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Statistical Papers

, Volume 60, Issue 1, pp 123–146 | Cite as

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

  • Wanbo LuEmail author
  • Rui Ke
Regular Article

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 \(\sqrt{T}\)—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.

Keywords

Autoregressive conditional duration model Generalized least squares estimator Quasi-maximum likelihood estimator Monte Carlo simulation 

Mathematics Subject Classification

62F12 62M10 

Notes

Acknowledgments

Wanbo Lu’s research is sponsored by the National Science Foundation of China (71101118) and the Program for New Century Excellent Talents in University (NCET-13-0961) and the Fundamental Research Funds for the Central Universities (JBK150501, JBK16062) in China. Rui Ke’s research is sponsored by the Fundamental Research Funds for the Central Universities (JBK1507102) in China.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of StatisticsSouthwestern University of Finance and EconomicsChengduChina

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