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Temporal Aggregation, Causality Distortions, and a Sign Rule

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Author Info
Tilak Abeysinghe () (National University of Singapore)
Gulasekaran Rajaguru (Bond University)

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Abstract

Temporally aggregated data is a bane for Granger causality tests. The same set of variables may lead to contradictory causality inferences at different levels of temporal aggregation. Obtaining temporally disaggregated data series is impractical in many situations. Since cointegration is invariant to temporal aggregation and implies Granger causality this paper proposes a sign rule to establish the direction of causality. Temporal aggregation leads to a distortion of the sign of the adjustment coefficients of an error correction model. The sign rule works better with highly temporally aggregated data. The practitioners, therefore, may revert to using annual data for Granger causality testing instead of looking for quarterly, monthly or weekly data. The method is illustrated through three applications.

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Paper provided by National University of Singapore, Department of Economics in its series Departmental Working Papers with number wp0406.

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Date of creation: Dec 2003
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Handle: RePEc:nus:nusewp:wp0406

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Keywords: Granger causality test cointegration error correction model adjustment coefficient sign rule

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C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models

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  1. Granger, C. W. J. & Siklos, Pierre L., 1995. "Systematic sampling, temporal aggregation, seasonal adjustment, and cointegration theory and evidence," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 357-369. [Downloadable!] (restricted)
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  2. Lahiri, Kajal & Mamingi, Nlandu, 1995. "Testing for cointegration: Power versus frequency of observation -- another view," Economics Letters, Elsevier, vol. 49(2), pages 121-124, August. [Downloadable!] (restricted)
  3. Pierse, R. G. & Snell, A. J., 1995. "Temporal aggregation and the power of tests for a unit root," Journal of Econometrics, Elsevier, vol. 65(2), pages 333-345, February. [Downloadable!] (restricted)
  4. Rajaguru GULASEKARAN & Tilak ABEYSINGHE, 2002. "The Distortionary Effects Of Temporal Aggregation On Granger Causality," Departmental Working Papers wp0204, National University of Singapore, Department of Economics. [Downloadable!]
  5. Clive W. J. Granger, 1988. "Aggregation of time series variables-a survey," Discussion Paper / Institute for Empirical Macroeconomics 1, Federal Reserve Bank of Minneapolis. [Downloadable!]
  6. Mosconi, Rocco & Giannini, Carlo, 1992. "Non-causality in Cointegrated Systems: Representation Estimation and Testing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 399-417, August.
  7. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211. [Downloadable!] (restricted)
  8. Ghosh, Atish R, 1995. "Intertemporal Tax-Smoothing and the Government Budget Surplus: Canada and the United States," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 27(4), pages 1033-45, November. [Downloadable!] (restricted)
  9. Grayham E. Mizon & David F. Hendry, 1998. "Exogeneity, causality, and co-breaking in economic policy analysis of a small econometric model of money in the UK," Empirical Economics, Springer, vol. 23(3), pages 267-294. [Downloadable!] (restricted)
  10. Neil R. Ericsson & John S. Irons & Ralph W. Tryon, 2001. "Output and inflation in the long run," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 241-253. [Downloadable!]
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  11. Mizon, Grayham E & Hendry, David F, 1980. "An Empirical Application and Monte Carlo Analysis of Tests of Dynamic Specification," Review of Economic Studies, Blackwell Publishing, vol. 47(1), pages 21-45, January. [Downloadable!] (restricted)
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