Mishra, SK (2008): Robust TwoStage Least Squares: some Monte Carlo experiments.

PDF
MPRA_paper_9737.pdf Download (310kB)  Preview 
Abstract
The TwoStage Least Squares (2SLS) is a well known econometric technique used to estimate the parameters of a multiequation (or simultaneous equations) econometric model when errors across the equations are not correlated and the equation(s) concerned is (are) overidentified or exactly identified. However, in presence of outliers in the data matrix, the classical 2SLS has a very poor performance. In this study a method has been proposed to conveniently generalize the 2SLS to the weighted 2SLS (W2SLS), 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.
Item Type:  MPRA Paper 

Original Title:  Robust TwoStage Least Squares: some Monte Carlo experiments 
Language:  English 
Keywords:  TwoStage Least Squares; multiequation econometric model; simultaneous equations; outliers; robust; weighted least squares; Monte Carlo experiments; unbiasedness; efficiency; breakdown point; perturbation; structural parameters; reduced form 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C8  Data Collection and Data Estimation Methodology ; Computer Programs > C87  Econometric Software C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C30  General 
Item ID:  9737 
Depositing User:  Sudhanshu Kumar Mishra 
Date Deposited:  29 Jul 2008 07:23 
Last Modified:  28 Sep 2019 01:16 
References:  Campbell, N. A. (1980) “Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation”, Applied Statistics, 29 (3): 231237 Hampel, F. R., Ronchetti, E.M., Rousseeuw, P.J. and W. A. Stahel, W.A. (1986) Robust Statistics: The Approach Based on Influence Functions, Wiley, New York. Mishra, S. K. (2008) “A New Method of Robust Linear Regression Analysis: Some Monte Carlo Experiments”, Working Paper Series, SSRN at http://ssrn.com/abstract=1155135 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/9737 