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

Wanbo Lu () and Rui Ke ()
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Wanbo Lu: Southwestern University of Finance and Economics
Rui Ke: Southwestern University of Finance and Economics

Statistical Papers, 2019, vol. 60, issue 1, No 8, 123-146

Abstract: 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.

Keywords: Autoregressive conditional duration model; Generalized least squares estimator; Quasi-maximum likelihood estimator; Monte Carlo simulation; 62F12; 62M10 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00362-016-0830-3

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