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Non‐parametric detection and estimation of structural change

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  • Dennis Kristensen

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: http://hdl.handle.net/10.1111/j.1368-423X.2012.00378.x
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Bibliographic Info

Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 15 (2012)
Issue (Month): 3 (October)
Pages: 420-461

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Handle: RePEc:wly:emjrnl:v:15:y:2012:i:3:p:420-461

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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. Ang, Andrew & Kristensen, Dennis, 2012. "Testing conditional factor models," Journal of Financial Economics, Elsevier, vol. 106(1), pages 132-156.

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