Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.     

基于伪谱方法的自适应鲁棒实时再入制导律研究

为了提高飞行器再入制导的鲁棒性和自适应性,将再入弹道跟踪问题转化为再入弹道状态调节问题,得到了一个LTV系统最优控制问题.在此基础上,利用基于伪谱算法的最优反馈控制算法,设计了一种便于在线实现的自适应鲁棒再入制导律.仿真结果表明,这种再入制导律对于再入点误差不敏感,具有良好的鲁棒陛.在气动参数模型存在较大误差的情况下,依然能够取得较高的再入制导精度.它不需要显示增益调度和积分,并且在不同情况下控制结构和参数无需改变.     

A linear benchmark for forecasting GDP growth and inflation?

Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory‐based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation. Copyright © 2008 John Wiley & Sons, Ltd.     

月径流序列的多层递阶预报研究

月径流序列是一类具有周期变化的非平稳时间序列．本文根据其特点,建立了多层递阶预报模型,文中对此类非平稳时间序列的建模及预报方法进行了深入研究     

自然主义、先验论证与怀疑主义——论斯特劳森的自然主义转向

后期斯特劳森追随休谟和维特根斯坦的道路而转向了自然主义,他认为任何企图通过论证来支持或反驳怀疑主义挑战的做法都是无根据的,因为人类的某些基本概念如物质物体、他心等概念是人类理性的自然倾向,是人类思维不可逃避的认知模式的体现。不过,自然主义需要联合先验论证来阐明这些信念的基础地位,否则就会流于口号。总而言之,自然主义是一种探究真理的第三条路径,它试图约束人类理性的过分自负,从而展示真理的自然属性。     

类比论证的特征及其评价标准

尽管隐喻和类比都属于科学的逻辑,但隐喻是创造相似性的,因此,如何评价一个隐喻的好坏,尚无公认的标准。而类比论证的基本特征是基于(已接受的)相似性的推理,从而可以对它给出相关评价。Bartha近来提出类比论证的评价标准和程序,他提出的标准主要包括先验关联和泛化潜力两个方面的。以达尔文的人工选择和自然选择的类比为例表明Bartha的标准和程序可以用于说明科学知识的变化问题。     

关于G/G/1排队模型中排队长度的积分

考虑了一个具有中度正则变化服务时间的G/G/1模型.假设Q(t)是排队长度,则在忙期[0,l]上,Q(t)下方所扫过的面积也具有中度正则变化的性质.     

一类Rayleigh型p-laplace时滞微分方程的周期解

应用Manásevich-Mawhin重合度定理,研究了形如:(φp(x'(t)))'+f(t,x(t-τ(t)),x '(t-σ(t)))+β(t)g(t,x(t-τ(t)))=e(t),的Rayleigh型p-Laplace多时滞微分方程.在β(t)可变号情形下,得到了一个关于周期解存在性的结果.     

力学中一类变系数微分方程可调参数模型解法

结合一种非均匀控制参数，提出了一种变系数微分方程的可调整参数模型解法，可以很方便地处理由于物理上，几何上的非均匀，非线性而导致寂的变系数微分方程，应用这种模型可以用非常少的单元得到较满意的数值结果。