An analysis of the indicator saturation estimator as a robust regression estimator
AbstractAn 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.
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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 2008-WO3.
Date of creation: 01 Jan 2008
Date of revision:
Empirical Processes; Huber's Skip; Indicator Saturation; M-Estimator; Outlier Robustness; Vector Autoregressive Process;
Other versions of this item:
- Søren Johansen & Bent Nielsen, 2008. "An analysis of the indicator saturation estimator as a robust regression estimator," Economics Papers 2008-W03, Economics Group, Nuffield College, University of Oxford.
- Søren Johansen & Bent Nielsen, 2008. "An analysis of the indicator saturation estimator as a robust regression estimator," CREATES Research Papers 2008-09, School of Economics and Management, University of Aarhus.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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2003-W23, Economics Group, Nuffield College, University of Oxford.
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- J. James Reade & Ulrich Volz, 2011. "From the General to the Specific," Discussion Papers 11-18, Department of Economics, University of Birmingham.
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