Abstract
In this paper, we present an adaptive estimator for panel data model with unknown unit-time varying heteroscedastic error component of unknown form by using probability weighted moments rather than conventional kernel estimators already available in the literature and then evaluate the finite sample performance of the proposed estimator in terms of efficiency and testing of hypothesis. The Monte Carlo evidence suggests that the proposed estimator performs adequately under different data generated processes, especially for small samples that are the most practical situations.