Rehman, Atiqur and Malik, Muhammad Irfan (2014): The modified R a robust measure of association for time series. Published in: Electronic Journal of Applied Statistical Analysis , Vol. 7, No. 1 (26 April 2014): pp. 113.

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Abstract
Since times of Yule (1926), it is known that correlation between two time series can produce spurious results. Granger and Newbold (1974) see the roots of spurious correlation in nonstationarity of the time series. However the study of Granger, Hyung and Jeon (2001) prove that spurious correlation also exists in stationary time series. These facts make the correlation coefficient an unreliable measure of association. This paper proposes ‘Modified R’ as an alternate measure of association for the time series. The Modified R is robust to the type of stationarity and type of deterministic part in the time series. The performance Modified R is illustrated via extensive Monte Carlo Experiments.
Item Type:  MPRA Paper 

Original Title:  The modified R a robust measure of association for time series 
Language:  English 
Keywords:  Correlation Coefficient; Spurious Regression; Stationary Series 
Subjects:  C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling 
Item ID:  60025 
Depositing User:  Mr. muhammad irfan Malik 
Date Deposited:  19 Nov 2014 05:35 
Last Modified:  24 Jun 2020 03:42 
References:  Aldrich, J. (2001). Correlation Spurious and Genuine in Pearson and Yule. Statistical Science , 10 (4), 364376. Elderton, W. P. (1907). Frequency Curves and Correlation. London: Institute of Actuaries. Enders, W. (1995). Applied econometric times series. New York: Willey. Granger, C. W., & Newbold, P. (1974). Spurious Regression in Econometrics. Journal of Econometrics , 2 (2), 111120. Granger, C. W., Hyung, N., & Jeon, Y. (2001). Spurious regressions with stationary series. Applied Economics , 33 (7), 899904. Greene, W. H. (2005). Econometric Analysis. New York: Prentice Hall. Gujarati, D. (1999). Essentials of Econometrics. Irwin: McGraw Hill . Nelson, C., & Plossor, C. (1982). Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications. Journal of Monetary Economics , 10 (2), 139162. Pearson, K. (1894). Contributions to the Mathematical Theory of Evolution. Philosophical Transactions of the Royal Society of London , 185, 71110. Pearson, K., Lee, A., & BramleyMoore, L. (1899). Mathematical Contributions to the Theory of Evolution. VI. Genetic (Reproductive) Selection: Inheritance of Fertility in Man, and of Fecundity in Thoroughbred Racehorses,. Philosophical Transactions of the Royal Society of London. Series A , 192, 252330. Phillips, P. C. (1987). Time series regression with a unit root. Econometrica , 55 (2), 277301. Simon, H. A. (1954). Spurious Correlation: A Causal Interpretation,. Journal of the American Statistical Association, Vol. 49, No. 267 (1954), pp. 467479 , 49 (267), 467479. Yule, G. U. (1910). On the Distribution of Deaths with Age when the Causes of Death Act Cumulatively, and Similar Frequency Distributions,. Journal of the Royal Statistical Society , 73 (1), 2638. Yule, G. U. (1897). On the Theory of Correlation. Journal of the Royal Statistical Society . 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), 164. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/60025 