Publication detail

Víšek, J. Á. : Estimating the Model with Fixed and Random Effects by a Robust Method

Author(s): prof. RNDr. Jan Ámos Víšek CSc.,
Type: Articles in journals with impact factor
Year: 2015
Number: 17
ISSN / ISBN: 1387-5841
Published in: Methodology and Computing in Applied Probability 17, 999 - 1014.
Publishing place: New York
Keywords: Linear regression model · The least weighted squares · Fixed and random effects · Numerical simulations
JEL codes: 62J02 · 62F35
Suggested Citation:
Grants: Dynamic Models in Economics – DYME, Grant Agency of the Czech Republic, Grant no. P402/12/G097, member of the team
Abstract: Regression model with fixed and random effects estimated by modified versions
of the Ordinary Least Squares (OLS) is a standard tool of panel data analysis. However,
it is vulnerable to the bad effects of influential observations (contamination and/or atypical
observations). The paper offers robustified versions of the classical methods for this
framework. The robustification is carried out by the same idea which was employed when
robustifying OLS, it is the idea of weighting down the large order statistics of squared residuals.
In contrast to the approach based on the M-estimators this approach does not need the
studentization of residuals to reach the scale- and regression-equivariance of estimator in
question. Moreover, such approach is not vulnerable with respect the inliers. The numerical
study reveals the reliability of the respective algorithm. The results of this study were collected
in a file which is possible to find on web, address is given below. Patterns of these
results were included also into the paper. The possibility to reach nearly the full efficiency
of estimation - due to the iteratively tailored weight function - in the case when there are no
influential points is also demonstrated.




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