An Alternative Estimation of Spurious Regression Model

In examining the likely consequences of nonsense relationship, Granger and Newbold (1974) made it clear that the differencing is not the universal sure solution to the problem of spurious regression models. This has prompted the discovery of the cointegration regression estimation by Engle and Granger (1987). In recent years applied econometricians are debating with the problem of the spurious regression model when the co movements between the variables are different. If the variables of the model are not cointegrated, there is a question whether the background economic or financial theory is plausible with the data that we are analyzing. This paper reviews the debate and propose an alternative solution to the problem. Our approach uses a suitable data transformation of the variables of the model to reduce the spurious correlation, stochastic means and variances in standard level. In a non-cointegrated USA information processing investment model, we apply our technique and found a meaningful solution