Estimation and Inference in Unstable Models Estimated via Nonlinear Least Squares

• Activity Number: 364
• Type: Contributed
• Date/Time: Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economics Statistics Section
• Author(s): Otilia Boldea*+ and Alastair Hall
• Companies: Tilburg University and University of Manchester/North Carolina State University
• Keywords: Structural Change ; Multiple Break Points ; Nonlinear Least Squares ; Nonlinear Asymptotics ; Empirical Processes

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 an unstable univariate nonlinear least squares (NLS) model with a limited number of parameter shifts occurring at unknown dates. In our framework, the break-dates are simultaneously estimated with the parameters via minimization of the residual sum of squares. Using nonlinear asymptotic theory, we derive the asymptotic distributions of both break-point and parameter estimates and propose several instability tests. We also present simulation results that validate our procedure. Our method is useful for estimating and testing nonlinear macroeconomic models with multiple unknown breaks. By construction, it also removes the usual confusion between nonlinearities and breaks.