The Wisdom of Laureates

The recent New York Times article on the Minneapolis Fed President, Narayana Kocherlakota is making a moderately big splash on economics blogs because it highlights Kocherlakota’s prominent reversal of his stance on monetary policy.

However, it’s the quote from Ed Prescott that seems to have most people talking.  Prescott is quoted as saying

It is an established scientific fact that monetary policy has had virtually no effect on output and employment in the U.S. since the formation of the Fed.

Paul Krugman says this statement reveals that Prescott is part of “an irrational cult.”  Noah Smith takes this as evidence that the Nobel Prize in economics is screwed-up because, just a few years after Prescott won the Nobel, Chris Sims won and Sims thinks that monetary policy has real effects.  Steve Williamson says that Prescott might simply be trying to be provocative (though he also admits that perhaps there is a case to be made that Prescott is right).

OK, first off – Prescott is wrong.  It is NOT an established fact the monetary policy has no effect on economic activity.  The balance of the evidence suggests the opposite.  Monetary policy seems to have clear measurable effects on the economy.  This isn’t what I want to talk about however.

It struck me as I read the Prescott quote that perhaps we should think a little bit before listening so closely to the opinions of Nobel Laureates.  Nobel Laureates are called upon often to make pronouncements and give policy assessments.  Some of them are asked to be regular contributors to the New York Times.  But should we pay so much attention to their opinions?  Should we grant Ed Prescott, or Paul Krugman, or Robert Lucas, or Peter Diamond much more credence than other smart observers?  It’s really not clear.

All of the Nobel winners are extremely smart.  They are a special group though.  Nobel Prize winners have typically devoted their entire careers to a rather narrow study of a particular area.  I’ll use Paul Krugman as an example.  Paul Krugman’s opinions on trade have to be taken very seriously.  When it comes to the best understanding of international trade, Krugman is the master of the universe.  When he moves on to topics outside of trade however, his assessment loses a lot of its authority.  Krugman is an excellent economist so it’s perfectly reasonable to expect that his opinions on heath policy, tax policy, business cycles, finance, etc. will be reasonable and it’s worth listening to him.  That said, he is not an expert on any of those topics the way he is on trade.

In addition to the fact that Nobel Laureates often have very narrow areas of true expertise, they are also often radical thinkers who are out to overturn established ways of thinking.  Paying attention to a Nobel Prize winner means paying attention to someone who thinks unusual thoughts.  Academics are rewarded not for having good insights on average; they are rewarded for having path-breaking ideas every now and then.   An academic who has one or two ingenious ideas in a career might well be viewed as someone worthy of a Nobel, even if most of their ideas are crazy.  In academia, we can have as many crazy ideas as we want provided that we have a few insightful ideas along the way.  In fact, the price we pay for having unusual insights might be that we often have unorthodox approaches to topics.  I would guess that the Nobel laureates are even more extreme than typical academics.  Prescott’s statement is obviously a bit unorthodox but that is the mode he has been in his whole career.  “Toeing the party line” is just not part of his playbook.  Prescott didn’t win the Nobel Prize for having a balanced assessment of the mainstream consensus of economic expertise.  He won it for putting forth new ways of thinking about economics.  This isn’t limited to Prescott.  Even Paul Krugman has been known to say some rather nutty things at times.

Bobby Fischer was perhaps the greatest chess player ever and was certainly extremely intelligent.  At the same time, Bobby Fischer said many crazy things during his life (he was a Holocaust denier, he thought the U.S. orchestrated the 911 attacks, …).*  Listening to Fischer talk about chess is one thing.  Listening to him talk about foreign policy would be like entering the Twilight Zone.  I certainly don’t want to make a very strong comparison between Bobby Fischer on the one hand, and Paul Krugman and Ed Prescott on the other.  But, when we listen to people like Prescott and Krugman we have to remember that we are drawing on the opinions of a very unusual group.

* Bobby Fischer is a truly tragic figure.  This clip from the ESPN short Finding Bobby Fischer gives a depressing glimpse of Fischer’s life.

Puzzles, Progress and the Scientific Method

In Noah’s reply to my earlier post, he interprets me as saying that purely empirical studies are “worse” than theoretical contributions even if the theories are rejected.  I hope I didn’t give the reader this impression because that certainly wasn’t the point I wanted to make.  If I did, please let me clarify. 

The scientific method goes something like this:

  1. Observation
  2. Formation of hypotheses
  3. Testing/evaluation
  4. Repeat

If you can follow these steps then anything (even economics! even macroeconomics!) can be studied scientifically.  When economics is at its best it truly is a science. 

A purely empirical study is a necessary step in the scientific method (it’s step 1).  Indeed, purely empirical studies, by which I mean simple observational studies, are somewhat undervalued in economics.  However, following the scientific method means that after the observation, we have to move on to forming a hypothesis (building a model or otherwise articulating a theory) and then go on to testing and evaluating the hypothesis (this is the hard part, and the part that is often quite unpleasant for many theories).  Step 1 is not “worse” than step 3.  Both are necessary.  I also didn’t say you should try to hypothesize without looking first at the data and I don’t think macroeconomists are typically modelling without a good grasp of the facts. 

Noah makes some other remarks which I also find interesting. 

First, he makes some surprising remarks about “moment matching.”  It’s true that macroeconomists often evaluate their theories by comparing the statistical moments implied by the model with the analogous moments in the data but it’s hard to see this as a problem.  Moment matching is the underpinning of almost all statistics.  Estimating a mean and a variance is a special case of matching moments.  So is OLS estimation (so is IV and basically every other econometric technique other than maximum likelihood estimation).  I’m a little puzzled as to what Noah would suggest that economists should be doing?  In fact, drop the word “moments” and ask yourself this: should we compare our theories with the data?  I would say we should.  Do we want the theories to match up with the data?  I would say we do. 

Second, he questions whether quantification is valuable when often the theory is rejected.  The way I look at it, the parameters are inputs into the models and will likely have relevance in many settings.  Suppose you have a labor supply / labor demand model and you want to use it to analyze an increase in the minimum wage.  The model makes a prediction and you could use this prediction as a basis for anticipating the effect of the policy.  The predicted change in unemployment will be a function of the labor supply elasticity and the labor demand elasticity.  You can certainly estimate these parameters without testing the model.  Suppose you find that there is no change in employment in the short run and a somewhat larger change in employment as time passes.  This would of course be a rejection of the simple model but it would suggest ways of modifying the model to accommodate the new observations.  Moreover, the parameter estimates retain their usefulness even if the model is rejected. 

Noah’s claim that empirical puzzles are easy to come by is simply wrong.  Noah’s (intentionally) facetious example would only be a puzzle if we had some reason to believe the theory a priori.  None of the puzzles in macroeconomics have trivial explanations.  Here’s an example: inventory accumulation is strongly procyclical (a basic observation).  Is this a puzzle for demand driven business cycle theories?  (No – see Bils and Kahn 2000!).  Here is another example: the real relative price of investment is countercyclical (again a simple observation).  Is this a puzzle?  It might be.  If one thinks that fluctuations in investment demand is an important cause of business cycles then you might think that this real price would be pro-cyclical.  It’s not.  Should we retain this observation?  Is it acceptable to highlight this observation as a puzzle?  I think the answer to both questions is ‘yes’.  Of course, one way to avoid puzzles is to simply avoid trying to explain the data.  You can’t lose a race if you don’t run it. 

There is the famous example of Johannes Kepler who tested the heliocentric theory of the universe (proposed by Copernicus).  His model assumed that planets orbited the sun in perfectly circular orbits.  Unfortunately, the data rejected the model.  He later tried different orbital patterns and discovered that the heliocentric theory worked if the planets followed elliptical orbits.  Was the intermediate stage of rejection (and depression, confusion, …) a sign that his efforts were wasted?  Not at all.  The famous Princeton mathematician Andrew Wiles once compared his work to stumbling around in a darkened room.  This is an excellent analogy and I often have to remind myself that confusion and frustration are the norm. 

Caballero is certainly correct about macro/finance being at the periphery rather than at the core of macro.  It used to be that price rigidity was at the core while financial market imperfections were at the periphery.  I suspect this is slowly changing.  If you are going to try to understand the financial crisis, you will need to have a model that has a central role for financial market failures.  

Is Macro Giving Economics a Bad Rap?

Noah Smith really has it in for macroeconomists.  He has recently written an article in The Week in which he claims that macro is one of the weaker fields in economics and even though there is little good work being done in macro, there is plenty of good work being done in other fields.

I think the opposite is true.  Macro is one of the stronger fields, if not the strongest.  At first glance it may appear to be a problematic area in economics but it is not – it is actually much healthier than most fields. [1]

A recurring theme in Noah’s article is that macro has some sort of a problem which other areas don’t have.  I think this is wrong.  Macro is quite productive and overall quite healthy.  There are several distinguishing features of macroeconomics which set it apart from many other areas in economics.  In my assessment, along most of these dimensions, macro comes out looking quite good.

First, macroeconomists are constantly comparing models to data.  Now, it’s true that many of the models used by macroeconomists (that is, the way we try to understand the world) have a really tough time when they are compared to the data.  Of course it is a problem that the theories are so soundly rejected but isn’t it worthwhile to make this comparison and to be candid about the results?  Noah gives his readers the impression that other theories are doing much better but is this really true?  In many other areas in economics the theories aren’t rejected because either the theories are never tested or the theories simply don’t exist.  There are many purely empirical studies in which there is little theory to speak of.  There are other areas in economics which are purely theoretical and the models in this research are rarely tested against actual data.  Holding theories up to the data is a scary and humiliating step but it is a necessary step if economic science is to make progress.  Judged on this basis, macro is to be commended because it so often takes this step and brings the theories close to the facts.  There are other fields that make these comparisons – trade is a notable example.  Modern versions of the Ricardian trade model and the Heckscher-Ohlin model are often compared with data.  Similarly, labor matching models are routinely compared with data though again there are more failures of these theories than successes.  [2]

Second, in macroeconomics, there is a constant push to quantify theories.  That is, there is always an effort to attach meaningful parameter values to the models.  You can have any theory you want but at the end of the day, you are interested not only in idea itself, but also in the magnitude of the effects.  This is again one of the ways in which macro is quite unlike other fields.

Third, when the models fail (and they always fail eventually), the response of macroeconomists isn’t to simply abandon the model, but rather they highlight the nature of the failure.  This is again a good research habit because mistakes and rejections have value – knowing the nature of the mismatch between the model and the data helps you to refine the theory.   There are many “puzzles” in macroeconomics (the excess sensitivity puzzle, the risk-free-rate puzzle, the equity premium puzzle, the international comovement puzzles, and so on, …).  At a superficial level one might be tempted to conclude that the prevalence of such puzzles shows that the field is in a constant state of disarray.  In fact, these mismatches between theory and data serve as an important guide to how to modify the theories.

Lastly, unlike many other fields, macroeconomists need to have a wide array of skills and familiarity with many sub-fields of economics.  As a group, macroeconomists have knowledge of a wide range of analytical techniques, probably better knowledge of history, and greater familiarity and appreciation of economic institutions than the average economist.

In his opening remarks, Noah concedes that macro is “the glamor division of econ”.  He’s right.  What he doesn’t tell you is that the glamour division is actually doing pretty well.

[Note 1]  It is not clear that macro is a field the same way that Public Finance, Labor, Development, Trade, etc., are fields.  I used to think that macro was a field but I’ve since changed my mind.  Instead I think that basically every field has a component of it which is macro.  So, in Public Finance you have people like Martin Feldstein, Alan Auerbach, Larry Kotlikoff, etc.  These researchers work in Public Finance obviously but they also have very strong macro components to their work.  In Labor there are researchers like the late Dale Mortensen, Chris Pissarides, Rob Shimer, Steve Davis, John Haltiwanger, and so forth.  Again, these economists all have very pronounced macro components to their work.  In fact, some of them (Mortensen and Shimer certainly) might be described most often as macroeconomists even though most of his work is on the theory of labor supply and demand.  The macro component of Development, is growth, with researchers like Daron Acemoglu, Chad Jones, Debraj Ray, etc. … In Economic History you have people like Christy Romer who is obviously a very well-known macroeconomist in addition to being a historian.  So, I think the proper way to view macroeconomics is not as its own field, but instead as half of all of the fields.

[Note 2]  There are examples of theories in economics that do exceptionally well.  What Larry Summers derides as “ketchup economics” – the application of no-arbitrage conditions to financial markets – does really well in the real world.  My understanding is that if you take modern option pricing formulas and examine historical option pricing prior to Black-Scholes you find a surprising amount of agreement between the actual market prices and what the Black-Scholes formula implies.  (There are aspects where the model doesn’t do as well as it should but overall it does remarkably well – if there are finance people who want to weigh-in an tell me that I’ve got this all wrong please go right ahead …).  Another area in which there is a strong coherence between predicted and actual behavior, is the study of auctions.  Auction theory and option pricing models are excellent examples of not only comparing theory to data but also examples in which the theory does a remarkably good job.

A further thought:  It’s true that most of the theories that we have run into trouble.  Noah has another recent post in which he points out that the consumption Euler equation does not fare very well in macroeconomic models.  That’s true though there are more in-depth, detailed analyses in which the model does much better.  For instance, there is a well-known paper by Gourinchas and Parker who use extremely detailed individual-level data together with a structural model and they show that most household’s exhibit behavior that suggests that they are liquidity constrained up to roughly age 40 – age 45 at which point they switch over and start exhibiting behavior that is more consistent with the PIH and what the Euler equation would predict.  In contrast to what Noah would have you believe, this is an excellent example of a theory being refined and sharpened as a result of being compared to detailed data.

Inequality in Adam Smith’s World

Inequality is a fact of economic life and it is becoming more and more pronounced over time.  It’s not exactly clear why this is happening but it is clear that it is happening.  Inequality is certainly rising for pre-tax income measures and I’m virtually certain that it is rising for after-tax measures of income as well. 

There are many forces in our economy that create income inequality.  The most basic of these forces however, is tied to the nature of trade itself and can be traced all the way back to Adam Smith and the division of labor.  If you read the Wealth of Nations (something I told you not to do a few blog posts back …) you will find that Smith begins by marveling at the incredible increases in productivity that can be obtained by exploiting the division of labor, that is, by specializing.  The way Adam Smith envisioned it, a market system would be populated by specialists.  Each person would have a single craft that they would focus on.  This is a world with doctors, lawyers, carpenters, teachers, chefs, and so on.  A jack-of-all-trades is simply not valued in a market system.  (Renaissance men are dinosaurs in Adam Smith’s world.) 

The way you get the division of labor to work is by combining it with trade.  Once you get to the point where you conclude that the division of labor is something you want to take advantage of, trade is a necessary next step.  A physician cannot consume only her own medical advice – she must draw on the productivity of the many other specialists in society.  If you are willing to do trade, you can, by specializing, reach incredible levels of productivity.  As populations grow, and as the ability to trade grows, you should see more and more specialization and higher and higher productivity.  (Obviously the division of labor is not the only source of increases in productivity.) 

However, there is a downside to this plan.  Adam Smith’s plan exposes people to incredible variations in income and thus a market system possesses and important force which causes inequality.  Specialization ties your entire wellbeing to a single industry.  If you decide that you are going to become a web designer, your fate is very closely tied to the market for web designers.  As a result, while Smith’s plan dramatically increases overall productivity, it also exposes us to incredible risk. 

Of course, markets do have responses to risk.  The typical response is for the market to provide insurance contracts to reduce risk.  In this case however, the type of insurance needed is actually income or job insurance.  These types of insurance contracts are not typically available.  It is possible that you might be able to use the stock market to insure yourself against job risk.  For instance you might be able to short-sell claims on the company you work for.  I suppose I could short-sell stock based on the profitability of the University of Michigan Economics Department.  In the event that the U of M Econ department gets in serious trouble, my short position in these assets would have a huge payoff and I would be insured.  In reality, few people have asset portfolios like this.  Moreover, it doesn’t seem like insurance companies are able to provide “career” insurance.  (In part, this post is related to work in the so-called “New Dynamic Public Finance.”)

I don’t mean to imply that all or even most of the alarming increase in inequality is due to the division of labor and trade.  I would guess that modern technology (which allows people to leverage luck to extreme degrees by cheaply reproducing and transmitting ideas and information) and inheritance, both play a significant role in creating inequality.  However, living with income inequality is an implicit part of the deal we made with Adam Smith and it will be with us in some form for a long time.  

The Robin Hood Principle and the Minimum Wage

Robin Hood stole from the rich and gave to the poor. However you feel about the ethical and efficiency implications of active income redistribution, I think most people would agree that Robin Hood was at least making transfers in the right direction. That is to say, even if you are strongly opposed to redistribution, once we have decided that we are going to redistribute, we had better be shifting income from the rich to the poor. I am open to reasonable variations on the ‘Robin Hood’ principle: from the fortunate to the unfortunate, from the privileged to the oppressed … you get the idea. If you are transferring income, at least make sure it’s going to and from the correct people.

The President supports an increase in the Federal Minimum wage from its current value of $7.25 to roughly $10.00 per hour. Now, you might think a policy like this might be a bad idea for an economy that is still pretty weak. Going from 7 dollars an hour to 10 seems like a pretty steep increase in labor costs and I wouldn’t be surprised if it ultimately cost quite a few jobs at the low end of the pay scale. Nevertheless, the bulk of the empirical evidence on the minimum wage seems to point to only modest job losses for minimum wage workers (where am I getting this “bulk of the evidence” thing you ask? – I asked the labor economists in Lorch Hall of course …). That said, I don’t support an increase in the minimum wage and I don’t think any economist should endorse the idea.

The minimum wage is simply bad policy. The main reason that it is poor policy is that (in addition to the fact that it distorts the labor market a bit) it violates the Robin Hood principle. Here are the best reasons to support an increase in the minimum wage:

1. It will probably transfer some income to low-wage workers and there are some wealthy people who will implicitly pay for this transfer.
2. It probably won’t have huge effects on employment for low-wage workers or a huge effect on business profits for firms that hire low-wage workers.
3. It is politically popular.

This last point, that the minimum wage is politically popular, might be the best reason to support it. Minimum wage laws are easy to understand. They can be sold by people who claim (truthfully I believe) to care about the poor. Also, the costs of the policy are hidden – the policy is discussed without candidly disclosing who actually pays for the policy. Among all policies that actually help low-income families, the minimum wage might be the one most likely to be passed into law.

It’s still bad policy though.

Let’s assume, in the best case scenario, that there will be no effect on the employment of low-wage workers. Minimum wage workers will simply get more money. Where is this money coming from? There are several possible sources.

The most obvious answer is that the transfer will come from the profits of businesses that hire lots of low wage workers. This might already strike you as a bit odd – Goldman Sachs doesn’t employ many (any?) workers at the minimum wage and as a result it doesn’t have to make any transfers to minimum wage workers. In contrast, businesses like McDonalds employ thousands of low wage workers and their reward for doing so will be that they get to pay a penalty called the minimum wage. OK, who has a claim on these profits? It depends on the business of course. If we are talking about profits of some huge publicly traded firm then the people who pay for the minimum wage will be shareholders so think of the typical owner of a mutual fund – perhaps a retiree or an upper income saver. If the business is a small family owned grocery then it will be the family members who own the business. These people are probably members of the middle class or perhaps upper middle class.

There are of course other people who will pay to fund the minimum wage. To the extent that businesses raise prices in response, the minimum wage will be financed by customers of these firms. OK, who buys goods from Walmart? From McDonalds? If you think these customers are particularly affluent, think again.

What about the people who get the transfer? While there are many people earning the minimum wage who are truly low-income, there are many who are not. A single mother who works a minimum wage job to make ends meet is very different from a teenager who works for minimum wage to get cash for the weekend. Unfortunately, the minimum wage is a blunt instrument – it can’t distinguish between these two people.

The truth is that, the reason that the minimum wage is politically popular is exactly the same reason that we should be opposed to it. In the best case scenario, while there are no jobs lost, it is not clear that we are transferring from the rich to the poor. It is certain that some low-income households will lose money because of the minimum wage. It is equally certain that some upper-income people will benefit.

This is not to say that we should not push for more aggressive income redistribution. The disparity in income inequality has been rising for quite some time and it is completely correct for people to desire a more equitable distribution of income. There are better policies that could be considered however. Expansion of the EITC is often offered as an example of an effective redistributive tool where we have control over who pays and who receives. A negative income tax bracket could transfer income to families in need, and encourage employment of low income workers.

Dwight Eisenhower’s farewell speech (Jan 17, 1961) still rings true … .

The most often quoted segment: 

This conjunction of an immense military establishment and a large arms industry is new in the American experience. The total influence – economic, political, even spiritual – is felt in every city, every state house, every office of the federal government. We recognize the imperative need for this development. Yet we must not fail to comprehend its grave implications. Our toil, resources and livelihood are all involved; so is the very structure of our society.

“In the councils of government, we must guard against the acquisition of unwarranted influence, whether sought or unsought, by the military-industrial complex. The potential for the disastrous rise of misplaced power exists and will persist.

We must never let the weight of this combination endanger our liberties or democratic processes. We should take nothing for granted. Only an alert and knowledgeable citizenry can compel the proper meshing of the huge industrial and military machinery of defense with our peaceful methods and goals, so that security and liberty may prosper together.

(Does it seem to anyone else that the quality of U.S. leadership has been steadily falling for the past 50 years?)

Rational Expectations and Reality

Last week, while I was going over the history of macroeconomic thought in class, I briefly discussed the concept of Rational Expectations and the place it occupied in the development of modern macroeconomics.  In truth, I don’t spend a lot of time talking formally about rational expectations because it is simply part of the standard backdrop.  That is to say, rational expectations (RE) models are not contentious anymore.  This was not always the case.  When rational expectations models were being developed in the 1960’s and early 1970’s it was a very foreign concept and was met with skepticism by mainstream macroeconomists.  Today, rational expectations are the norm and are rarely discussed as a controversial part of the field.  Outside of the economics profession, the idea that people possess something called rational expectations in the real world is often assumed to be wholly unrealistic and prima facie evidence that economic reasoning is useless and out of touch.

I’m going to use this post to discuss the meaning of rational expectations because (1) I think it is one of the most commonly misunderstood aspects of macroeconomics and (2) for the most part, rational expectations is essentially true in the real world – that is to say, most people actually have rational expectations.

The notion of rational expectations that I prefer (what I might call the weak-form of RE) goes something like this:  People have rational expectations if their beliefs are consistent with the world they live in.

People hold lots of beliefs and these anticipations and expectations constantly influence their choices.  For example, the exact time you leave for work in the morning might be influenced by your beliefs about when traffic is particularly heavy.  When you leave might also depend on your beliefs about when the parking garage at work fills up.  Your choice of restaurants might be influenced by how busy you think the restaurant will be, and so forth.  The RE hypothesis says that these beliefs are consistent with the real world.  If you think that your parking garage fills up by 8:45 am then you should not observe that the garage typically has empty spaces at 9:00.  This is not to say that RE implies that your beliefs are perfectly correct.  Expectational errors (or forecast errors) are perfectly consistent with the rational expectations hypothesis.  For instance, if I roll two 6-sided dice, you will guess that the most likely number to get is 7.  You will usually be wrong of course, but you won’t be systematically biased in your beliefs.  It’s easy to find economic examples of RE as well.  For example, if I tell you the economy is experiencing high unemployment and low inflation then you might guess that the Fed is more likely to cut the Federal funds rate rather than to raise it (assuming we aren’t at the zero lower bound of course).  Again, the bond market traders could be surprised, but they shouldn’t be systematically surprised.

For this definition of rational expectations (the weak-form of RE), there is no need for individuals to understand why they observe the patterns they observe.  They don’t need to know the model.

The examples I chose above are all cases in which RE works well.  In these cases, RE is clearly the correct way to understand people’s actual beliefs and behavior based on those beliefs.  Part of the reason these situations work well is that people have lots of experience with these situations.  Everyone knows how to play the morning commuting game.  Everyone knows how to play the restaurant game and so forth.  We have repeated exposure to these situations and so beliefs which don’t fit with the real world will be punished and inevitably people will adopt “correct” beliefs.  (It will take only a few days when you can’t find parking at 8:55 am before you revise your anticipations.)

Of course, there are situations when the rational expectations hypothesis is much less likely to hold.  For example, we only play the marriage game one (or two or three) times.  Hopefully we will only play the retirement game once.  In these cases, we can’t rely on experience to discipline our beliefs and guarantee that our expectations will be rational.  This doesn’t necessarily mean that RE isn’t a good assumption.  We can still learn from the experiences of others; we can ask our friends or relatives what their experiences were like, and so forth.

The reason that RE is so common in macroeconomics is twofold.  First, because much of macroeconomics concerns behavior which unfolds over time, anticipations and expectations inevitably influence current choices.  As a result, macroeconomists have to make some assumption about where these beliefs come from.  Second, while many situations might not be natural settings to trust a RE solution, RE is often a good benchmark.

One area in macroeconomics in which RE solutions are used often but in which we have good reason not to trust the solution concerns one-time unanticipated events.  An investment tax subsidy should of course encourage investment but it should also influence the long-term payoff to capital.  Forming expectations about this payoff requires investors to form accurate beliefs which extend far into the future.  This seems like a hopeless case.  The common solution (and the one I would use) is to invoke RE.  This is convenient because it allows me to “fill in the blanks” and attach beliefs to future events but I’ll be the first to admit that this is a bit of a stretch.  An even more difficult case might concern a policy for which we have little basis for comparison at all.  The rapid extension of loan guarantees to financial intermediaries during the crisis surely had important effects on the market and surely behavior was conditioned to some extent by beliefs.  I doubt these beliefs were anything resembling rational expectations though.  My suspicion is that situations like this create confusion on the part of market participants.  Unfortunately we don’t really have a good theory of confusion yet.

At its core, the rational expectations hypothesis says that your beliefs are not really your own.  Your seemingly subjective and personal beliefs are influenced by the world you live in.  In econ-lingo, your beliefs are endogenous.

Let me make one final comment.  RE doesn’t really say anything directly about where your beliefs come from or how they are built up over time.  If you move to a new city, you will gradually learn about the local commuting patterns and as you do so, your beliefs will converge to RE beliefs and your commuting choices will become more and more refined.  Technically, RE assumes that the people in the model economy already have rational expectations.  That is, RE doesn’t incorporate learning.  Of course we have the ability to include learning but it is not too common in most models.  There is of course the important issue of whether learning will actually lead to a RE equilibria.  Usually it will – among the many important papers in one by Kalai and Lehrer (1993) which says that eventually RE will emerge provided that peoples’ initial beliefs possess a “grain of truth.”

(OK, I’m going to stop here, if I’m not careful I will go into full blown geek-out mode … )

Noah Smith’s not-so-damning critique of DSGE models.

Noah Smith and Matthew Yglesias both have recent posts in which they argues that because DSGE models have not been adopted by investment bankers and other financial market participants that they have failed the market test.  As Noah puts it, “(if) DSGE models work, why don’t people use them to get rich?”

Noah continues: “If you have a model that both A) satisfies the Lucas Critique and B) is a decent model of the economy, you can make huge amounts of money. This is because although any old spreadsheet can be used to make unconditional forecasts of the economy, you need Lucas-robust models to make good policy-conditional forecasts.” 

This “market test” argument might sound good but Noah’s critique is actually somewhat off target.  The fact that investors do not use DSGE models to make money might says basically nothing about whether DSGE models are useful analytical tools. 

Think about simple supply and demand models.  Supply and demand models are DSGE models and they will fail the market test that Noah emphasizes.  (For those of you who don’t know, DSGE stands for Dynamic Stochastic General Equilibrium.)  To be specific, let’s consider supply and demand in the market for oranges.  How the market behaves is determined by the elasticities of supply and demand which, respectively, tell us how price sensitive orange farmers and orange buyers are.  OK, now suppose that occasionally there are spells of bad weather which make growing oranges difficult.  A casual observer would notice that when the weather gets bad in Florida, the price of oranges rises and the quantity produced and purchased falls.  When the weather is good, prices are low and quantities are high.  This observer will notice these patterns and the patterns will become part of her beliefs about the world she lives in.  Of course, the observer may not understand why this pattern exists – she merely understands that the pattern does exist. 

Alright, now let’s extend our supply and demand model a bit.  Let’s now suppose that weather conditions are somewhat persistent from year to year.  If the weather is bad this year then it is likely to be bad next year.  In this case, when prices are high one year, they will tend to be high next year (high prices this year means that the weather must be currently bad).  Again, our casual observer will incorporate this pattern into her beliefs and again she will not be required to understand why this pattern exists.  Suppose we add a financial market which coexists with the orange market.  The financial market sells claims on future orange prices.  A hypothetical contract might pay one dollar in the event that the price of oranges next year is above the historical average price. 

If you are following along, you will realize that we are squarely in DSGE territory.  This is obviously an Equilibrium model; the model is Stochastic (due to the recurring random swings in the weather); the model is Dynamic (due to the persistence of the weather conditions), and the model is General (due to the presence of both an orange market and the financial market making bets on the future price of oranges, both of which are in equilibrium).  In fact, as I’ve described it, it sounds like the model satisfies the rational expectations hypothesis too. 

Suppose now an economist comes up with a model which explains the price and quantity variations in terms of supply and demand.  Unbeknownst to this economist, the model is actually true.  The model provides a meaningful and accurate description of how the orange market works.  However, the model is not particularly useful for predicting future prices.  The model says that if there is an adverse shift in supply, then prices should rise and quantities should fall.  Given the shift in supply, the amount of the price and quantity change are governed by the two structural parameters (the two elasticities).  However, predicting future prices in this environment boils down to predicting the weather, and on that score, the supply and demand model, despite being true, is of little help.  In contrast, quantifying the observable patterns in the data is definitely helpful for the purpose of forecasting future prices.  In fact, the current price contains valuable information on the likely future price.  A simple regression of the current price on the past price will provide financial market participants with enough information to price bets on future prices.  (If prices and quantities are measured with error, then the best forecast will make use of both price and quantity to predict the future price.) 

In this environment, the financial traders have no use for the DSGE model.  Thus this supply and demand system will fail the market test in Noah’s and Matt’s posts.  At the same time, the supply and demand model provides key insights into how this market works. 

In fairness, Noah does sneak in a slight caveat in his post.  He says that a correct DSGE model should do a good job of providing policy-conditional forecasts.  Fair enough.  If there is a change in policy then the statistical patterns that prevailed in the past might well change (this is an instance of the well known Lucas critique).  If there were a subsidy to orange farmers in our example, the economist’s DSGE model would correctly predict that average prices would fall and average quantities would rise and so you might think that having a correct prediction would mean that the model would be valuable in this instance.  Are we really to believe that, faced with some new policy, people don’t turn to models like this to refine their predictions?  I would think that it would be reasonable to think that for most purposes, investment bankers can simply use purely ad hoc statistical forecasting methods – methods devoid of any structural economic content but which have substantial predictive content — to make market predictions.  In the rare instance that there is some important change in policy they might use a structural model to adjust their predictions.  Ask yourself this: when the Affordable Care Act was being discussed, how do you think observers and participants in the markets for health care made predictions about what might happen to their industry?  If your answer is that they turned to estimated structural economic models, then can you really say that these models are failing the test of the market?  

Is the General Theory worth reading today?

My first class for graduate Macroeconomics II (ECON 607) was on Wednesday and I began, as I always do, with a very brief overview of the history of macroeconomic thought – a history which begins with Keynes’ General Theory of Employment, Interest and Money (1936).  Published in the wake of the Great Depression, the General Theory contained many new ideas which still animate a lot of research and policy intuitions today.  Virtually all macroeconomics in the 40 years (roughly) following the General Theory was based to some extent on the Keynes’ ideas or on the Hicksian image of Keynes’ ideas in his IS/LM model.  Hick’s IS/LM model was introduced in “Mr. Keynes and the Classics: A Suggested Interpretation” published in Econometrica in 1937.  (In the interest of full disclosure, there are many economists who did not, and do not, accept that Hick’s IS/LM model captures the core ideas in Keynes – a future post perhaps.)

As I was talking about this material in class and trying to give the first-year students a sense of how these contributions connected with work going on now, I made the off-hand remark that I didn’t think that it was really worth reading the General Theory as a first year graduate student.  I didn’t mean that reading the General Theory is a bad thing to do of course.  I just wasn’t convinced it was the best use of the students’ time, at least not at this point in their careers.

My guess is that the important insights of these earlier contributions have been, more or less, adequately incorporated into modern textbooks.  I’m fairly sure that few modern biologists read Darwin’s original Origin of Species, and even fewer modern mathematicians read Euclid’s Elements.  If you want to have a good understanding of modern geometry and number theory, you should simply read a good college-level mathematics text on the subject.  The same holds for the study of evolution.  As great as Darwin’s contribution was, our modern understanding of evolution now eclipses his.  This is the reason behind my casual dismissal of the idea of reading the original General Theory.  If you want a good understanding of these ideas, you are better served by consulting say Mankiw’s intermediate-level Macroeconomics than by reading Keynes or Hicks.  For a somewhat more advanced treatment, you can take a look at Chapter 10 in Blanchard and Fisher (1989).

I would guess that the same holds for other revered tomes as well.  Is there really good reason to read the original Wealth of Nations or Ricardo’s Principles of Political Economy?  I doubt it.  The contributions of authors like Smith, Ricardo and Keynes (and many others), are so important that, when they aren’t advertised in bold type, they swim through the background of graduate and undergraduate textbooks.  Reading a text like Marx’s Capital is of highly dubious value.  True, fewer of Marx’s ideas are present in modern texbooks compared with Smith and Co. and so students are exposed to these ideas less frequently.  There is a good reason for this however:  many of Marx’s ideas simply don’t have much currency in modern economics.

I could of course be completely wrong.  I recall Christy Romer saying that her decision to spend one of her summers in graduate school reading Friedman and Schwarz’s A Monetary History of the United States was one of the best decisions she ever made.  In a course on the history of thought, or on the rhetoric of economics, I would expect the students to read selections of the originals.  And, of course, there are ideas in the earlier works that are poorly understood, underappreciated or simply forgotten.  Remember though, there is a probably a reason these ideas were not incorporated into the contemporary narrative of economics.

But for a graduate student today, the best way to get up to speed is by reading standard modern textbooks.  David Romer has already gathered together what he thinks of as the essential components of modern macroeconomics.  You will learn more and learn faster by reading Romer’s Advanced Macroeconomics than you will by reading the General Theory.