Estimation and inference in unstable nonlinear least squares models

Otilia Boldea and Alastair Hall

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper, we extend Bai and Perron's (1998, Econometrica, pp. 47-78) method for detecting multiple breaks to nonlinear models. To that end, we consider a nonlinear model that can be estimated via nonlinear least squares (NLS) and features a limited number of parameter shifts occurring at unknown dates. In our framework, the break-dates are estimated simultaneously with the parameters via minimization of the residual sum of squares. Using new uniform convergence results for partial sums, we derive the asymptotic distributions of both break-point and parameter estimates and propose several instability tests. We provide simulations that indicate good finite sample properties of our procedure. Additionally, we use our methods to test for misspecification of smooth-transition models in the context of an asymmetric US federal funds rate reaction function and conclude that there is strong evidence of sudden change as well as smooth behavior.

Keywords: Multiple Change Points; Nonlinear Least Squares; Smooth Transition (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 C32 (search for similar items in EconPapers)
Date: 2010-05-20
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Estimation and inference in unstable nonlinear least squares models (2013)
Working Paper: Estimation and Inference in Unstable Nonlinear Least Squares Models (2012)
Working Paper: Estimation and Inference in Unstable Nonlinear Least Squares Models (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:23150

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