What’s Wrong With Modern Macro? Part 8 Rational Expectations Aren't so Rational

Part 8 in a series of posts on modern macroeconomics. This post continues the criticism of the underlying assumptions of most macroeconomic models. It focuses on the assumption of rational expectations, which was described briefly in Part 2. This post will summarize many of the points I made in a longer survey paper I wrote about expectations in macroeconomics that can be found here.

What economists imagine to be rational forecasting would be considered obviously irrational by anyone in the real world who is minimally rational
Roman Frydman (2013) – Rethinking Economics p. 148

Why Rational Expectations?

Although expectations play a large role in many explanations of business cycles, inflation, and other macroeconomic data, modeling expectations has always been a challenge. Early models of expectations relied on backward looking adaptive expectations. In other words, people form their expectations by looking at past trends and extrapolating them forward. Such a process might seem plausible, but there is a substantial problem with using a purely adaptive formulation of expectations in an economic model.

For example, consider a firm that needs to choose how much of a good to produce before it learns the price. If it expects the price to be high, it will want to produce a lot, and vice versa. If we assume firms expect today’s price to be the same as tomorrow’s, they will consistently be wrong. When the price is low, they expect a low price and produce a small amount. But the low supply leads to a high price in equilibrium. A smart firm would see their errors and revise their expectations in order to profit. As Muth argued in his original defense of rational expectations, “if the prediction of the theory were substantially better than the expectations of the firms, then there would be opportunities for ‘the insider’ to profit from the knowledge.” In equilibrium, these kinds of profit opportunities would be eliminated by intelligent entrepreneurs.

The solution proposed by Muth and popularized in macro by Lucas, was to simply assume that agents had the same model of the economy as the economist. Under rational expectations, an agent does not need to look at past data to make a forecast. Instead, their expectations are model based and forward looking. If an economist can detect consistent errors in an agent’s forecasting, rational expectations assumes that the agents themselves can also detect these errors and correct them.

What Does the Data Tell Us?

The intuitive defense of rational expectations is appealing and certainly rational expectations marked an improvement over previous methods. But if previous models of expectations gave agents too little cognitive ability, rational expectations models give them far too much. Rational expectations leaves little room for disagreement between agents. Each one needs to instantly jump to the “correct” model of the economy (which happens to correspond to the one created by the economist) and assume every other agent has made the exact same jump. As Thomas Sargent put it, rational expectations leads to a “communism of models. All agents inside the model, the econometrician, and God share the same model.”

The problem, as Mankiw et al note, is that “the data easily reject this assumption. Anyone who has looked at survey data on expectations, either those of the general public or those of professional forecasters, can attest to the fact that disagreement is substantial.” Branch (2004) and Carroll (2003) offer further evidence that heterogeneous expectations play an important role in forecasting. Another possible explanation for disagreement in forecasts is that agents have access to different information. Even if each agent knew the correct model of the economy, having access to private information could lead to a different predictions. Mordecai Kurz has argued forcefully that disagreement does not stem from private information, but rather different interpretations of the same information.

Experimental evidence also points to heterogeneous expectations. In a series of “learning to forecast” experiments, Cars Hommes and coauthors have shown that when agents are asked to forecast the price of an asset generated by an unknown model, they appear to resort to simple heuristics that often differ substantially from the rational expectations forecast. Surveying the empirical evidence surrounding the assumptions of macroeconomics as a whole, John Duffy concludes “the evidence to date suggests that human subject behavior is often at odds with the standard micro-assumptions of macroeconomic models.”

“As if” Isn’t Enough to Save Rational Expectations

In response to concerns about the assumptions of economics, Milton Friedman offered a powerful defense. He agreed that it was ridiculous to assume that agents make complex calculations when making economic decisions, but claimed that that is not at all an argument against assuming that they made decisions as if they knew this information. Famously, he gave the analogy of an expert billiard player. Nobody would ever believe that the player planned all of his shots using mathematical equations to determine the exact placement, and yet a physicist who assumed he did make those calculations could provide an excellent model of his behavior.

The same logic applies in economics. Agents who make forecasts using incorrect models will be driven out as they are outperformed by those with better models until only the rational expectations forecast remains. Except this argument only works if rational expectations is actually the best forecast. As soon as we admit that people use various models to forecast, there is no guarantee it will be. Even if some agents know the correct model, they cannot predict the path of economic variables unless they are sure that others are also using that model. Using rational expectations might be the best strategy when others are also using it, but if others are using incorrect models, it may be optimal for me to use an incorrect model as well. In game theory terms, rational expectations is a Nash Equilibrium, but not a dominant strategy (Guesnerie 2010).

Still, Friedman’s argument implies that we shouldn’t worry too much about the assumptions underlying our model as long as it provides predictions that help us understand the world. Rational expectations models also fail in this regard. Even after including many ad hoc fixes to standard models like wage and price stickiness, investment adjustment costs, inflation indexing, and additional shocks, DSGE models with rational expectations still provide weak forecasting ability, losing out to simpler reduced form vector autoregressions (more on this in future posts).

Despite these criticisms, we still need an answer to the Lucas Critique. We don’t want agents to ignore information that could help them to improve their forecasts. Rational expectations ensures they do not, but it is far too strong. Weaker assumptions on how agents use the information to learn and improve their forecasting model over time retain many of the desirable properties of rational expectations while dropping some of its less realistic ones. I will return to these formulations in a future post (but read the paper linked in the intro – and here again – if you’re interested).


What Does it Mean to Be Rational?

One of the most basic assumptions that underlies economic analysis is that people are rational. Given a set of possible choices, an individual in an economic model always chooses the one they think will give them the largest net benefit. This assumption has come under attack by many within the profession as well as by outsiders.

Some of these criticisms are easy to reject. For example, it is common for non-economists to make the jump from rationality to selfishness and denounce economics for assuming all humans only care about themselves. Similar criticisms point to economics for being too materialistic when real people actually value intangible goods as well. Neither of these arguments hits the mark. When an economist talks of rationality, they mean it in the broadest possible way. If donating to charity or giving gifts or going to poor countries to build houses are important for you, economics does not say you are wrong to choose those things. Morality or duty can have just as powerful an impact on your choices as material desires. Economics makes no distinction between these categories. You choose what you prefer. The reason you prefer it is irrelevant for an economist.

More thoughtful criticisms argue that even after defining preferences correctly, people sometimes make decisions that actively work against their own interests. The study of these kinds of issues with decision-making  has come to be called “behavioral economics.” One of the most prominent behavioral economists, Richard Thaler, likes to use the example of the Herzfeld Caribbean Basin Fund. Despite its NASDAQ ticker CUBA, the fund has little to do with the country Cuba. Nevertheless, when Obama announced an easing of Cuban trade restrictions in 2014, the stock price increased 70%. A more recent example occurred when investors swarmed to Nintendo stock after the release of Pokemon Go before realizing that Nintendo doesn’t even make the game.

But do these examples show that people are irrational? I don’t think so. There needs to be a distinction between being rational and being right. Making mistakes doesn’t mean that your original goal wasn’t to maximize your own utility, it just means that the path you chose was not the best way to accomplish that goal. To quote Ludwig von Mises, “human action is necessarily always rational” because “the ultimate end of action is always the satisfaction of some desires” (Human Action, p. 18). He gives the example of doctors 100 years ago trying to treat cancer. Although the methods used at that time may appear irrational by modern standards, those methods were what the doctors considered to be the best given the knowledge available at the time. Mises even applies the same logic to emotional actions. Rather than call emotionally charged actions irrational, Mises instead chooses to argue that “emotions disarrange valuations”  (p. 16). Again, an economist cannot differentiate between the reasons an individual chooses to act. Anger is as justifiable as deliberate calculation.

That is not to say that the behavioralists don’t have a point. They just chose the wrong word. People are not irrational. They just happen to be wrong quite often. The confusion is understandable, however, because rationality itself is not well defined. Under my definition, rationality only requires that agents make the best decision possible given their current knowledge. Often economic models go a step further and make specific (and sometimes very strong) assumptions about what that knowledge should include. Even in models with uncertainty, people are usually expected to know the probabilities of events occurring. In reality, people often face not only unknown events, but unknowable events that could never be predicted beforehand. In such a world, there is no reason to expect that even the most intelligent rational agent could make a decision that appears rational in a framework with complete knowledge.

Roman Frydman describes this point nicely

After uncovering massive evidence that contemporary economics’ standard of rationality fails to capture adequately how individuals actually make decisions, the only sensible conclusion to draw was that this standard was utterly wrong. Instead, behavioral economists concluded that individuals are less than fully rational or are irrational
Rethinking Expectations: The Way Forward For Macroeconomics, p. 148-149

Don’t throw out rationality. Throw out the strong knowledge assumptions. Perhaps the worst of these is the assumption of “rational expectations” in macroeconomics. I will have a post soon arguing that they are not really rational at all.