Non-Robust Dynamic Inferences from Macroeconometric Models: Bifurcation Stratification of Confidence Regions

William Barnett and Evgeniya A. Duzhak

MPRA Paper from University Library of Munich, Germany

Abstract: Grandmont (1985) found that the parameter space of the most classical dynamic models are stratified into an infinite number of subsets supporting an infinite number of different kinds of dynamics, from monotonic stability at one extreme to chaos at the other extreme, and with many forms of multiperiodic dynamics between. The econometric implications of Grandmont’s findings are particularly important, if bifurcation boundaries cross the confidence regions surrounding parameter estimates in policy-relevant models. Stratification of a confidence region into bifurcated subsets seriously damages robustness of dynamical inferences. Recently, interest in policy in some circles has moved to New Keynesian models. As a result, in this paper we explore bifurcation within the class of New Keynesian models. We develop the econometric theory needed to locate bifurcation boundaries in log-linearized New-Keynesian models with Taylor policy rules or inflation-targeting policy rules. Central results needed in this research are our theorems on the existence and location of Hopf bifurcation boundaries in each of the cases that we consider.

Keywords: Bifurcation; Hopf bifurcation; Euler equations; New Keynesian macroeconometrics; Bergstrom-Wymer model (search for similar items in EconPapers)
JEL-codes: C3 C5 E3 (search for similar items in EconPapers)
Date: 2007-11-27
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-mac and nep-mon
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Related works:
Journal Article: Non-robust dynamic inferences from macroeconometric models: Bifurcation stratification of confidence regions (2008)
Working Paper: Non-Robust Dynamic Inferences from Macroeconometric Models: Bifurcation Stratification of Confidence Regions (2006)
Working Paper: Non-Robust Dynamic Inferences from Macroeconometric Models: Bifurcation Stratification of Confidence Regions (2006)
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