A NEW UNBIASED ESTIMATOR OF A MULITPLE LINEAR REGRESSION MODEL OF THE CAPM IN CASE OF MULTICOLLINEARITY
Abstract
In this work we propose an unbiased estimator for a multiple linear regression model of the CAPM in the presence of multicollinearity in the explanatory variables. Multicollinearity is a common problem in empirical Econometrics. The existing methods so far do not deal with cases of perfect multicollinearity. This new optimization method that belongs to the class of unbiased estimators is suitable for cases with strong or perfect multicollinearity, imposes restrictions of the minimizing matrix and produces small standard errors for the estimated parameters. At first we present the theoretical background of our approach and next we derive an expression for the covariance matrix of estimated coeffcients. As an example we estimate the basic linear regression model on Apple Inc expected stock returns and we examine multivariate extensions of this model in the special case of multicollinearity using the proposed method.
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DOI: https://doi.org/10.22190/FUMI210406074P
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