Open AccessMODELING MODEL UNCERTAINTY
Author(s) -
Onatski Alexei,
Williams Noah
Publication year - 2003
Publication title -
journal of the european economic association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.792
H-Index - 93
eISSN - 1542-4774
pISSN - 1542-4766
DOI - 10.1162/154247603770383406
Subject(s) - econometrics , nonparametric statistics , sensitivity analysis , parametric statistics , monetary policy , minimax , economics , bayesian probability , point (geometry) , point estimation , lag , computer science , uncertainty analysis , macroeconomics , microeconomics , statistics , mathematics , computer network , geometry , artificial intelligence , simulation
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 U.S. 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. (JEL: E52, C32, D81)