Robust Twoï¿½Stage Least Squares: Some Monte Carlo Experiments
The Twoï¿½Stage Least Squares (2ï¿½SLS) is a well known econometric technique used to estimate the parameters of a multiï¿½equation econometric model when errors across the equations are not correlated and the equation(s) concerned is (are) overï¿½identified or exactly identified. However, in presence of outliers in the data matrix, the classical 2ï¿½SLS has a very poor performance. In this study a method has been proposed to generalize the 2ï¿½SLS to the Weighted Twoï¿½Stage Least Squares (W2ï¿½SLS), which is robust to the effects of outliers and perturbations. Monte Carlo experiments have been conducted to demonstrate the performance of the proposed method. It has been found that robustness of the proposed method is not much destabilized by the magnitude of outliers. The breakdown point of the method is quite high, somewhere between 45 to 50 percent of the number of points in the data matrix.
Volume (Year): 3 (2008)
Issue (Month): 4(6)_Winter2008 ()
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- Sudhanshu Kumar MISHRA, 2008.
"A New Method Of Robust Linear Regression Analysis: Some Monte Carlo Experiments,"
Journal of Applied Economic Sciences,
Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(3(5)_Fall), pages 261-268.
- Mishra, SK, 2008. "A new method of robust linear regression analysis: some monte carlo experiments," MPRA Paper 9445, University Library of Munich, Germany.
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