Equilibrium Heterogeneous-agent Models As Measurement Tools: Some Monte Carlo Evidence

This paper discusses a series of Monte Carlo experiments designed to evaluate the empirical properties of heterogeneous-agent macroeconomic models in the presence of sampling variability. The calibration procedure leads to the welfare analysis being conducted with the wrong parameters. The ability of the calibrated model to correctly predict the long-run welfare changes induced by a set of policy experiments is assessed. The results show that, for the policy reforms with sizable welfare effects (i.e., more than 0.2%), the model always predict the right sign of the welfare effects. However, the welfare effects can be evaluated with the wrong sign, when they are small and when the sample size is fairly limited. Quantitatively, the maximum errors made in evaluating a policy change are very small for some reforms (in the order of 0.02 percentage points), but bigger for others (in the order of 0.6 p.p.). Finally, having access to better data, in terms of larger samples, does lead to substantial increases in the precision of the welfare effects estimates, though the rate of convergence can be slow.