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nonlinearleastsquaresestimationoflogacdmodels
Chen Zhao1; Liu Wei2; Wang Christina Dan3; Wu Wuqing4; Wu Yaohua5
2018
Source Publicationactamathematicaeapplicataesinica
ISSN0168-9673
Volume034Issue:003Pages:516
AbstractThis 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.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/45224
Collection中国科学院数学与系统科学研究院
Affiliation1.School of Data Science,Fudan University
2.中国科学院数学与系统科学研究院
3.上海纽约大学
4.School of Business,Renmin University of China
5.Department of Statistics and Finance,University of Science and Technology of China
Recommended Citation
GB/T 7714
Chen Zhao,Liu Wei,Wang Christina Dan,et al. nonlinearleastsquaresestimationoflogacdmodels[J]. actamathematicaeapplicataesinica,2018,034(003):516.
APA Chen Zhao,Liu Wei,Wang Christina Dan,Wu Wuqing,&Wu Yaohua.(2018).nonlinearleastsquaresestimationoflogacdmodels.actamathematicaeapplicataesinica,034(003),516.
MLA Chen Zhao,et al."nonlinearleastsquaresestimationoflogacdmodels".actamathematicaeapplicataesinica 034.003(2018):516.
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