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Common factors in conditional distributions for Bivariate time series

Author

Listed:
  • Granger, Clive W. J.
  • Terasvirta, Timo
  • Patton, Andrew J.

Abstract

A definition for a common factor for bivariate time series is suggested by considering the decomposition of the conditional density into the product of the marginals and the copula, with the conditioning variable being a common factor if it does not directly enter the copula. The links of this definition with a common factor being a dominant feature in standard linear representations is shown. An application using a business cycle indicator as the common factor in the relationship between U.S. income and consumption found that both series held the factor in their marginals but not in the copula.

Suggested Citation

  • Granger, Clive W. J. & Terasvirta, Timo & Patton, Andrew J., 2003. "Common factors in conditional distributions for Bivariate time series," LSE Research Online Documents on Economics 24854, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:24854
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    File URL: https://researchonline.lse.ac.uk/id/eprint/24854/
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    References listed on IDEAS

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    1. Granger, C. W. J., 1987. "Implications of Aggregation with Common Factors," Econometric Theory, Cambridge University Press, vol. 3(2), pages 208-222, April.
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    6. Patton, Andrew J, 2001. "Modelling Time-Varying Exchange Rate Dependence Using the Conditional Copula," University of California at San Diego, Economics Working Paper Series qt01q7j1s2, Department of Economics, UC San Diego.
    7. Patton, Andrew J, 2001. "Estimation of Copula Models for Time Series of Possibly Different Length," University of California at San Diego, Economics Working Paper Series qt3fc1c8hw, Department of Economics, UC San Diego.
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    Keywords

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    JEL classification:

    • 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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