Thursday, May 4th, 2017
Article By: David McDonald
It is extremely difficult for economists, bankers, and political figureheads to predict a recession due to the sheer volatility of the US and global economy. It kind of puts a damper on my spirits because I’m currently studying economics in university with the hopes of someday even becoming an economist.
Economic theory actually tells us quite little about forecasting the future of the economy: Consumers should cut back their spending if they believe that their earning power will fall for an extended period of time, but if they believe the hard times are temporary—say, a short period out of work—then they should “smooth” by borrowing in hard times and paying back when things pick up. Because of smoothing, consumption should not shrink as much as the economy does.
Furthermore, many speculate that economics is even a real science because of it’s failure to accurately and consistently predict shortcomings in the economy.
In fact, one of the biggest things that economists get grief about is their failure to predict big events like recessions. Even the Queen of England, that most reserved of personages, got in on the game, back in 2008, according to the U.K. Telegraph:
“During a briefing by academics at the London School of Economics on the turmoil on the international markets the Queen asked: “Why did nobody notice it?”…Professor Luis Garicano, director of research at the London School of Economics’ management department, had explained the origins and effects of the credit crisis…Prof Garicano said: “She was asking me if these things were so large how come everyone missed it.“
The queen is correct. Here, courtesy of Institute for Monetary and Financial Stability economist Volker Wieland and Goethe University economist Maik Wolters, is a picture of how badly economists’ models failed to predict the Great Recession:
As you can see, even in the third quarter of 2008, the best models we have missed the big recession entirely.
Economists didn’t just fail to see that monster recession; they routinely fail to see economic events coming.
The best models we have — the ones central banks use, which take graduate-level training in order to handle — have about as much forecasting power as simple, naïve mathematical techniques that any undergraduate statistics major could whip up in a few minutes.
Pointing this out usually leads to the eternal (and eternally fun) debate over whether economics is a real science.
Some say that if you don’t make successful predictions, you aren’t a science. Economists will respond that seismologists can’t forecast earthquakes, and meteorologists can’t forecast hurricanes, and who cares what’s really a “science” anyway.
The debate, however, misses the point. Forecasts aren’t the only kind of predictions a science can make. In fact, they’re not even the most important kind.
Take physics for example. Sometimes physicists do make forecasts — for example, eclipses. But those are the exception. Usually, when you make a new physics theory, you use it to predict some new phenomenon — some kind of thing that no one has seen before, because they haven’t bothered to look. For example, quantum mechanics has gained a lot of support from predicting the strange new things like quantum tunnelling or quantum teleporation.
Other times, a theory will predict things we have seen before, but will describe them in terms of other things that we thought were totally separate, unrelated phenomena. This is called unification, and it’s a key part of what philosophers think science does. For example, the theory of electromagnetism says that light, electric current, magnetism, radio waves are all really the same phenomenon. Pretty neat!
What’s important about these predictions is, first of all, that they’re testable — the evidence isn’t going to give you an ambiguous answer. It’s also important that they’re novel — each theory can predict more than just the phenomena that inspired the theory. As Richard Feynman, the great physicist and amateur philosopher of science, :
“When you have put a lot of ideas together to make an elaborate theory, you want to make sure, when explaining what it fits, that those things it fits are not just the things that gave you the idea for the theory; but that the finished theory makes something else come out right, in addition.“
So that’s physics. What about economics? Actually, econ has a number of these too. When Dan McFadden used his Random Utility Model to predict how many people would ride San Francisco’s Bay Area Rapid Transit system, it was a totally new experiment. And he got it right. And he got many other things right with the same theory — it wasn’t developed to explain only train ridership.
Unfortunately, though, this kind of success isn’t very highly regarded in the economics world — at least, not that I’ve seen. If you manage this kind of success you can start a consultancy, but your fellow academics will think it merely a feather in your cap. The kind of theories that are held in the highest regard are usually not empirically successful ones, but new ones — theories that use new kinds of math, for instance. These are prized because they give a lot of other economists work to do,specifically making variation upon variation of the cool new model.
Above is an economic forecast for the next seven or so years for the United States economy, evaluated at 2009 dollars. I think we can all agree that this forecast is not going to be 100% correct. I personally am skeptical to the accuracy of this estimate due to the looming potential recession the US economy is expected to face by 2020. I guess we’ll have to just wait and see.
When economists do praise a model for its empirical success, it’s usually about how well the model fits the data on the phenomenon the model was created to describe. This, as Feynman pointed out, is a pretty low bar. If that’s the only hurdle models have to clear, you can make one new theory to describe each new phenomenon. If you have a million phenomena, you end up with a million models. The models probably contradict each other, but that doesn’t matter, since each model is only judged on how well it “explains” the thing it was created to describe. Which, at least in the macroeconomics literature, is pretty much what we see.
How did econ get this way? My guess is that it’s because economics didn’t evolve from science — it evolved from literature. Back in the days of Adam Smith and David Ricardo, there was no such thing as economic data — all you had were thought experiments and casual observations. So economics wasn’t able to hold itself to scientific standards of validation in the old days, and it’s been an uphill battle to graft those standards onto the discipline. Not that people haven’t tried — if you read Milton Friedman’s “ ” from 1966, you will find him saying much the same things I’m saying.
Budget forecasting has never been easy, but these days it’s getting even harder. Revenues have become increasingly volatile, and global commerce is ever more intertwined — as China’s market tumble laid bare. “For someone to be a good recession forecaster, they need to predict it more than once,” says Tara Sinclair, chief economist at the jobs site Indeed and a professor at George Washington University. “That’s where we’ve pretty much seen this epic failure, where nobody’s been able to consistently forecast.”
The lag time in accurate data can be a big culprit. State forecasters tend to rely heavily on the jobs growth and employment data released monthly by the U.S. Department of Labor, because job growth is connected to increases in income and sales taxes — two of the biggest revenue raisers for states.
However, the monthly figures are typically revised two or even three months after they are first published. So forecasters don’t really have an accurate picture of March’s economy, for example, until it’s already June. “That makes it really much harder to project those turning points, because you’re always looking in that rearview mirror,” says Juliette Tennert, director of economic and public policy research the University of Utah. Tennert was a forecaster for the state of Utah during the recession and, more recently, the state’s budget director.
This process has real consequences for governments. The foundation of all budgets is the forecast of how much the state will earn that year in revenues. When forecasters miss a downturn, lawmakers have to cut spending mid-year to balance the budget.
So how can economists better predict recessions? Sinclair thinks that more economists should focus solely on predicting major turns in the economy. But there just isn’t much incentive for economists to work only on recessions, which tend to happen about once every six years.
Tennert says forecasters should also track unemployment claims in their state because they provide a better real-time indication of the jobs economy. A big help, she adds, is getting out in the field and talking with a region’s business leaders about what they are seeing in the economy.
State forecasters usually build a small cushion into their revenue forecasts, but budget offices and policymakers also need to develop other cushions — rainy day funds, for example — in case of a downturn. “It would be nice if you got to under-forecast by 10 percent so when you get to a downturn everyone’s OK,” Tennert says. “But when you have these really critical needs at play, you just can’t be in that situation.”
Maybe now, with the ascendance of empirical economics and a decline in theory, we’ll see a focus on producing fewer but better theories, more unification, and more attempts to make novel predictions. Someday, maybe macroeconomists will even be able to make forecasts! At least, I hope so!