AN ALGORITHM TO FIND SIGNIFICANT COMMODITIES IN STOCHASTIC LASPEYRES REGRESSION MODEL

The main objective of this paper is to develop a step by step procedure to discover the influential commodities in stochastic Laspayres regression model when the errors are assumed to be serially correlated with autoregressive process of order p. The two familiar methods of finding unusual observations the ?Hat matrix? and difference in parameter vector beta i.e. ?DFBETA? are considered in this algorithm. This algorithm facilitates the researcher to carry out their research in convenient way.