Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series

This article proposes new bootstrap procedures for detecting multiple persistence shifts in a time series driven by non-stationary volatility. The assumed volatility process can accommodate discrete breaks, smooth transition variation as well as trending volatility. We develop wild bootstrap sup-Wald tests of the null hypothesis that the process is either stationary [I(0)] or has a unit root [I(1)] throughout the sample. We also propose a sequential procedure to estimate the number of persistence breaks based on ordering the regime-specific bootstrap p-values. The asymptotic validity of the advocated procedures is established both under the null of stability and a variety of persistence change alternatives. A comparison with existing tests that assume homoskedasticity illustrates the finite sample improvements offered by our methods. An application to OECD inflation rates highlights the empirical relevance of the proposed approach and weakens the case for persistence change relative to existing procedures.     

A time series bootstrap procedure for interpolation intervals

A sieve bootstrap procedure for constructing interpolation intervals for a general class of linear processes is proposed. This sieve bootstrap provides consistent estimators of the conditional distribution of the missing values, given the observed data. A Monte Carlo experiment is used to show the finite sample properties of the sieve bootstrap and finally, the performance of the proposed method is illustrated with a real data example.     

Bootstrapping the Local Periodogram of Locally Stationary Processes

Abstract. Locally stationary processes are non‐stationary stochastic processes the second‐order structure of which varies smoothly over time. In this paper, we develop a method to bootstrap the local periodogram of a locally stationary process. Our method generates pseudo local periodogram ordinates by combining a parametric time and non‐parametric frequency domain bootstrap approach. We first fit locally a time varying autoregressive model so as to capture the essential characteristics of the underlying process. A locally calculated non‐parametric correction in the frequency domain is then used so as to improve upon the locally parametric autoregressive fit. As an application, we investigate theoretically the asymptotic properties of the bootstrap method proposed applied to the class of local spectral means, local ratio statistics and local spectral density estimators. Some simulations demonstrate the ability of our method to give accurate estimates of the quantities of interest in finite sample situations and an application to a real‐life data‐set is presented.     

Selecting the forgetting factor in subset autoregressive modelling

Abstract. Conventional methods to determine the forgetting factors in autoregressive (AR) models are mostly based on arbitrary or personal choices. In this paper, we present two procedures which can be used to select the forgetting factor in subset AR modelling. The first procedure uses the bootstrap to determine the value of a fixed forgetting factor. The second procedure starts from this base and applies the time-recursive maximum likelihood estimation to a variable forgetting factor. In one illustration using real exchange rates, we demonstrate the effect of the forgetting factor in subset AR modelling on ex ante forecasting of non-stationary time series. In a second illustration, these two procedures are applied to time-update forecasts for a stock market index. Subset AR models not including a forgetting factor act as a set of benchmarks for assessing ex ante forecasting performance, and consistently improved forecasting performance is demonstrated for these proposed procedures.     

Modelling the dynamics of the students’ academic performance in the German region of the North Rhine-Westphalia: an epidemiological approach with uncertainty

Student academic underachievement is a concern of paramount importance in Europe, where around 15% of the students in the last high school courses do not achieve the minimum knowledge academic requirement. In this paper, we propose a model based on a system of differential equations to study the dynamics of the students’ academic performance in the German region of the North Rhine-Westphalia. This approach is supported by the idea that both good and bad study habits, are a mixture of personal decisions and influence of classmates. This model allows us to forecast the student academic performance by means of confidence intervals over the next few years.     

基于通用智能卡的可信引导方案

为了解决传统的操作系统引导机制存在关键验证信息被绕过的风险和引导数据被篡改的安全隐患,基于可信计算理论,结合带光盘文件系统的智能卡技术,提出了基于通用智能卡的可信引导方案.在不改变智能卡和终端设备的硬件和固件结构的基础上,通过改造智能卡的存储数据和磁盘的引导数据,实现用户身份信息、智能卡和终端设备绑定的安全目标,将可信计算机制从开机加电扩展至应用层,确保操作系统的初始状态可信.通过安全性分析和性能分析,证明终端设备引导的安全性,并且在实际应用中得到了验证.     

制造误差的灰自助动态预报

综合考虑灰色系统理论和Bootstrap统计理论的信息预报特点,建立制造误差的灰自助动态预报模型GBM(1,1),以解决信息预报中存在的一些问题。GBM(1,1) 在灰微分建模时进行Bootstrap再抽样,更多地挖掘系统信息,从而更准确地预报系统真值及其分布区间的瞬态变化状况。在计算机仿真中,研究了各种随机误差系统例如正态分布、瑞利分布、均匀分布、三角分布以及混合分布等系统的预报问题,也涉及到一些系统误差例如上升趋势、下降趋势和周期趋势等误差的预报问题。在实际试验中,研究了滚动轴承套圈磨削圆度误差的预报问题。计算机仿真和试验研究表明,GBM(1,1)允许小的数据样本以及各种类型的随机误差与系统误差存在,预报的准确率可以达到95%以上。     

复杂电磁干扰环境下的C6000 DSP自举方法

对TMS320C620X／670XDSP的存储结构和特点进行分析,采用分两次生成ROM文件的方式实现TMS320C620X／670XDSP自举,并阐述了该自举方法的关键过程。通过该自举方法,程序在片内RAM中执行,预置参数和常量从外部ROM中读取。结合软件三模冗余,该自举方法可以防止复杂电磁干扰环境下单粒子翻转效应。该方法已得到实际应用,行之有效。     

A new approach of goodness-of-fit testing for exponentiated laws applied to the generalized Rayleigh distribution

Goodness-of-fit statistics are considered which are appropriate for generalized families of distributions, resulting from exponentiation. The tests employ a variation of the data determined by the cumulative distribution function of the corresponding non-generalized distribution. The resulting test, which makes use of the Mellin transform of the transformed data, is shown to be consistent. Simulation results for the case of the generalized Rayleigh distribution show that the proposed test compares well with standard methods based on the empirical distribution function.