在CKLS(Chan K,Kalolyi F,Longstaff F,et al.An empirical comparison of alternative models of the short-term interest rate,Journal of Finance,1992,47(3):1209—1227)的扩展框架下,以中国银行间债券市场国债回购利率R007的日数...在CKLS(Chan K,Kalolyi F,Longstaff F,et al.An empirical comparison of alternative models of the short-term interest rate,Journal of Finance,1992,47(3):1209—1227)的扩展框架下,以中国银行间债券市场国债回购利率R007的日数据为样本,对五个短期利率模型进行了最优估计、模型选择和参数偏差校正,并将估计得到的最优模型的参数用于对认股权证定价模型.对长江电力认股权证(CWB1)的实际估价结果表明,在认股权证定价模型中允许利率的随机变动,用本文所得到的最优动态利率模型估计短期利率,与假定利率固定不变的情形相比,可以得到与市场价格更为接近的权证估价结果,相对估价误差也要小得多.展开更多
The occurrence of lightning-induced forest fires during a time period is count data featuring over-dispersion （i.e., variance is larger than mean） and a high frequency of zero counts. In this study, we used six gene...The occurrence of lightning-induced forest fires during a time period is count data featuring over-dispersion （i.e., variance is larger than mean） and a high frequency of zero counts. In this study, we used six generalized linear models to examine the relationship between the occurrence of lightning-induced forest fires and meteorological factors in the Northern Daxing＇an Mountains of China. The six models included Poisson, negative binomial （NB）, zero- inflated Poisson （ZIP）, zero-inflated negative binomial （ZINB）, Poisson hurdle （PH）, and negative binomial hurdle （NBH） models. Goodness-of-fit was compared and tested among the six models using Akaike information criterion （AIC）, sum of squared errors, likelihood ratio test, and Vuong test. The predictive performance of the models was assessed and compared using independent validation data by the data-splitting method. Based on the model AIC, the ZINB model best fitted the fire occurrence data, followed by （in order of smaller AIC） NBH, ZIP, NB, PH, and Poisson models. The ZINB model was also best for pre- dicting either zero counts or positive counts （〉1）. The two Hurdle models （PH and NBH） were better than ZIP, Poisson, and NB models for predicting positive counts, but worse than these three models for predicting zero counts. Thus, the ZINB model was the first choice for modeling the occurrence of lightning-induced forest fires in this study, which implied that the excessive zero counts of lightning- induced fires came from both structure and sampling zeros.展开更多
文摘在CKLS(Chan K,Kalolyi F,Longstaff F,et al.An empirical comparison of alternative models of the short-term interest rate,Journal of Finance,1992,47(3):1209—1227)的扩展框架下,以中国银行间债券市场国债回购利率R007的日数据为样本,对五个短期利率模型进行了最优估计、模型选择和参数偏差校正,并将估计得到的最优模型的参数用于对认股权证定价模型.对长江电力认股权证(CWB1)的实际估价结果表明,在认股权证定价模型中允许利率的随机变动,用本文所得到的最优动态利率模型估计短期利率,与假定利率固定不变的情形相比,可以得到与市场价格更为接近的权证估价结果,相对估价误差也要小得多.
基金funded by Asia–Pacific Forests Net(APFNET/2010/FPF/001)National Natural Science Foundation of China(Grant No.31400552)
文摘The occurrence of lightning-induced forest fires during a time period is count data featuring over-dispersion （i.e., variance is larger than mean） and a high frequency of zero counts. In this study, we used six generalized linear models to examine the relationship between the occurrence of lightning-induced forest fires and meteorological factors in the Northern Daxing＇an Mountains of China. The six models included Poisson, negative binomial （NB）, zero- inflated Poisson （ZIP）, zero-inflated negative binomial （ZINB）, Poisson hurdle （PH）, and negative binomial hurdle （NBH） models. Goodness-of-fit was compared and tested among the six models using Akaike information criterion （AIC）, sum of squared errors, likelihood ratio test, and Vuong test. The predictive performance of the models was assessed and compared using independent validation data by the data-splitting method. Based on the model AIC, the ZINB model best fitted the fire occurrence data, followed by （in order of smaller AIC） NBH, ZIP, NB, PH, and Poisson models. The ZINB model was also best for pre- dicting either zero counts or positive counts （〉1）. The two Hurdle models （PH and NBH） were better than ZIP, Poisson, and NB models for predicting positive counts, but worse than these three models for predicting zero counts. Thus, the ZINB model was the first choice for modeling the occurrence of lightning-induced forest fires in this study, which implied that the excessive zero counts of lightning- induced fires came from both structure and sampling zeros.
