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Testing Cointegrating Relationships Using Irregular and Non-Contemporaneous Series with an Application to Paleoclimate Data

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  • J. Isaac Miller

    (Department of Economics, University of Missouri, Columbia, Missouri, USA)

Abstract

Published in the Journal of Time Series Analysis (https://doi.org/10.1111/jtsa.12469) Time series that are observed neither regularly nor contemporaneously pose problems for most multivariate analyses. Common and intuitive solutions to these problems include linear and step interpolation or other types of imputation to a higher, regular frequency. However, interpolation is known to cause serious problems with the size and power of statistical tests. Due to the difficulty in measuring stochastically varying paleoclimate phenomena such as CO2 concentrations and surface temperatures, time series of such measurements are observed neither regularly nor contemporaneously. This paper presents large- and small-sample analyses of the size and power of cointegration tests of time series with these features and confirms the robustness of cointegration of these two series found in the extant literature. Step interpolation is preferred over linear interpolation.

Suggested Citation

  • J. Isaac Miller, 2018. "Testing Cointegrating Relationships Using Irregular and Non-Contemporaneous Series with an Application to Paleoclimate Data," Working Papers 1809, Department of Economics, University of Missouri.
    • Handle: RePEc:umc:wpaper:1809
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    5. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Advances in Econometrics, in: Essays in Honor of Peter C. B. Phillips, volume 14, pages 93-122, Emerald Group Publishing Limited.
    6. James E. H. Davidson & David B. Stephenson & Alemtsehai A. Turasie, 2016. "Time series modeling of paleoclimate data," Environmetrics, John Wiley & Sons, Ltd., vol. 27(1), pages 55-65, February.
    7. Eric Ghysels & J. Isaac Miller, 2015. "Testing for Cointegration with Temporally Aggregated and Mixed-Frequency Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(6), pages 797-816, November.
    8. Dieter Lüthi & Martine Le Floch & Bernhard Bereiter & Thomas Blunier & Jean-Marc Barnola & Urs Siegenthaler & Dominique Raynaud & Jean Jouzel & Hubertus Fischer & Kenji Kawamura & Thomas F. Stocker, 2008. "High-resolution carbon dioxide concentration record 650,000–800,000 years before present," Nature, Nature, vol. 453(7193), pages 379-382, May.
    9. J. Isaac Miller, 2010. "Cointegrating regressions with messy regressors and an application to mixed‐frequency series," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(4), pages 255-277, July.
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    12. J. Isaac Miller & Xi Wang, 2016. "Implementing Residual-Based KPSS Tests for Cointegration with Data Subject to Temporal Aggregation and Mixed Sampling Frequencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 810-824, November.
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    17. Johansen, Søren & Juselius, Katarina, 1992. "Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for UK," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 211-244.
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    Cited by:

    1. Vasco J.Gabriel & Luis F. Martins & Anthoulla Phella, 2021. "Modelling Low-Frequency Covariability of Paleoclimatic Data," Working Papers 2022_17, Business School - Economics, University of Glasgow.
    2. Burak Alparslan Eroğlu & J. Isaac Miller & Taner Yiğit, 2022. "Time-varying cointegration and the Kalman filter," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 1-21, January.
    3. Proietti, Tommaso & Maddanu, Federico, 2024. "Modelling cycles in climate series: The fractional sinusoidal waveform process," Journal of Econometrics, Elsevier, vol. 239(1).

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    More about this item

    Keywords

    cointegration; irregularly time series; non-contemporaneous time series; misaligned time series; paleoclimate data;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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