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Asymptotic Theory For The Durbin–Watson Statistic Under Long-Memory Dependence

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  • Nakamura, Shisei
  • Taniguchi, Masanobu

Abstract

In time series regression models with “short-memory” residual processes, the Durbin–Watson statistic (DW) has been used for the problem of testing for independence of the residuals. In this paper we elucidate the asymptotics of DW for “long-memory” residual processes. A standardized Durbin–Watson statistic (SDW) is proposed. Then we derive the asymptotic distributions of SDW under both the null and local alternative hypotheses. Based on this result we evaluate the local power of SDW. Numerical studies for DW and SDW are given.

Suggested Citation

  • Nakamura, Shisei & Taniguchi, Masanobu, 1999. "Asymptotic Theory For The Durbin–Watson Statistic Under Long-Memory Dependence," Econometric Theory, Cambridge University Press, vol. 15(6), pages 847-866, December.
    • Handle: RePEc:cup:etheor:v:15:y:1999:i:06:p:847-866_15
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    Cited by:

    1. Anurag Banerjee, 2004. "Sensitivity of OLS estimates against ARFIMA error process as small sample Test for long memory," Econometric Society 2004 Australasian Meetings 159, Econometric Society.
    2. Kleiber, Christian & Krämer, Walter, 2004. "Finite sample of the Durbin-Watson test against fractionally integrated disturbances," Technical Reports 2004,15, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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