Abstract
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.
Highlights
Panel data and longitudinal models have become progressively widespread among applied researchers due to their heightened capacity for capturing the complexity of human behaviour unlike cross-sectional or time series data models [1]
This study employs Monte Carlo experiments to evaluate the performances of some panel data estimators in the presence of one-sided exponential heteroscedasticity structure (OEHS) of oneway error component model (ECM). It focuses on random effects models (REM) with 150 and 300 as cross-sectional units (N) and 5 and 10 as time periods (T)
It was observed that Panel generalised least squares estimator (PGLS) consistently outperformed all other estimators when considering the Absolute Bias (ABIAS) and, when the Root Mean Squared Error (RMSE)
Summary
Panel data and longitudinal models have become progressively widespread among applied researchers due to their heightened capacity for capturing the complexity of human behaviour unlike cross-sectional or time series data models [1]. Despite these advantages, panel data are subject to their own experimental problems. Prominent among the problems constantly addressed in panel data econometrics are selectivity and heterogeneity biases. Assuming homoscedastic disturbances when heteroscedasticity is present will still result in unbiased and consistent estimates of the regression coefficients, these estimates will not be efficient [2]. When one begins to look at a crosssection of regions, states, countries, etc., these aggregate units may exhibit a cross-sectional correlation that has to be dealt with [3]
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