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Is climate change time reversible?

Author

Listed:
  • Francesco Giancaterini

    (Department of Quantitative Economics, Maastricht University, The Netherlands)

  • Alain Hecq

    (Maastricht University, The Netherlands; Rimini Centre for Economic Analysis)

  • Claudio Morana

    (Center for European Studies, University of Milano-Bicocca, Italy; Rimini Centre for Economic Analysis; CeRP, Collegio Carlo Alberto, Italy)

Abstract

This paper proposes strategies to detect time reversibility in stationary stochastic processes by using the properties of mixed causal and noncausal models. It shows that they can also be used for non-stationary processes when the trend component is computed with the Hodrick-Prescott filter rendering a time-reversible closed-form solution. This paper also links the concept of an environmental tipping point to the statistical property of time irreversibility and assesses fourteen climate indicators. We find evidence of time irreversibility in GHG emissions, global temperature, global sea levels, sea ice area, and some natural oscillation indices. While not conclusive, our findings urge the implementation of correction policies to avoid the worst consequences of climate change and not miss the opportunity window, which might still be available, despite closing quickly.

Suggested Citation

  • Francesco Giancaterini & Alain Hecq & Claudio Morana, 2022. "Is climate change time reversible?," Working Paper series 22-08, Rimini Centre for Economic Analysis, revised Dec 2022.
  • Handle: RePEc:rim:rimwps:22-08
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    References listed on IDEAS

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    1. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    2. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 671-690.
    3. F. J. Breidt & R. A. Davis, 1992. "Time‐Reversibility, Identifiability And Independence Of Innovations For Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(5), pages 377-390, September.
    4. Christian Gourieroux & Joann Jasiak, 2022. "Nonlinear Fore(Back)casting and Innovation Filtering for Causal-Noncausal VAR Models," Papers 2205.09922, arXiv.org, revised Apr 2024.
    5. Clemen, Robert T. & Murphy, Allan H. & Winkler, Robert L., 1995. "Screening probability forecasts: contrasts between choosing and combining," International Journal of Forecasting, Elsevier, vol. 11(1), pages 133-145, March.
    6. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    7. Morten O. Ravn & Harald Uhlig, 2002. "On adjusting the Hodrick-Prescott filter for the frequency of observations," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 371-375.
    8. Christian Gouriéroux & Jean-Michel Zakoian, 2013. "Explosive Bubble Modelling by Noncausal Process," Working Papers 2013-04, Center for Research in Economics and Statistics.
    9. Backus, David K & Kehoe, Patrick J, 1992. "International Evidence of the Historical Properties of Business Cycles," American Economic Review, American Economic Association, vol. 82(4), pages 864-888, September.
    10. Quandt, Richard E, 1974. "A Comparison of Methods for Testing Nonnested Hypotheses," The Review of Economics and Statistics, MIT Press, vol. 56(1), pages 92-99, February.
    11. Marc Hallin & Claude Lefèvre & Madan Lal Puri, 1988. "On time-reversibility and the uniqueness of moving average representations for non-Gaussian stationary time series," ULB Institutional Repository 2013/2017, ULB -- Universite Libre de Bruxelles.
    12. Fries, Sébastien & Zakoian, Jean-Michel, 2019. "Mixed Causal-Noncausal Ar Processes And The Modelling Of Explosive Bubbles," Econometric Theory, Cambridge University Press, vol. 35(6), pages 1234-1270, December.
    13. Tommaso Proietti, 2023. "Peaks, gaps, and time‐reversibility of economic time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 43-68, January.
    14. John Geweke & Gianni Amisano, 2011. "Hierarchical Markov normal mixture models with applications to financial asset returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
    15. Chen, Yi-Ting & Chou, Ray Y. & Kuan, Chung-Ming, 2000. "Testing time reversibility without moment restrictions," Journal of Econometrics, Elsevier, vol. 95(1), pages 199-218, March.
    16. Geweke, John, 2001. "Bayesian econometrics and forecasting," Journal of Econometrics, Elsevier, vol. 100(1), pages 11-15, January.
    17. Ramsey, James B & Rothman, Philip, 1996. "Time Irreversibility and Business Cycle Asymmetry," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 28(1), pages 1-21, February.
    18. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    19. Belaire-Franch, Jorge & Contreras, Dulce, 2003. "Tests for time reversibility: a complementarity analysis," Economics Letters, Elsevier, vol. 81(2), pages 187-195, November.
    20. Hinich , Melvin J. & Rothman, Philip, 1998. "Frequency-Domain Test Of Time Reversibility," Macroeconomic Dynamics, Cambridge University Press, vol. 2(1), pages 72-88, March.
    21. Francesco Giancaterini & Alain Hecq, 2020. "Inference in mixed causal and noncausal models with generalized Student's t-distributions," Papers 2012.01888, arXiv.org, revised Nov 2022.
    22. Lanne Markku & Saikkonen Pentti, 2011. "Noncausal Autoregressions for Economic Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
    23. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    24. Robert M. DeConto & David Pollard & Richard B. Alley & Isabella Velicogna & Edward Gasson & Natalya Gomez & Shaina Sadai & Alan Condron & Daniel M. Gilford & Erica L. Ashe & Robert E. Kopp & Dawei Li , 2021. "The Paris Climate Agreement and future sea-level rise from Antarctica," Nature, Nature, vol. 593(7857), pages 83-89, May.
    25. Emir Shuford & Arthur Albert & H. Edward Massengill, 1966. "Admissible probability measurement procedures," Psychometrika, Springer;The Psychometric Society, vol. 31(2), pages 125-145, June.
    26. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    27. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive Density Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 5, pages 197-284, Elsevier.
    28. Shujun Li & Lixin Wu & Yun Yang & Tao Geng & Wenju Cai & Bolan Gan & Zhaohui Chen & Zhao Jing & Guojian Wang & Xiaohui Ma, 2020. "The Pacific Decadal Oscillation less predictable under greenhouse warming," Nature Climate Change, Nature, vol. 10(1), pages 30-34, January.
    29. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    30. Robert M. de Jong & Neslihan Sakarya, 2016. "The Econometrics of the Hodrick-Prescott Filter," The Review of Economics and Statistics, MIT Press, vol. 98(2), pages 310-317, May.
    31. Wenju Cai & Guojian Wang & Agus Santoso & Michael J. McPhaden & Lixin Wu & Fei-Fei Jin & Axel Timmermann & Mat Collins & Gabriel Vecchi & Matthieu Lengaigne & Matthew H. England & Dietmar Dommenget & , 2015. "Increased frequency of extreme La Niña events under greenhouse warming," Nature Climate Change, Nature, vol. 5(2), pages 132-137, February.
    32. Christian Gourieroux & Joann Jasiak, 2016. "Filtering, Prediction and Simulation Methods for Noncausal Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 405-430, May.
    33. Breid, F. Jay & Davis, Richard A. & Lh, Keh-Shin & Rosenblatt, Murray, 1991. "Maximum likelihood estimation for noncausal autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 36(2), pages 175-198, February.
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    Cited by:

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    3. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
    4. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.

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    More about this item

    Keywords

    mixed causal and noncausal models; time reversibility; Hodrick-Prescott filter; climate change; global warming; environmental tipping points;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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