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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 leastsquares 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 ofsquares. 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 indicategood finite sample properties of our procedure. Additionally, we use our methods to test for misspecification of smooth-transition models in the contextof an asymmetric US federal funds rate reaction function and conclude that there is strong evidence of sudden change as well as smooth behavior.



Item Type: MPRA Paper -

Original Title: Estimation and inference in unstable nonlinear least squares models-

Language: English-

Keywords: Multiple Change Points, Nonlinear Least Squares, Smooth Transition-

Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space ModelsC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: GeneralC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: GeneralC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes-





Autor: Boldea, Otilia

Fuente: https://mpra.ub.uni-muenchen.de/23150/







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