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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: ftp://ftp.econ.au.dk/creates/rp/11/rp11_13.pdf
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    References listed on IDEAS

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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. Adrian, Tobias & Crump, Richard K. & Moench, Emanuel, 2015. "Regression-based estimation of dynamic asset pricing models," Journal of Financial Economics, Elsevier, vol. 118(2), pages 211-244.
    2. Ang, Andrew & Kristensen, Dennis, 2012. "Testing conditional factor models," Journal of Financial Economics, Elsevier, vol. 106(1), pages 132-156.
    3. Isabel Casas & Xiuping Mao & Helena Veiga, 0503. "Reexamining financial and economic predictability with new estimators of realized variance and variance risk premium," CREATES Research Papers 2018-10, Department of Economics and Business Economics, Aarhus University.
    4. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
    5. Chen, Bin, 2015. "Modeling and testing smooth structural changes with endogenous regressors," Journal of Econometrics, Elsevier, vol. 185(1), pages 196-215.
    6. Daniel J. Henderson & Christopher F. Parmeter & Liangjun Su, 2017. "M-Estimation of a Nonparametric Threshold Regression Model," Working Papers 2017-15, University of Miami, Department of Economics.
    7. Wu, Jilin, 2016. "Detecting structural changes under nonstationary volatility," Economics Letters, Elsevier, vol. 146(C), pages 151-154.
    8. Yu, Ping & Phillips, Peter C.B., 2018. "Threshold regression with endogeneity," Journal of Econometrics, Elsevier, vol. 203(1), pages 50-68.
    9. repec:eee:econom:v:202:y:2018:i:2:p:245-267 is not listed on IDEAS
    10. Tao, Yubo & Phillips, Peter C.B. & Yu, Jun, 2017. "Random Coefficient Continuous Systems: Testing for Extreme Sample Path Behaviour," Economics and Statistics Working Papers 18-2017, Singapore Management University, School of Economics.
    11. Byrne, Joseph P & Ibrahim, Boulis Maher & Sakemoto, Ryuta, 2017. "The Time-Varying Risk Price of Currency Carry Trades," MPRA Paper 80788, University Library of Munich, Germany.

    More about this item

    Keywords

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

    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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