In conventional Econometrics, the unit root and cointegration analysis are the only ways to circumvent the spurious regression which may arise from missing variable (lag values) rather than the nonstationarity process in time series data. We propose the Ghouse equation solution of autoregressive distributed lag mechanism which does not require additional work in unit root testing and bound testing. This advantage makes the proposed methodology more efficient compared to the existing cointegration procedures. The earlier tests weaken their position in comparison to it, as they had numerous linked testing procedures which further increase the size of the test and/or reduce the test power. The simplification of the Ghouse equation does not attain any such type of error, which makes it a more powerful test as compared to widely cited exiting testing methods in econometrics and statistics literature.

Nelson and Kang (1984) showed that regression of a unit root time-series on a linear time trend provides significant results even if there is no forecast able association amongst the path of the time-series and linear trend. Using Monte Carlo simulations, this paper shows that phenomenon also exists in stationary time series and regression of a stationary time series on linear trend also produces significant results without the existence of any predictable relationship between the time -series and linear trend. The spurious trend is observable in most of the moderate sample sizes and sometimes in sufficiently large samples of size over 500 observations. The implications of these findings for unit root test procedures are discussed briefly.