Optimal Recursive Estimation of Dynamic Models
本文用真实和模拟数据检验自适应技术追踪时变动态模型参数的效果，通过优化递推算法的跟踪系数（如折扣因子和学习率）来提升预测性能，对从事时间序列预测和自适应建模的研究者有用。
Abstract This article checks, using both real and simulated data, the effectiveness of modern adaptive techniques to track the parameters of time-varying dynamic models. The real case studies concern a bone marrow transplant data set published by Tong, the gas furnace model of Box and Jenkins, and two series of West German interest rates. Simulation studies focus on ARX models with smoothly and suddenly changing parameters. The general approach is to compare the fitting-forecasting performance of classical and adaptive methods, holding fixed the order of the models. At the methodological level, the basic step is taken by unifying known estimators, such as recursive least squares and Kalman filter, into a general algorithm. Next, the problem of optimal design of the tracking coefficients (such as discounting factors and learning rates), is solved by optimizing a quadratic functional based on one-step-ahead prediction errors. All applications show that adaptive modeling, based on the design and the optimization of recursive algorithms, leads to significant improvements of the forecasting performance.