White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice

Abstract:     
Classic linear regression models and their concomitant statistical designs assume a univariate response and white noise. By definition, white noise is normally, independently, and identically distributed with zero mean. This survey tries to answer the following questions: (i) How realistic are these classic assumptions in simulation practice? (ii) How can these assumptions be tested? (iii) If assumptions are violated, can the simulation's I/O data be transformed such that the assumptions hold? (iv) If not, which alternative statistical methods can then be applied?

Number of Pages in PDF File: 20

Keywords: metamodels, experimental designs, generalized least squares, multivariate analysis, normality, jackknife, bootstrap, heteroscedasticity, common random numbers, validation

JEL Classification: C0, C1, C9, C15, C44

working papers series