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Cointegration And Distance Between Information Sets

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  • Triacca, Umberto

Abstract

This paper investigates Granger noncausality and the cointegrating relation between two time series in the Hilbert space framework. This framework allows us to analyze the relationship between cointegration and distance between two information sets. In particular, we prove that if two variables, X and Y, are cointegrated, then the distance between two information sets, concerning the differenced series ΔX and ΔY, must be less than the standard deviation of ΔX.

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  • Triacca, Umberto, 2000. "Cointegration And Distance Between Information Sets," Econometric Theory, Cambridge University Press, vol. 16(1), pages 102-111, February.
    • Handle: RePEc:cup:etheor:v:16:y:2000:i:01:p:102-111_16
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    Cited by:

    1. Umberto Triacca, 2002. "Cointegration in VAR(1) process: Characterization and testing," Statistical Papers, Springer, vol. 43(3), pages 435-443, July.
    2. Focker, Fulvia & Triacca, Umberto, 2006. "Interpreting the concept of joint unpredictability of asset returns: A distance approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 765-770.

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