Estimating models of Extreme Behavior: A Monte Carlo Comparison between SWAT and Quantise Regression

We are interested in theoretical explanations of extreme behavior in social and management science situations. For example, in studying organizational performance we theorize that organizations achieve high levels of performance by employing innovative or unique behavioral characteristics. Methodologially, several approaches have emerged to facilitate investigation of how extreme cases differ from the typical. This paper focuses on two quantitative approaches that provide. tools for modeling extreme hehavior: substantively weighied analytical techniques (SWAT) and quantile regression. We evaluate both for their anility to accurately estimate models of extreme behavior when that behavior significantly differs from the average case. Since we attempt to evaluate statistical approaches in situations where standard axiomatic approaches fall short. our strategy is to use simulation techniques where the underlying data-generating structure is known and desisted to have different underlying mathematical relationships between the middle and the two extremes. We also apply a Monte Carlo approach of repeated simulating to investigate the sampling characteristics of these approaches. Finally, we apply standard measures for evaluation of statistical estimators, mean square error, to examine both the bias and the relative efficiency of each approach. The experimental results demonstrate that quantile regression provides a more accurate and reliable estimation of extreme phenomena.