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Time series properties of global climate variables: detection and attribution of climate change

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  • David I. Stern

    (Australian National University, Centre for Resource and Environmental Studies)

  • Robert K. Kaufmann

    ()
    (Boston University, Center for Energy and Environmental Studies)

Abstract

Several time series investigations of global climate change have been published, but the time series properties of the variables has received little attention with a few exceptions in the case of global temperature series. We focus on the presence or absence of stochastic trends. We use three different tests to determine the presence of stochastic trends in a selected group of global climate change data for the longest time series available. The test results indicate that the radiative forcing due to changes in the atmospheric concentrations of CO2, CH4, CFCs, and N2O, emissions of SOX, CO2, CH4, and CFCs and solar irradiance contain a unit root while most tests indicate that temperature does not. The concentration of stratospheric sulfate aerosols emitted by volcanoes is stationary. The radiative forcing variables cannot be aggregated into a deterministic trend which might explain the changes in temperature. Taken at face value our statistical tests would indicate that climate change has taken place over the last 140 years but that this is not due to anthropogenic forcing. However, the noisiness of the temperature series makes it difficult for the univariate tests we use to detect the presence of a stochastic trend. We demonstrate that multivariate cointegration analysis can attribute the observed climate change directly to natural and anthropogenic forcing factors in a statistically significant manner between 1860 and 1994.

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Bibliographic Info

Paper provided by Australian National University, Centre for Resource and Environmental Studies, Ecological Economics Program in its series Working Papers in Ecological Economics with number 9702.

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Date of creation: Mar 1997
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Handle: RePEc:anu:wpieep:9702

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Web page: http://incres.anu.edu.au/EEP/wp.html

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  1. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  2. Schwert, G William, 2002. "Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 5-17, January.
  3. Phillips, P.C.B., 1986. "Testing for a Unit Root in Time Series Regression," Cahiers de recherche 8633, Universite de Montreal, Departement de sciences economiques.
  4. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
  5. Schmidt, Peter & Phillips, C B Peter, 1992. "LM Tests for a Unit Root in the Presence of Deterministic Trends," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 257-87, August.
  6. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  7. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  8. Dickey, David A & Rossana, Robert J, 1994. "Cointegrated Time Series: A Guide to Estimation and Hypothesis Testing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 56(3), pages 325-53, August.
  9. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
  10. Peter C.B. Phillips & Sam Ouliaris, 1987. "Asymptotic Properties of Residual Based Tests for Cointegration," Cowles Foundation Discussion Papers 847R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1988.
  11. Kim, Kiwhan & Schmidt, Peter, 1990. "Some evidence on the accuracy of Phillips-Perron tests using alternative estimates of nuisance parameters," Economics Letters, Elsevier, vol. 34(4), pages 345-350, December.
  12. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
  13. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
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Cited by:
  1. Pierre Perron & Francisco Estrada, 2012. "Breaks, trends and the attribution of climate change: a time-series analysis," Boston University - Department of Economics - Working Papers Series WP2012-013, Boston University - Department of Economics.
  2. Liu, Hui & Rodríguez, Gabriel, 2005. "Human activities and global warming: a cointegration analysis," MPRA Paper 9939, University Library of Munich, Germany.

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