Yield Curve Forecasting with the Burg Model

We introduce a versatile and robust model that may help policymakers, bond portfolio managers and financial institutions to gain insight into the future shape of the yield curve. The Burg model forecasts a 20‐day yield curve, which fits a pth‐order autoregressive (AR) model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson–Durbin recursion. Then, it uses an infinite impulse response prediction error filter. Results are striking when the Burg model is compared to the Diebold and Li model: the model not only significantly improves accuracy, but also its forecast yield curves stick to the shape of observed yield curves, whether normal, humped, flat or inverted. Copyright © 2016 John Wiley & Sons, Ltd.     

伯格谱估计方法在VSAR中的应用

针对目前速度合成孔径雷达（ＶＳＡＲ）中速度分辨力不高和天线数目过多的问题进行改进,以便对慢动目标进行检测,采用伯格法对ＶＳＡＲ信号处理中的相位信息进行处理,以提取速度信息,从而取代传统的经典谱估计方法。仿真结果表明,使用伯格法可以获得更高的速度分辨力和方位分辨力。同时,柏格法的使用可以降低天线数目和整个天线阵列的总长度,从而降低硬件的复杂度。     

基于Burg算法与卡尔曼滤波的有色噪声状态估计

探讨一种有色噪声卡尔曼滤波算法。基于静态环境下多传感器的噪声信号,借助Burg算法和自回归模型,获得可在线估计噪声信号的噪声模型,进而结合经典卡尔曼滤波算法,获得可估计系统状态的有色噪声卡尔曼滤波模型。数值实验结果比较显示该模型对系统的状态估计精度高、噪声抑制能力强。