Empirical Macroeconomics and DSGE Modeling in Statistical Perspective

TL;DR

This paper critically evaluates DSGE models as statistical tools, revealing their poor estimation performance and limited structural interpretability through simulation and data swapping tests.

Contribution

It introduces a statistical perspective to validate DSGE models, demonstrating their limitations and proposing methods for macroeconomic model validation.

Findings

Poor estimation even with extensive data

Model fit remains similar or improves with random data swaps

Questions the structural interpretability of DSGE parameters

Abstract

Dynamic stochastic general equilibrium (DSGE) models have been an ubiquitous, and controversial, part of macroeconomics for decades. In this paper, we approach DSGEs purely as statstical models. We do this by applying two common model validation checks to the canonical Smets and Wouters 2007 DSGE: (1) we simulate the model and see how well it can be estimated from its own simulation output, and (2) we see how well it can seem to fit nonsense data. We find that (1) even with centuries' worth of data, the model remains poorly estimated, and (2) when we swap series at random, so that (e.g.) what the model gets as the inflation rate is really hours worked, what it gets as hours worked is really investment, etc., the fit is often only slightly impaired, and in a large percentage of cases actually improves (even out of sample). Taken together, these findings cast serious doubt on the meaningfulness of parameter estimates for this DSGE, and on whether this specification represents anything structural about the economy. Constructively, our approaches can be used for model validation by anyone working with macroeconomic time series.