macroeconomics - Conversable Economist

October 10, 2011 conversableeconomist

2011 Nobel Prize to Thomas Sargent and Christopher Sims

According to the Nobel website: \”The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2011 was awarded jointly to Thomas J. Sargent and Christopher A. Sims `for their empirical research on cause and effect in the macroeconomy.\’\” But what does that actually mean?

The website of the Nobel organization always offers useful background information about the laureates, including a \”Scientific Background\” paper about the winners. This year\’s background paper about Thomas Sargent and Christopher Sims is going to be hard sledding for those uninitiated into academic macroeconomics–by which I mean it has a bunch of equations. But the opening pages offer an accessible overview of why they are eminently deserving of the prize. Here are some excerpts, mixed with some of my own explanations:

How was macroeconomic analysis done before the work of Sargent, Sims, and others?
Here\’s my own description: If one looks back at how macroeconomics was typically done in the 1960s and into the early 1970s, the common macroeconomic models were big sets of equations–that is, they added up relationships between elements like consumption, investment, saving, imports, exports and total economic output, along with equations for how interest rates and exchange rates affected each other and these categories. A big category like \”consumption\” would be broken down in to durable goods and nondurable goods, and in turn these categories would be broken down still further. The resulting models would have hundreds of equations all interrelated with each other, and adding up to a picture of the macroeconomy as a whole. But as the Nobel background paper reports: \”This estimated system was then used to interpret macroeconomic time series, to forecast the economy, and to conduct policy experiments. Such large models were seemingly successful in accounting for historical data. However, during the 1970s most western countries experienced high rates of inflation combined with slow output growth and high unemployment. In this era of stagflation, instabilities appeared in the large models, which were increasingly called into question.\”

The key role of expectations in this analysis
Many of the public policy discussions in the stagflation of the 1970s focused on expectations. What if workers were expecting higher wages? What if firms could promise higher wages because they expected prices to rise? Were the expectations causing inflation and recession, or were inflation and recession causing the expectations, or were there feedback loops in all of these and other economic factors? The macroeconomics of that time had no clear-cut tools for dealing with these issues.

The background paper puts it this way: \”In any empirical economic analysis based on observational data, it is difficult to disentangle cause and effect. This becomes especially cumbersome in macroeconomic policy analysis due to an important stumbling block: the key role of expectations. Economic decision-makers form expectations about policy, thereby linking economic activity to future policy. Was an observed change in policy an independent event? Were the subsequent changes in economic activity a causal reaction to this policy change? Or did causality run in the opposite direction, such that expectations of changes in economic activity triggered the observed change in policy? Alternative interpretations of the interplay between expectations and economic activity might lead to very different policy conclusions. The methods developed by Sargent and Sims tackle these difficulties in different, and complementary, ways.\”

Sargent and structural econometrics
Instead of trying to build a macroeconomic model on a pile of statistics, and how those statistics added up and interrelated, the approach of Sargent (and others) was to build a macroeconomic model starting from the idea that economic actors like households and firms were doing their best to pursue their own interests. This approach has sometimes been called \”rational expectations,\” but that term is probably misleading. The \”rationality\” here doesn\’t mean that economic actors have all available information, can calculate everything perfectly, and always make correct decisions. It only implies that they won\’t make the same mistake over and over again. In Sargent\’s hands, at least, this approach explicitly leaves open the question of just how people form expectations and learn.

Here\’s the background paper: \”Sargent began his research around this time [the early 1970s], during the period when an alternative theoretical macroeconomic framework was proposed. It emphasized rational expectations, the notion that economic decisionmakers like households and firms do not make systematic mistakes in forecasting. This framework turned out to be essential in interpreting the inflation-unemployment experiences of the 1970s and 1980s. It also formed a core of newly emerging macroeconomic theories. Sargent played a pivotal role in these developments. He explored the
implications of rational expectations in empirical studies, by showing how rational expectations could be implemented in empirical analyses of macroeconomic events–so that researchers could specify and test theories using formal statistical methods–and by deriving implications for policymaking. …
In fact, the defining characteristic of Sargent\’s overall approach is not an insistence on rational expectations, but rather the essential idea that expectations are formed actively, under either full or bounded rationality. In this context, active means that expectations react to current events and incorporate an understanding of how these events affect the economy. This implies that any systematic change in policymaking will influence expectations, a crucial insight for policy analysis.\”

I would add that instead of a model of the macroeconomy with potentially hundreds of variables, Sargent and others worked with models that on the surface appeared much simpler: for example, one example in the \”background\” paper is a model of the macroeconomy that has only three variables: inflation, output, and a nominal interest rate. But the inferences about cause-and-effect in these models are defensible and logical.

Sims and vector autoregressions
Sims pointed out that the earlier generation of macroeconomic models were built on a series of assumptions about how certain economic factors or policies \”caused\” other policies. But in an model of expectations, these statements about \”cause\” needed to be demonstrated, not assumed. Thus, instead of having a model in which some factors caused other factors, Sims proposed that macroeconomic analysis should begin with a model in which is was possible for every factor to \”cause\” a change in every other factor, and in addition for past values of every factor over the last few years to \”cause\” a change in every factor. This approach is called a \”vector autoregression,\” but I often preferred to think of it as starting from a position of honest ignorance.

You then plug in all your data–say, quarterly data over a period of years–and see what patterns emerge. As you might imagine, it immediately looks clear that certain factors are not affecting others. Sims proposed a process for figuring out when certain factors aren\’t connected. As you begin to rule out what is NOT connected, what is left behind is a model of connections that actually exist. It\’s sort of like the way that a sculptor starts with a block of stone, and by gradually removing pieces, ends up with an image.

The background paper puts it this way: \”Sims launched what was perhaps the most forceful critique
of the predominant macroeconometric paradigm of the early 1970s by focusing on identification, a central element in making causal inferences from observed data. Sims argued that existing methods relied on \”incredible\” identification assumptions, whereby interpretations of \”what causes what\”
in macroeconomic time series were almost necessarily flawed. Misestimated models could not serve as useful tools for monetary policy analysis and, often, not even for forecasting. As an alternative, Sims proposed that the empirical study of macroeconomic variables could be built around a statistical tool, the vector autoregression (VAR). Technically, a VAR is a straightforward N-equation, N-variable (typically linear) system that describes how each variable in a set of macroeconomic variables depends on its own past values, the past values of the remaining N – 1 variables, and on some exogenous \”shocks.\” Sims\’s insight was that properly structured and interpreted VARs might overcome many identification problems and thus were of great potential value not only for
forecasting, but also for interpreting macroeconomic time series and conducting monetary policy experiments.\”

Other thoughts and resources
Sargent and Sims were colleagues at the University of Minnesota for about 15 years. The Federal Reserve Bank of Minneapolis puts out a readable publication called \”The Region,\” which often does in-depth interviews with prominent economists about their work. An interview with Sargent from the September 2010 issue is available here; an interview with Sims from the June 2007 issue is available here.

Also, Sims published an article in the Spring 2010 issue of my own Journal of Economic Perspectives called \”But Economics Is Not an Experimental Science,\” on issues of how to draw defensible cause-and-effect inferences from naturally-occurring data. Like all article in my journal, it is freely available courtesy of the American Economic Association.

While no one quite knows what the Nobel committee is thinking when they choose laureates, it seems clear that one standard is whether the ideas have been important enough to launch a sustained research literature. The ideas of Sargent and Sims from back in the 1970s and early 1980s certainly meet this test. Both these authors, and hundreds of others, have built on these ideas for decades.

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September 8, 2011 conversableeconomist

Paul Krugman Critiques Modern Macro

Paul Krugman\’s Presidential Address to the Eastern Economic Association, \”The Profession and the Crisis,\” has been published in the Eastern Economic Journal. Some excerpts:

On the shortcomings of economics in recent years:

\”There is a real sense in which times like these are what economists are for, just as wars are what career military officers are for. OK, maybe I can let microeconomists off the hook. But macroeconomics is, above all, about understanding and preventing or at least mitigating economic downturns. This crisis was the time for the economics profession to justify its existence, for us academic scribblers to show what all our models and analysis are good for. We have not, to put it mildly, delivered.\”

On the failure of many economists to see the housing bubble:

Well, from my point of view — which, because I’m like everyone else, is that what I saw and no more is what everyone should have seen — it still seems bizarre how many economists failed to see that we were experiencing a monstrous housing bubble. As Robert Shiller has documented — and, crucially, was documenting in real time circa 2004–5–6 [Shiller 2005] — the rise in real housing prices after 2002 or so took them into completely unprecedented territory. It was the clearest market mispricing I’ve seen in my professional life, even more obviously out of line than the dot-com bubble, which at least had the excuse that it involved novel technologies with unknown potential; houses have been with us for 7,000 years or so, and we should have a reasonable idea of what they’re worth. …
What about what would happen when the bubble burst? I personally failed to realize how big the “knock-on” effects would be; and according to the self-justifying principle, I’m tempted to say that nobody could reasonably have been expected to get that right. But actually, we should have seen that coming too — maybe not in full detail, but even a casual walk through historical crises should have indicated that a housing bust was likely to bring large financial and balance-sheet problems in its wake. I kick myself every once in a while for failing to think that part through. …
Still, as Yogi Berra said, it\’s tough to make predictions, especially about the future. There are so many things going on in the world, many of them off any modeler\’s radar, that the profession\’s failure to see this crisis coming is not, in my mind, anything close to its biggest sin.\”

A return to the macroeconomic Dark Ages?

\”But what became clear in the policy debate after the 2008 crisis was that many economists — including many macroeconomists — don’t know the simplest multiplier analysis. They literally know nothing about models in which aggregate demand can be determined by more than the quantity of money. I’m not saying that they have looked into such models and rejected them; they are unaware that it\’s even possible to tell a logically consistent Keynesian story. We’ve entered a Dark Age of macroeconomics, in which much of the profession has lost its former knowledge, just as barbarian Europe had lost the knowledge of the Greeks and Romans. …

     All of this would have been OK if the triumph of anti-Keynesianism was justified by superior empirical success. But it wasn’t. As I read the history of the equilibrium approach, it\’s a story of failing upward. Lucas-type models clearly failed to account for the duration of slumps; rather than reconsider flexible prices and rational expectations, Lucas\’s followers moved on to real business cycles (RBC). RBC models failed to generate any strikingly successful predictions, and in fact lost whatever plausibility they had once productivity started becoming pro-cyclical rather than counter-cyclical. But by that time the people doing these models didn’t know that there was any alternative.

     And the result was that faced with a severe economic crisis, the profession spoke with a cacophony of voices. Or maybe a better way to put it is that the policy debate of 2009–2010 was virtually indistinguishable from the policy debate of 1931–1932. Long-refuted doctrines that should have been consigned to the dustbin of history were stated as if they were fresh new ideas — and they were fresh and new to many economists, because our profession had lost so much of its heritage.

     In short, in responding to the crisis, the profession presented a sorry spectacle of unnecessary ignorance that didn’t even recognize itself as ignorance, of bitter debate over issues that were resolved many decades earlier. And all of this, of course, made the profession mostly useless at a time when it could and should have been of great service. Put it this way: we would have responded better to this crisis if macroeconomics had been frozen at the level of knowledge it had in 1948, when Paul Samuelson published the first edition of his famous textbook. And the result has been to leave actual policy discussion without any discipline from the people who should be shaping that discussion: politicians and officials have been free to follow their prejudices and intuitions, never mind the lessons of history and analysis. Economists have failed to fulfill their social function.\”

June 7, 2011 conversableeconomist

Setting up a discussion of the Obama stimulus package

Last week I gave a talk to an alumni group here at Macalester College about budget deficits in the short run and in the long run. For the short-run portion of this talk, I set up my discussion with two figures. I reproduce them here, in part for those who would like to cut-and-paste a copy for their own presentations.

The first figure shows three paths for the unemployment rate. The middle line shows the path for the unemployment rate presented by Christina Romer, head of President Obama\’s Council of Economic Advisers, in January 2009–more specifically, it\’s actual data up though 2008, and then a forecast after that date. The bottom line shows Romer\’s prediction that the unemployment rate would be lower if the \”stimulus package\” legislation — the American Recovery and Reinvestment Act of 2009– was enacted. The top line shows the actual path of the unemployment rate. This version of the figure appears in \”Did the Stimulus Stimulate? Real Time Estimates of the Effects of the American Recovery and Reinvestment Act,\” by James Feyrer and Bruce Sacerdote, NBER Working Paper 16759, February 2011.

Of course, this figure is often used to argue that the stimulus didn\’t work. But of course, it is equally possible that the January 2009 forecast was too optimistic, and that the stimulus made the unemployment rate lower than it would otherwise have been.

The Congressional Budget Office publishes regular estimates of the effects of the ARRA legislation. The May 2011 report, for example, states that the legislation:

— Lowered the unemployment rate by between 0.6 percentage points and 1.8 percentage points,
— Increased the number of people employed by between 1.2 million and 3.3 million, and
— Increased the number of full-time-equivalent jobs by 1.6 million to 4.6 million compared with what would have occurred otherwise …

Here\’s a useful CBO figure for illustrating their case that the stimulus improved matters.

With these two figures to set up the discussion, one can then talk about what makes a stimulus package more or less effective in a variety of contexts. Is it timely, temporary and targeted? What about factors like the pre-existing level of debt, the extent to which the economy is open to international trade, the response of monetary authorities, and other factors?

In their NBER paper, Fehrer and Sacerdote summarize some of the conflicting evidence about the effects of the stimulus. Their own findings, as summarized in the abstracts are: \”A cross state analysis suggests that one additional job was created by each $170,000 in stimulus spending. Time series analysis at the state level suggests a smaller response with a per job cost of about $400,000. These results imply Keynesian multipliers between 0.5 and 1.0, somewhat lower than those assumed by the administration. However, the overall results mask considerable variation for different types of spending. Grants to states for education do not appear to have created any additional jobs. Support programs for low income households and infrastructure spending are found to be highly expansionary. Estimates excluding education spending suggest fiscal policy multipliers of about 2.0 with per job cost of under $100,000.\”

June 1, 2011 conversableeconomist

When Should Short-Term Fiscal Stimulus Go Into Reverse?

If policy-makers use fiscal policy–that is, some mixture of government spending and tax cuts–to stimulate an economy in recession, at what point should this short-term policy be reversed? Alan Auerbach and William Gale point out that over the last four U.S. recessions, the willingness to reverse short-term fiscal policy has diminished.

For example, in the deep recession of 1981-82, President Reagan had pushed for tax cuts in 1981. But in
August 1982, when the recession was not yet over, Congress passed and Reagan signed the Tax Equity and Fiscal Responsibility Act (TEFRA), scaling back those tax cuts. Similarly, in the recession of 1990-91, the first President George Bush and Congressional leaders produced legislation aimed at reducing in October 1990–when the recession was still going on.

This pattern has changed, so that instead of acting to reduce looming deficits while the recession is still going on, the U.S. now continues its fiscal stimulus policies a couple of years into the recovery. For example, the recession that ended in November 2001 was followed by additional tax cuts in 2002 and 2003. Now, with a recession that ended two years ago in June 2009, there is still a widespread belief that it is \”too soon\” to undertake a serious effort to reduce the budget deficit.

I\’m a reluctant and dour Keynesian who supports aggressive fiscal policy during a recession, especially a dire event like the Great Recession of 2007-2009 — but who views such policies as only helping to ameliorate the pain of the recession itself. The arguments for continuing fiscal stimulus for several years after a recession has ended are much weaker. The Congressional Budget Office forecasts that the U.S. economy won\’t return to full employment until 2016. It seems unwise, to plan on eight consecutive years of fiscal stimulus, from when the recession became apparent in 2008 until the economy returns to full employment in 2016. In fact, global capital markets, looking at the projected rise in the debt/GDP ratio, may be unwilling to let the U.S. government borrow to pursue such a policy.