In a new paper (pdf) entitled “Where Modern Macroeconomics Went Wrong,” Nobel laureate Joseph Stiglitz of Columbia University lays much of the blame on the models used to understand the economy. These Dynamic Stochastic General Equilibrium (DSGE) models have become increasingly popular among macroeconomists, central bankers, and other analysts. [...]
“The core of the failings of the DSGE model can be traced to the attempt, decades ago, to reconcile macroeconomics with micro-economics,” writes Stiglitz. Here, Stiglitz challenges one of the primary appeals of DSGE models: their “micro foundations.” This means that all models are built up from the decisions of an individual or “representative agent.” These models generally assume that individuals act to maximize their utility “over an infinite lifetime without borrowing constraints,” he writes. [...]
First, the models haven’t been good enough at predicting economic trends, particularly around crises, because they are built to detect short-term fluctuations and not large shocks. Second, they don’t sufficiently incorporate the significant influence of the finance industry, because the models are better at incorporating information about individuals instead of institutions. Third, shocks in DSGE-based systems assume that they are caused by external factors and don’t account for the fact that some crises arise from within. [...]
But instead of scrapping efforts to use micro insights to model the macro economy, researchers at the Bank of England suggest doubling down on a data-heavy approach. In a recent paper (pdf), they also acknowledge the problems with modern economic models. They say that machine learning could address some of these shortfalls by taking advantage of the increasingly large amounts of “micro and high-frequency data” available to central banks and regulators, such as transactions between financial institutions and detailed household consumption patterns.
