This paper estimates a standard version of the New Keynesian monetary (NKM) model under alternative specifications of the monetary policy rule using U.S. and Eurozone data. The estimation procedure implemented is a classical method based on the indirect inference principle. An unrestricted VAR is considered as the auxiliary model. On the one hand, the estimation method proposed overcomes some of the shortcomings of using a structural VAR as the auxiliary model in order to identify the impulse response that defines the minimum distance estimator implemented in the literature. On the other hand, by following a classical approach we can further assess the estimation results found in recent papers that follow a maximum-likelihood Bayesian approach. The estimation results show that some structural parameter estimates are quite sensitive to the specification of monetary policy. Moreover, the estimation results in the U.S. show that the fit of the NKM under an optimal monetary plan is much worse than the fit of the NKM model assuming a forward-looking Taylor rule. We also find, in contrast to the literature, evidence of indeterminacy under the best fitting monetary policy rule under the Greenspan era. In contrast to the U.S. case,in the Eurozone the best fit is obtained assuming a backward-looking Taylor rule and determinacy holds, but the improvement is rather small with respect to assuming either a forward-looking Taylor rule or an optimal plan.

Abraham, Arpad / Carceles-Poveda , Eva / Cavalcanti, Tiago / Kambourov, Gueorgui / Lambertini, Luisa / Ruhl, Kim / Tavares, Jose
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How Does the New Keynesian Monetary Model Fit in the U.S. and the Eurozone? An Indirect Inference Approach
1Universidad de Murcia & InUEFF, ramonmar@um.es
2Universidad del País Vasco, jepvapej@bs.ehu.es
Citation Information: Topics in Macroeconomics. Volume 6, Issue 2, Pages –, ISSN (Online) 1534-5998, DOI: 10.2202/1534-5998.1446, September 2006
Publication History:
- Published Online:
- 2006-09-28
Keywords: NKM model; Taylor rule; indirect inference


















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