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Nonparametric Detection and Estimation of Structural Change

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  • Dennis Kristensen

    ()
    (Columbia University and CREATES)

Abstract

We propose a nonparametric approach to the estimation and testing of structural change in time series regression models. Under the null of a given set of the coefficients being constant, we develop estimators of both the nonparametric and parametric components. Given the estimators under null and alternative, generalized F and Wald tests are developed. The asymptotic distributions of the estimators and test statistics are derived. A simulation study examines the fi?nite-sample performance of the estimators and tests. The techniques are employed in the analysis of structural change in US productivity and the Eurodollar term structure.

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File URL: ftp://ftp.econ.au.dk/creates/rp/11/rp11_13.pdf
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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-13.

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Length: 60
Date of creation: 18 Apr 2011
Date of revision:
Handle: RePEc:aah:create:2011-13

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: structural change; regression; nonparametric; estimation; testing; generalized likelihood ratio; time-varying; locally stationary.;

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  1. Bai, Jushan, 1999. "Likelihood ratio tests for multiple structural changes," Journal of Econometrics, Elsevier, vol. 91(2), pages 299-323, August.
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Cited by:
  1. Dennis Kristensen & Andrew Ang, 2009. "Testing Conditional Factor Models," CREATES Research Papers 2009-09, School of Economics and Management, University of Aarhus.

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