Robust Estimation of Multiple Regression Model with Non-normal Error: Symmetric Distribution
AbstractIn this paper, we develop the modified maximum likelihood (MML) estimators for the multiple regression coefficients in linear model with the underlying distribution assumed to be symmetric, one of Student's t family. We obtain the closed form of the estimators and derive their asymptotic properties. In addition, we demonstrate that the MML estimators are more appropriate to estimate the parameters in the Capital Asset Pricing Model by comparing its performance with that of least squares estimators (LSE) on the monthly returns of US portfolios. Our empirical study reveals that the MML estimators are more efficient than the LSE in terms of relative efficiency of one-step-ahead forecast mean square error for small samples.
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Bibliographic InfoPaper provided by Monash University, Department of Economics in its series Monash Economics Working Papers with number 09/05.
Length: 18 pages
Date of creation: 01 Jun 2005
Date of revision:
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Postal: Department of Economics, Monash University, Victoria 3800, Australia
Web page: http://www.buseco.monash.edu.au/eco/
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Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
- G1 - Financial Economics - - General Financial Markets
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