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Estimation and Inference in Unstable Nonlinear Least Squares Models

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  • Otilia Boldea
  • Alastair R. Hall

Abstract

There is compelling evidence that many macroeconomic and financial variables are not generated by linear models. This evidence is based on testing linearity against either smooth nonlinearity or piece-wise linearity, but there is no framework that encompasses both. This paper provides an econometric framework that allows for both breaks and smooth nonlinearity in-between breaks. We estimate the unknown break-dates simultaneously with other parameters via nonlinear least-squares. Using new central limit results for nonlinear processes, we provide inference methods on break-dates and parameter estimates and several instability tests. We illustrate our methods via simulated and empirical smooth transition models with breaks.

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Paper provided by Economics, The Univeristy of Manchester in its series Centre for Growth and Business Cycle Research Discussion Paper Series with number 174.

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Length: 35 pages
Date of creation: 2012
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Handle: RePEc:man:cgbcrp:174

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Cited by:
  1. Otilia Boldea & Alastair R. Hall, 2012. "Estimation and Inference in Unstable Nonlinear Least Squares Models," Centre for Growth and Business Cycle Research Discussion Paper Series 174, Economics, The Univeristy of Manchester.

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