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?