Nonlinear Least Squares Estimation of Log-ACD Models

This paper studies a nonlinear least squares estimation method for the logarithmic autoregressive conditional duration (Log-ACD) model. We establish the strong consistency and asymptotic normality for our estimator under weak moment conditions suitable for applications involving heavy-tailed distrib...

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Veröffentlicht in:Acta Mathematicae Applicatae Sinica 2018-07, Vol.34 (3), p.516-533
Hauptverfasser: Chen, Zhao, Liu, Wei, Wang, Christina Dan, Wu, Wu-qing, Wu, Yao-hua
Format: Artikel
Sprache:eng
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Zusammenfassung:This paper studies a nonlinear least squares estimation method for the logarithmic autoregressive conditional duration (Log-ACD) model. We establish the strong consistency and asymptotic normality for our estimator under weak moment conditions suitable for applications involving heavy-tailed distributions. We also discuss inference for the Log-ACD model and Log-ACD models with exogenous variables. Our results can be easily translated to study Log-GARCH models. Both simulation study and real data analysis are conducted to show the usefulness of our results.
ISSN:0168-9673
1618-3932
DOI:10.1007/s10255-018-0766-6