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Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series

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Abstract

We examine the effects of mixed sampling frequencies and temporal aggregation on standard tests for cointegration. While it is well known that aggregation and sampling frequency do not affect the long-run properties of time series, we find that the effects of aggregation on the size of commonly used tests may be severe. Matching sampling schemes of all series generally reduces size, and the nominal size is obtained when all series are skip-sampled in the same way -- e.g., end-of-period sampling. When matching is not feasible, the size of the likelihood-based trace test may be improved by using a mixed-frequency model rather than an aggregated model. However, a mixed-frequency strategy may not improve the size distortion of residual-based cointegration tests compared to aggregated series. We test stock prices and dividends for cointegration as an empirical demonstration of the size distortion.

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  • Eric Ghysels & J. Isaac Miller, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," Working Papers 1307, Department of Economics, University of Missouri, revised 07 May 2014.
  • Handle: RePEc:umc:wpaper:1307
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    Cited by:

    1. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    2. 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.
    3. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    4. Götz, Thomas B. & Hecq, Alain, 2014. "Nowcasting causality in mixed frequency vector autoregressive models," Economics Letters, Elsevier, vol. 122(1), pages 74-78.
    5. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, vol. 192(1), pages 207-230.
    6. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    7. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.
    8. John Cotter & Mark Hallam & Kamil Yilmaz, 2017. "Mixed-Frequency Macro-Financial Spillovers," Koç University-TUSIAD Economic Research Forum Working Papers 1704, Koc University-TUSIAD Economic Research Forum.
    9. Marçal, Emerson Fernandes & Zimmermann, Beatrice Aline & Mendonça, Diogo de Prince & Merlin, Giovanni Tondin, 2015. "Does mixed frequency vector error correction model add relevant information to exchange misalignment calculus? Evidence for United States," Textos para discussão 385, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
    10. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    11. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    12. repec:eee:ecosta:v:5:y:2018:i:c:p:45-66 is not listed on IDEAS
    13. J. Isaac Miller, 2016. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1142-1171, June.
    14. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.

    More about this item

    Keywords

    temporal aggregation; mixed sampling frequencies; cointegration; trace test; residual-based cointegration test;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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

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