Nonparametric Detection and Estimation of Structural Change
AbstractWe 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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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-13.
Date of creation: 18 Apr 2011
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Web page: http://www.econ.au.dk/afn/
structural change; regression; nonparametric; estimation; testing; generalized likelihood ratio; time-varying; locally stationary.;
Other versions of this item:
- Dennis Kristensen, 2012. "Non‐parametric detection and estimation of structural change," Econometrics Journal, Royal Economic Society, vol. 15(3), pages 420-461, October.
- 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
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- Bai, Jushan, 1999. "Likelihood ratio tests for multiple structural changes," Journal of Econometrics, Elsevier, vol. 91(2), pages 299-323, August.
- Andrew Ang & Dennis Kristensen, 2011.
"Testing Conditional Factor Models,"
NBER Working Papers
17561, National Bureau of Economic Research, Inc.
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