In a bizarre leader article The Economist praises microeconomists for their use of data to better predict people’s behaviour and recommend macroeconomists do the same:
Macroeconomists are puritans, creating theoretical models before testing them against data. The new breed [of microeconomists] ignore the whiteboard, chucking numbers together and letting computers spot the patterns. And macroeconomists should get out more. The success of micro is its magpie approach, stealing ideas from psychology to artificial intelligence. If macroeconomists mimic some of this they might get some cool back. They might even make better predictions.
I’m tempted to label this as obvious baiting but the misunderstanding is deeper than that. The newspaper appears to be suggesting that the way forward for better macroeconomic forecasts is to replace theory with data mining. Economists well remember when they last thought that empirical models and relationships could be used to improve forecasts and set policy. The heady days of the 1960s saw economists attempting to fine-tune the economy using empirical relationships such as the Phillips curve. As the empirical relationship disintegrated in the 1970s the developed world fell into a disastrous period of stagflation; a situation not anticipated by the empirical models in use.
Enter our heroes: Milton Friedman, Robert Lucas, Finn Kydland and Ed Prescott. These intrepid macroeconomists convincingly demonstrated that nearly any empirical model would fail to predict the outcome of policy changes. The core problem is that data-driven predictive models incorporate a myriad of implicit assumptions about the relationships and interactions between people in the economy. Policy changes alter those relationships and the models then become very poor predictors. That insight ultimately led to the development of micro-founded models such as the New-Keynesian DSGE models used by most central banks today.
Anyone who has worked with general equilibrium models will know that they are immensely data-hungry and require vast amounts of the stuff to produce simple predictions. But they do so in a fashion that is theoretically structured to avoid the problems of the 1960s. Better data complements better theory, it is not a substitute. The Economist’s misguided recommendation would throw out some of the greatest advances in policy-making of the past half century. Economists must resist the lure of Big Data mining and ensure that theoretical innovation keeps up with the explosion in available data.