An analysis of the indicator saturation estimator as a robust regression estimator
An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, is analyzed with the purpose of finding an estimator that is robust to outliers and structural breaks.� This estimator is an example of a one-step M-estimator based on Huber's skip function.� The asymptotic theory is derived in the situation where there are no outliers or structural breaks using empirical process techniques.� Stationary processes, trend stationary autoregressions and unit root processes are considered.
|Date of creation:||01 Jan 2008|
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- Bent Nielsen, 2003.
"Strong consistency results for least squares estimators in general vector autoregressions with deterministic terms,"
Economics Series Working Papers
2003-W23, University of Oxford, Department of Economics.
- Nielsen, Bent, 2005. "Strong Consistency Results For Least Squares Estimators In General Vector Autoregressions With Deterministic Terms," Econometric Theory, Cambridge University Press, vol. 21(03), pages 534-561, June.
- Bent Nielsen, 2003. "Strong consistency results for least squares estimators in general vector autoregressions with deterministic terms," Economics Papers 2003-W23, Economics Group, Nuffield College, University of Oxford.
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