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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

Abstract

Time series that are observed neither regularly nor contemporaneously pose problems for most multivariate analyses. Common and intuitive solutions to these problems include interpolation and 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 dating paleoclimate data such as CO2 concentrations and surface temperatures, time series of such measurements are observed neither regularly nor contemporaneously. This article presents large‐ and small‐sample analyses of the size and power of cointegration tests of time series with these features and supports the robustness of cointegration of these two series found in the extant literature. Compared to linear or higher‐order polynomial interpolation, step interpolation results in the least size distortion and is therefore recommended.

Suggested Citation

  • J. Isaac Miller, 2019. "Testing Cointegrating Relationships Using Irregular and Non‐Contemporaneous Series with an Application to Paleoclimate Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 936-950, November.
    • Handle: RePEc:bla:jtsera:v:40:y:2019:i:6:p:936-950
    DOI: 10.1111/jtsa.12469
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    Cited by:

    1. Proietti, Tommaso & Maddanu, Federico, 2024. "Modelling cycles in climate series: The fractional sinusoidal waveform process," Journal of Econometrics, Elsevier, vol. 239(1).
    2. 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.
    3. Castro, Tomas del Barrio & Escribano, Alvaro & Sibbertsen, Philipp, 2025. "Modeling and forecasting the long memory of Cyclical Trends in paleoclimate data," Energy Economics, Elsevier, vol. 147(C).
    4. 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.

    More about this item

    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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