Examining the Influence of Heteroscedasticity on Regression Parameter Estimation and Hypothesis Testing Validity
Posted: Feb 27, 2019
Abstract
Heteroscedasticity represents one of the most pervasive challenges in applied regression analysis, affecting the validity of parameter estimates and statistical inference across numerous scientific disciplines. The conventional assumption of homoscedasticity, where error variance remains constant across observations, rarely holds in practical applications involving real-world data. Despite extensive literature on the consequences of heteroscedasticity, our understanding of its nuanced effects remains incomplete, particularly regarding how different functional forms of variance heterogeneity influence estimation precision and hypothesis testing validity. This research addresses critical gaps in the current literature by systematically examining how various heteroscedastic patterns, beyond the commonly studied monotonic relationships, impact regression outcomes.
Downloads: 56
Abstract Views: 1587
Rank: 130679