جیہڑا ویکھ کے لہراں ہار گیا
کدی اوہ ناں بیڑا پار گیا
جہیڑا حسن دا مان کریندا سی
اوہ یوسف مصر بازار گیا
جیہڑا وڑیا عشق دے میلے نوں
اوہ عقل دی بازی ہار گیا
اوہ دوہتا پاک نبیؐ دا اے
بن جنت دا سردار گیا
اوہ بندہ جانو چنگا اے
جیہڑا سوہنا وقت گزار گیا
اوہنوں ساری دنیا یاد رہی
ہک مینوں منوں وسار گیا
اوہ خالص بندہ مولا دا
جیہڑا خالص لے کردار گیا
After the Prophets of Allah Almighty, the most sacred class of mankind is the class of Prophet Muhammad's Companions. Those are the people who had seen the prophet of Islam with their naked eyes, remained in his companionship and got the heights of knowledge and actions and proved to be a great example of character by attaining the image of Prophet Muhammad's model of excellence. Another class which, like the companions of Prophet (pbuh), deserves such honor is the class of Tabe’en (The followers of the Companions). Tabe'en had contributed a matchless role in the history of Islam regarding religious knowledge and literature. They had also examplary performed in social, economical, political and military services. Due to these great services they are considered the most sacred class of the Ummah after the prophet’companions. There is a list of academic and literary services ahead of the name of each person in this class. And these services are the great testimony to the greatness of these people. The prophet's companions received the religious knowledge directly from the Prophet (peace and blessings of Allah be upon him), while Tabe'en got it from the companions and then published it in the whole world. The sincere efforts performed by Tabe'en regarding Quran, Hadith, Tafseer and Islamic litrature are of so high level that no one had reached such level of sincerity in the entire history of Islam. The steps that occur after that period, regarding the development of Islamic culture are only the effects of their services. Actually ‘‘Tabe’en’’ is the only class that has spread the social, moral and spritual blessings of Islam throughout the world. That is why, it is not only the Quran that witnesses their greatness but the Prophet (peace be upon him) also praises them.
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.