  The presence of outlying observations may adversely affect statistical
testing procedures that result in unstable test statistics and unreliable
inferences depending on the distortion in parameter estimates. In spite of the
fact that the adverse effects of outliers in panel data models, there are only
a few robust testing procedures available for model specification. In this
paper, a new weighted likelihood based robust specification test is proposed to
determine the appropriate approach in panel data including individual-specific
components. The proposed test has been shown to have the same asymptotic
distribution as that of most commonly used Hausman's specification test under
null hypothesis of random effects specification. The finite sample properties
of the robust testing procedure are illustrated by means of Monte Carlo
simulations and an economic-growth data from the member countries of the
Organisation for Economic Co-operation and Development. Our records reveal that
the robust specification test exhibit improved performance in terms of size and
power of the test in the presence of contamination.

Methodology;Econometrics