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 u...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.展开更多
基金The research was supported by the National Natural Science Foundation of China(11690014,11690015,10871188)the Research Funds of Renmin University of China(No.16XNB025)the Social Science Foundation of Beijing(No.17GLB022)
文摘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.
