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,"
2003-W23, Economics Group, Nuffield College, University of Oxford.
- 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 Series Working Papers 2003-W23, University of Oxford, Department of Economics.
- Roger Koenker & Kevin F. Hallock, 2001.
Journal of Economic Perspectives,
American Economic Association, vol. 15(4), pages 143-156, Fall.
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