Working Paper: NBER ID: w9566
Authors: Alexei Onatski; Noah Williams
Abstract: Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We point out that different assumptions about the uncertainty may result in drastically different robust' policy recommendations. Therefore, we develop new methods to analyze uncertainty about the parameters of a model, the lag specification, the serial correlation of shocks, and the effects of real time data in one coherent structure. We consider both parametric and nonparametric specifications of this structure and use them to estimate the uncertainty in a small model of the US economy. We then use our estimates to compute robust Bayesian and minimax monetary policy rules, which are designed to perform well in the face of uncertainty. Our results suggest that the aggressiveness recently found in robust policy rules is likely to be caused by overemphasizing uncertainty about economic dynamics at low frequencies.
Keywords: Monetary Policy; Model Uncertainty; Bayesian Methods; Minimax Techniques
JEL Codes: E5
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| different assumptions about model uncertainty (C51) | drastically different robust policy recommendations (D78) |
| overemphasizing uncertainty about economic dynamics at low frequencies (E32) | aggressiveness found in robust policy rules (E61) |
| policymakers' fears of low-frequency deviations from the reference model (C54) | more aggressive monetary policies (E63) |
| certain policy rules under nonparametric calibration (C51) | instability (C62) |
| true model's perturbations (C59) | disastrous policy performance (E65) |
| more aggressive policies (E65) | fears of inflation when low-frequency shocks are emphasized (E31) |