be recruiting farmers for feedback,” he said. “We’re in the early stages of the project, but we all understand the value a tool like this represents for growers, especially smaller operations, to capitalize on opportunities and avoid major setbacks.”

This article by Adam Russell originally appeared on AgriLife Today .

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