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

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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.

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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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  1. School of Statistics, Southwestern University of Finance and Economics, Chengdu, 610074, China

    Wanbo Lu & Rui Ke

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  1. Wanbo Lu
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  2. Rui Ke
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Correspondence to Wanbo Lu.

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Lu, W., Ke, R. A generalized least squares estimation method for the autoregressive conditional duration model. Stat Papers 60, 123–146 (2019). https://doi.org/10.1007/s00362-016-0830-3

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