Robust Hausman Pretest for Panel Data Model in the Presence of Heteroscedasticity and Influential Observations
DOI: https://doi.org/10.33003/jobasr-2023-v1i1-19
Muhammad Sani.
Shamsuddeen Suleiman.
Mustapha Isyaku.
Abstract
The classical Hausman pretest (HT) is used to specified the right model between
random and fixed effect panel data models. However, in the presence of
heteroscedastic error variances and influential observations (IOs) in the data set,
it may not correctly identify the right model. Therefore, this motivated us to
proposed a new method termed Robust Hausman Test (RHTFIID) which employed
residuals from weighted least square (WLS) instead of OLS in the construction of
heteroscedasticity consistent covariance matrix (HCCM) estimator. The
weighting method is based on an efficient High Leverage Points (HLPs) detection
method called Fast Improvised Influential Distance (FIID) which down weight
only vertical outliers and bad HLPs. The good HLPs were allowed in the
estimation as they might contribute to the precision of the estimate. The result
indicates that the new proposed RHTFIID outperformed the existing classical
Hausman pretest by identifying the right model with and without
heteroscedasticity and influential observations.
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