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

PDF
MPRA_paper_83973.pdf Download (593kB)  Preview 
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:  26 Sep 2019 08:49 
References:  AtiqurRehman, A. U. R., & Zaman, A. (2008). Model specification, observational equivalence and performance of unit root tests. Ahking, F. W. (2002). Model misspecification and Johansen's cointegration analysis: an application to the US money demand. Journal of Macroeconomics, 24(1), 5166. Agunloye, O. K., & Shangodoyin, D. K. (2014). Lag Length Specification in Engle Granger Cointegration Test: A Modified Koyck Mean Lag Approach Based on Partial Correlation. Statistics in Transition new series, 15(4). Charemza, W. W., & Deadman, D. F. (1997). New directions in econometric practice. Books. Choi, C. Y., Hu, L., & Ogaki, M. (2004). A spurious regression approach to estimating structural parameters. Ohio State University Department of Economics Working Paper, (0401). Carrasco Gutierrez, C. E., Castro Souza, R., & Teixeira de Carvalho Guillén, O. (2009). Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features. Chaouachi, K. (2013). False positive result in study on hookah smoking and cancer in Kashmir: measuring risk of poor hygiene is not the same as measuring risk of inhaling water filtered tobacco smoke all over the world. British journal of cancer, 108(6), 1389. Davidson, J. E., Hendry, D. F., Srba, F., & Yeo, S. (1978). Econometric modelling of the aggregate timeseries relationship between consumers' expenditure and income in the United Kingdom. The Economic Journal, 661692. DeJong, D. N., Nankervis, J. C., Savin, N. E., & Whiteman, C. H. (1992). The power problems of unit root test in time series with autoregressive errors. Journal of Econometrics, 53(13), 323343. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427431. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 10571072. Engle, R. F., & Granger, C. W. (1987). Cointegration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251276. Engle, R. and Yoo Sam (1991). Forecasting and Testing in Cointegrated Systems, In Engle and Granger (eds.), Long Run Economic Relationships. Readings in Cointegration, Oxford University Press, New York, 23767. Frey, B. S. (2002). Inspiring economics: Human motivation in political economy. Edward Elgar Publishing. Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of econometrics, 2(2), 111120. Hashimzade, N., & Thornton, M. A. (Eds.). (2013). Handbook of research methods and applications in empirical macroeconomics. Edward Elgar Publishing. Hassler, U. (2003). Nonsense regressions due to neglected timevarying means. Statistical Papers, 44(2), 169182. Hendry, D. F. (1980). Econometricsalchemy or science?. Economica, 387406. Hendry, D. F., & Richard, J. F. (1983). The econometric analysis of economic time series. International Statistical Review/Revue Internationale de Statistique, 111148. Hendry, D. F., Pagan, A. R., & Sargan, J. D. (1984). Dynamic specification. Handbook of econometrics, 2, 10231100. Höfer, Thomas; Hildegard Przyrembel; Silvia Verleger (2004). New evidence for the Theory of the Stork. Paediatric and Perinatal Epidemiology. 18 (1): 18–22. Juselius, K. (1992). Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for UK. Journal of econometrics, 53(13), 211244. Leybourne, S. J., & Newbold, P. (2003). Spurious rejections by cointegration tests induced by structural breaks. Applied Economics, 35(9), 11171121. Nelson, C. R., & Plosser, C. R. (1982). Trends and random walks in macroeconmic time series: some evidence and implications. Journal of monetary economics, 10(2), 139162. Plosser, C. I., & Schwert, G. W. (1978). Money, income, and sunspots: measuring economic relationships and the effects of differencing. Journal of Monetary Economics, 4(4), 637660. Perron, P. (1990). Testing for a unit root in a time series with a changing mean. Journal of Business & Economic Statistics, 8(2), 153162. Phillips, P. C. (1986). Understanding spurious regressions in econometrics. Journal of econometrics, 33(3), 311340. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 335346. Pesaran, M. H., Shin, Y., & Smith, R. J. (1996). Testing for the' Existence of a Longrun Relationship' (No. 9622). Faculty of Economics, University of Cambridge. Pesaran, M. H. (1997). The role of economic theory in modelling the long run. The Economic Journal, 107(440), 178191. Pesaran, M. H., & Smith, R. (1995). Estimating longrun relationships from dynamic heterogeneous panels. Journal of econometrics, 68(1), 79113. Rehman, A. U., & Malik, M. I. (2014). The modified R a robust measure of association for time series. Electronic Journal of Applied Statistical Analysis, 7(1), 113. Sapsford, Roger; Jupp, Victor, eds. (2006). Data Collection and Analysis. Sage. ISBN 0761943625. Simon, H. A. (1954). Spurious correlation: a causal interpretation. Journal of the American statistical Association, 49(267), 467479. Sun, Y. (2004). A convergent tstatistic in spurious regressions. Econometric Theory, 20(05), 943962. Su, J. J. (2008). A note on spurious regressions between stationary series. Applied Economics Letters, 15(15), 12251230. Schwert, G. W. (2002). Tests for unit roots: A Monte Carlo investigation. Journal of Business & Economic Statistics, 20(1), 517. VentosaSantaulària, D. (2009). Spurious regression. Journal of Probability and Statistics, 2009. Yule, G. U. (1926). Why do we sometimes get nonsensecorrelations between TimeSeries?a study in sampling and the nature of timeseries. Journal of the royal statistical society, 89(1), 163. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/83973 