Ghouse, Ghulam and Khan, Saud Ahmed and Rehman, Atiq Ur (2018): ARDL model as a remedy for spurious regression: problems, performance and prospectus.

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Abstract
Spurious regression have performed a vital role in the construction of contemporary time series econometrics and have developed many tools employed in applied macroeconomics. The conventional Econometrics has limitations in the treatment of spurious regression in nonstationary time series. While reviewing a wellestablished study of Granger and Newbold (1974) we realized that the experiments constituted in this paper lacked Lag Dynamics thus leading to spurious regression. As a result of this paper, in conventional Econometrics, the Unit root and Cointegration analysis have become the only ways to circumvent the spurious regression. These procedures are also equally capricious because of some specification decisions like, choice of the deterministic part, structural breaks, autoregressive lag length choice and innovation process distribution. This study explores an alternative treatment for spurious regression. We concluded that it is the missing variable (lag values) that are the major cause of spurious regression therefore an alternative way to look at the problem of spurious regression takes us back to the missing variable which further leads to ARDL Model. The study mainly focus on Monte Carlo simulations. The results are providing justification, that ARDL model can be used as an alternative tool to avoid the spurious regression problem.
Item Type:  MPRA Paper 

Original Title:  ARDL model as a remedy for spurious regression: problems, performance and prospectus 
Language:  English 
Keywords:  Spurious regression, misspecification, Stationarity, unit root, cointegration and ARDL 
Subjects:  B  History of Economic Thought, Methodology, and Heterodox Approaches > B4  Economic Methodology > B41  Economic Methodology C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics C  Mathematical and Quantitative Methods > C5  Econometric Modeling C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods 
Item ID:  83973 
Depositing User:  Mr Ghulam Ghouse 
Date Deposited:  19 Jan 2018 02:37 
Last Modified:  19 Jan 2018 02:38 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/83973 