【摘要】 为了更好地捕捉计数时间序列中观察到的非对称性和结构波动特征,本研究深入探讨了分位数回归(QR)方法在分析和预测门限整数值时间序列模型中的应用..具体而言,我们聚焦于带有对称性、非对称性和污染新息的一阶自激励门限整数值自回归过程(SETINAR(2,1))中的参数估计.我们在一定的正则条件下建立了估计量的渐近性质.蒙特卡洛模拟显示, QR方法在估计性能上优于条件最小二乘法(CLS).此外,我们通过对匹兹堡的盗窃事件和CAD毒品报警次数进行分位数回归估计和预测,验证了所提方法在不同数据异质性水平下的稳健性和有效性.更多还原

【Abstract】 To better capture the characteristics of asymmetry and structural fluctuations observed in count time series, this study delves into the application of the quantile regression(QR)method for analyzing and forecasting nonlinear integer-valued time series exhibiting a piecewise phenomenon. Specifically, we focus on the parameter estimation in the first-order Self-Exciting Threshold Integer-valued Autoregressive(SETINAR(2,1)) process with symmetry, asymmetry, and contaminated innovations. We establish the asymptotic properties of the estimator under certain regularity conditions. Monte Carlo simulations demonstrate the superior performance of the QR method compared to the conditional least squares(CLS) approach. Furthermore, we validate the robustness of the proposed method through empirical quantile regression estimation and forecasting for larceny incidents and CAD drug call counts in Pittsburgh, showcasing its effectiveness across diverse levels of data heterogeneity.更多还原