Gross product simulation with pooling of linear and nonlinear regression models

Abstract

This paper discusses the problem of decision support systems in the organization. The procedure (linear combination) developed with the aim to combine some predicted results obtained with simulation of linear and nonlinear regression models (experts), multiple regression model, nonparametric regression model, and semi parametric regression model. This adjustment procedure enforce some statistical characteristics like the expected value of the gross production rate based on Cobb-Douglas production function is unbiased for the actual value, and the total weights (importance) of all models (experts) is equal to one. We used modeling and simulation techniques to generate our data and to apply the procedure.