Robust Two-Stage Least Squares: some Monte Carlo experiments
AbstractThe Two-Stage Least Squares (2-SLS) is a well known econometric technique used to estimate the parameters of a multi-equation (or simultaneous equations) econometric model when errors across the equations are not correlated and the equation(s) concerned is (are) over-identified or exactly identified. However, in presence of outliers in the data matrix, the classical 2-SLS has a very poor performance. In this study a method has been proposed to conveniently generalize the 2-SLS to the weighted 2-SLS (W2-SLS), which is robust to the effects of outliers and perturbations in the data matrix. 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, but it is sensitive to the number of outliers/perturbations in the data matrix. The breakdown point of the method is quite high, somewhere between 45 to 50 percent of the number of points in the data matrix.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 9737.
Date of creation: 26 Jul 2008
Date of revision:
Two-Stage Least Squares; multi-equation econometric model; simultaneous equations; outliers; robust; weighted least squares; Monte Carlo experiments; unbiasedness; efficiency; breakdown point; perturbation; structural parameters; reduced form;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
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- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-08-06 (All new papers)
- NEP-ECM-2008-08-06 (Econometrics)
- NEP-ORE-2008-08-06 (Operations Research)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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,
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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