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Nonparametric Detection and Estimation of Structural Change

Author

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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.

Suggested Citation

  • Dennis Kristensen, 2011. "Nonparametric Detection and Estimation of Structural Change," CREATES Research Papers 2011-13, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2011-13
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    File URL: https://repec.econ.au.dk/repec/creates/rp/11/rp11_13.pdf
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    References listed on IDEAS

    as
    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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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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