The Need of Efficient Water Management in Pakistan

Water is a sign of life, all living beings need it to remain alive. It is natural commodity and three fourth of this planet is water. Still not all is suitable for human consumption and sufficient to meet the ever increasing demands. Availability and supply of water is getting scarce with increasing population of states especially the developing societies. If we look at the history of life on this planet, we find that the humanity has suffered disasters on acquisition and possession of natural resources which are vital for survival of one group, may be at the cost of others. Water falls in that category where if not properly managed its scarcity and stress would lead to unending conflicts and wars till demands equals the supply with the destruction of extra consumptions. Solution to the disastrous situation is in efficient management of water. The efficient and effective management combines awareness and justified storage and distribution of water by the states within their domains. Modern technology and management provides solutions for this efficient management of water by the states. ______

Ardl Model As a Remedy for Spurious Regression: Problems, Performance and Prospects

The most important feature that directed to the development of new time series econometrics was the spurious regression. It is a phenomenon known to econometricians since the times of Yule (1926) who attributed this problem to missing variable. A spurious regression occurs when two independent series come up with significant regression results. For a long time, missing variables were considered as root cause of spurious regression. However, Granger and Newbold (1974) challenged this wisdom and presented unit root as one of the causes of spurious regression. The extensive literature considers the nonstationarity as the only cause of spurious regression. The researchers frequently employed unit root and co-integration procedures for the treatment of spurious regression in case of nonstationarity but these procedures are equally unreliable because of uncertainty about various specification decisions like choice of the deterministic part, structural breaks, choice of autoregressive, lag length and distribution of error term. On the other hand Granger et al. (2001) show that unit root is not the only reason for spurious regression. They show the possibility of spurious regression in stationary time series. Whereas unit root and cointegration are unable to deal with this problem because they deal only nonstationary series. Such amount of conventional econometric literature is inadequate to deal with the problem of spurious regression in stationary time series. The objective of this study is to provide an alternative solution of spurious regression for both stationary and nonstationary time series. So, this study makes two contributions in this particular setup. First, spurious regression occurs due to missing variable and can be avoided by including missing lag values. Therefore, an alternative way to look at the problem of spurious regression takes us back to the missing variable (lag values) which further leads to ARDL model. Second, it significantly reduces the probability of spurious regression in both stationary and nonstationary time series case. This study mainly focusing on Monte Carlo simulations and real data is also used for performance comparison of ARDL model and conventional procedures. Our results indicate that conventional methods are significantly suffering in size and there is power problems but the performance of ARDL in both cases is far better than conventional methods. ARDL model significantly reduced the probability of spurious regression in stationary and nonstationary time series case.