Forecasting by Dynamic Regression Models with an Application

Abstract

ABSTRACT Dynamic Regression Model is that model which takes the time into account. The modeling of the Dynamic Regression shows how the output is resulted from the input. This depends on the following: 1. The relation of the lag time with the input and output. 2. The time composition for the turbulence series (random error) In order provide mathematical model, the relative model was identified by specifying the linear transformation function. The relative model of the transformation function was of the degree (0, 0, 1). When the values of turbulence series were examined by using auto - correlation and partial auto correlation coefficients, it is found that all of the coefficients were insignificant and that consequently proves the turbulence series which is a series of random residuals, so that:- N‌t=at. Nt=at.