Improved Inference of Heteroscedastic Fixed Effects Models

Afshan Saeed, Dr. Muhammad Aslam Asadi

Abstract


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.

Keywords


Bootstrap; HCCME; Kernel smoothing; Leverage points; Size distortion

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DOI: http://dx.doi.org/10.18187/pjsor.v12i4.1441

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Title

Improved Inference of Heteroscedastic Fixed Effects Models

Keywords

Bootstrap; HCCME; Kernel smoothing; Leverage points; Size distortion

Description

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.

Date

2016-12-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 12 No. 4, 2016



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810