I’ve spent 14 posts telling you what’s wrong with modern macro. It’s about time for something positive. Here I hope to give a brief outline of what my ideal future of macro would look like. I will look at four current areas of research in macroeconomics outside the mainstream (some more developed than others) that I think offer a better way to do research than currently accepted methods. I will expand upon each of these in later posts.
Learning and Heterogeneous Expectations
Let’s start with the smallest deviation from current research. In Part 8 I argued that assuming rational expectations, which means that agents in the model form expectations based on a correct understanding of the environment they live in, is far too strong an assumption. To deal with that criticism, we don’t even need to leave the world of DSGE. A number of macroeconomists have explored models where agents are required to learn about how important macroeconomic variables move over time.
These kinds of models generally come in two flavors. First, the econometric learning models summarized in Evans and Honkapohja’s 2001 book, Learning and Expectations in Macroeconomics, which assume that agents in the model are no smarter than the economists that create them. They must therefore use the same econometric techniques to estimate parameters that economists do. Another approach assumes even less about the intelligence of agents by only allowing them to use simple heuristics for prediction. Based on the framework of Brock and Hommes (1997), these heuristic switching models allow agents to hold heterogeneous expectations in equilibrium, an outcome that is difficult to achieve with rational expectations, but prevalent in reality. A longer post will look at these types of models in more detail soon.
Experiments
Most macroeconomic research is based on the same set of historical economic variables. There are probably more papers about the history of US macroeconomics than there are data points. Even if we include all of the countries that provide reliable economic data, that doesn’t leave us with a lot of variation to exploit. In physics or chemistry, an experiment can be run hundreds or thousands of times. In economics, we can only observe one run.
One possible solution is to design controlled experiments aimed to answer macroeconomic questions. The obvious objection to such an idea is that a lab with a few dozen people interacting can never hope to capture the complexities of a real economy. That criticism makes sense until you consider that many accepted models only have one agent. Realism has never been the strong point of macroeconomics. Experiments of course won’t be perfect, but are they worse than what we have now? John Duffy gives a nice survey of some of the recent advances in experimental macroeconomics here, which I will discuss in a future post as well.
Agent Based Models
Perhaps the most promising alternative to DSGE macro models, an agent based model (ABM) attempts to simulate an economy from the ground up inside a computer. In particular, an ABM begins with a group of agents that generally follow a set of simple rules. The computer then simulates the economy by letting these agents interact according to the provided rules. Macroeconomic results are obtained by simply adding the outcomes of individuals.
I will give examples of more ABMs in future posts, but one I really like is a 2000 paper by Peter Howitt and Robert Clower. In their paper they begin with a decentralized economy that consists of shops that only trade two commodities each. Under a wide range of assumptions, they show that in most simulations of an economy, one of the commodities will become traded at nearly every shop. In other words, one commodity become money. Even more interesting, agents in the model coordinate to exploit gains from trade without needing the assumption of a Walrasian Auctioneer to clear the market. Their simple framework has since been expanded to a full fledged model of the economy.
Empirical Macroeconomics
If you are familiar with macroeconomic research, it might seem odd that I put empirical macroeconomics as an alternative path forward. It is almost essential for every macroeconomic paper today to have some kind of empirical component. However, the kind of empirical exercises performed in most macroeconomic papers don’t seem very useful to me. They focus on estimating parameters in order to force models that look nothing like reality to nevertheless match key moments in real data. In part 10 I explained why that approach doesn’t make sense to me.
In 1991, Larry Summers wrote a paper called “The Scientific Illusion in Empirical Macroeconomics” where he distinguishes between formal econometric testing of models and more practical econometric work. He argues that economic work like Friedman and Schwartz’s A Monetary History of the United States, despite eschewing formal modeling and using a narrative approach, contributed much more to our understanding of the effects of monetary policy than any theoretical study. Again, I will save a longer discussion for a future post, but I agree that macroeconomic research should embrace practical empirical work rather than its current focus on theory.
The future of macro should be grounded in diversity. DSGE has had a good run. It has captivated a generation of economists with its simple but flexible setup and ability to provide answers to a great variety of economic questions. Perhaps it should remain a prominent pillar in the foundation of macroeconomic research. But it shouldn’t be the only pillar. Questioning the assumptions that lie at the heart of current models – rational expectations, TFP shocks, Walrasian general equilibrium – should be encouraged. Alternative modeling techniques like agent based modeling should not be pushed to the fringes, but welcomed to the forefront of the research frontier.
Macroeconomics is too important to ignore. What causes business cycles? How can we sustain strong economic growth? Why do we see periods of persistent unemployment, or high inflation? Which government or central bank policies will lead to optimal outcomes? I study macroeconomics because I want to help answer these questions. Much of modern macroeconomics seems to find its motivation instead in writing fancy mathematical models. There are other approaches – let’s set them free.
