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A generalized least squares estimation method for the autoregressive conditional duration model

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Listed:
  • Wanbo Lu

    (Southwestern University of Finance and Economics)

  • Rui Ke

    (Southwestern University of Finance and Economics)

Abstract

A generalized least squares estimation method with inequality constraints for the autoregressive conditional duration model is proposed in this paper. The estimation procedure includes three stages. The final generalized least-squares estimator is consistent and $$\sqrt{T}$$ T —asymptotically normal distributed. Our estimator has the advantage over the often used quasi-maximum likelihood estimator in which it easily implemented and does not require the choice of initial values for the iterative optimization procedure. A large number of simulation studies confirm our theoretical results and suggest that the proposed estimator is more robust compared to quasi-maximum likelihood estimator. An application to IBM volume duration shows that the performance of the proposed estimation is better than quasi-maximum likelihood estimation in forecasting.

Suggested Citation

  • Wanbo Lu & Rui Ke, 2019. "A generalized least squares estimation method for the autoregressive conditional duration model," Statistical Papers, Springer, vol. 60(1), pages 123-146, February.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:1:d:10.1007_s00362-016-0830-3
    DOI: 10.1007/s00362-016-0830-3
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    References listed on IDEAS

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