Identifying best-practices: A Monte Carlo evaluation of quantile regression

Abstract

The area of best practice research has only recently begun to embrace statistically based comparisons as a basis for identifying recommended practices. In part motivated by growing interest in performance measurement activities these new approaches hold significant potential for improvin our ability to identify and utilize true best practices. Unfortunately, little has been done to study how to apply statistical methods to this task appropriately. In this paper a Monte Carlo evaluation is developed to demonstrate that how Quantile Regression methods can be used to identify best practice. After a brief literature review and a summary of the Quantile Regression technique, the paper develops a specific monte carle simulation design based on statistical situations with varying numbers of high, medium and low performing organizations. Next, we apply quantile regression to the simulated data and attempts to develop some reasonable guidance about how to apply quantile regressions to real world data. The results demonstrate that quantile regression can accurately estimate different models for different types of organizations (e.g. high and low performing) and should be considered as an effective tool for the empirical study of these practices when samples of similar organizations are available. As an order based statistical estimation approach, it also has the virtue of being more robust than typical moment apporoaches.