Simulation Experiments in Practice: Statistical Design and Regression Analysis

Jack P.C. Kleijnen

No 2007-09, Discussion Paper from Tilburg University, Center for Economic Research

Abstract: In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic theory assumes a single simulation response that is normally and independently distributed with a constant variance; moreover, the regression (meta)model of the simulation model?s I/O behaviour is assumed to have residuals with zero means. This article addresses the following questions: (i) How realistic are these assumptions, in 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 do hold? (iv) If not, which alternative statistical methods can then be applied?

JEL-codes: C0 C1 C9 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp
Date: 2007

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Working Paper: Simulation Experiments in Practice: Statistical Design and Regression Analysis (2007)
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Persistent link: http://EconPapers.repec.org/RePEc:dgr:kubcen:200709

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