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Heteroscedasticity is a stern problem that distorts estimation and testing of panel data model (PDM). Arellano (1987) proposed the White (1980) estimator for PDM with heteroscedastic errors but it provides erroneous inference for the data sets including high leverage points. In this paper, our attempt is to improve heteroscedastic consistent covariance matrix estimator (HCCME) for panel dataset with high leverage points. To draw robust inference for the PDM, our focus is to improve kernel bootstrap estimators, proposed by Racine and MacKinnon (2007). The Monte Carlo scheme is used for assertion of the results.
Bootstrap HCCME Kernel smoothing Leverage points Size distortion
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How to Cite
Saeed, A., & Asadi, D. M. A. (2016). Improved Inference of Heteroscedastic Fixed Effects Models. Pakistan Journal of Statistics and Operation Research, 12(4), 589-608. https://doi.org/10.18187/pjsor.v12i4.1441