Testing for heteroskedasticity and spatial correlation in a two way random effects model

Kouassi, Eugène; Mougoué, Mbodja; Sango, Joel; Brou, Jean Marcelin Bosson; Amba, Claude M. O.; Salisu, Afees A.

doi:10.1016/j.csda.2013.09.003

Cited by 2 publications

(1 citation statement)

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“…Thus, the OLS estimator is not efficient relative to other linear and unbiased estimators under such situations. A number of works on the methodologies and applications of panel data modelling have appeared in the literature ( [10], [22], [23], [24] and [29]). …”

Section: Review Of Some Related Studiesmentioning

confidence: 99%

Exploration Analysis of Some Panel Data Estimators in the Presence of One-Sided Exponential Heteroscedasticity Structure

Joshua¹

2017

AJTAS

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This paper aimed at assessing the performance of some estimators in the presence of one-sided exponential heteroscedasticity structure in panel model estimation. This study employs Monte Carlo experiments to evaluate the performances. It focuses on random effects models with 150 and 300 as cross-sectional units (N) and 10 and 20 as time periods (T) with Absolute Bias (ABIAS) and Root Mean Squared Error (RMSE) were criterion for assessing the performances of the estimators. The estimators were then ordered according to their performances. Generally, the performance improved as the combinations of N and T increased in experiments. The ranking of the eight estimators for the experiment are in the order: PGLS (95%), SWAR (69%), NER (64%), WG (45%), AM (43%), WALHUS (37%), BG (36%) and POLS (28%). Panel generalised least squares estimator (PGLS) outperformed other estimators in the presence of OEHS, using POLS as a known benchmark to gauge the performance and the work will help in the choice of estimators when faced with empirical datasets that exhibit exponential heteroscedasticity.

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Section: Review Of Some Related Studiesmentioning

confidence: 99%