An Efficient and Theoretically Consistent Procedure for Generating Correlated, Non-Normal Random Variables in Simulation Models
W. G. Boggess and
Charles Moss ()
No 271052, 1990 Annual meeting, August 5-8, Vancouver, Canada from American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association)
In recent years, simulation has become an important methodology for applied decision analysis under uncertainty. A typical simulation effort requires generating a set of possibly correlated and non-normal random variables, using information regarding their underlying joint probability density function contained in a presumably random sample. A few techniques have been suggested to accomplish this task, but most have not met ·the requirement of being correct and efficient from the statistical point of view. This study proposes a multivariate hyperbolic sine probability density function as a basis to develop an efficient and theoretically consistent approach for generating correlated, non-normal random variables.
Keywords: Agricultural and Food Policy; Research Methods/ Statistical Methods (search for similar items in EconPapers)
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