Forecasting With the Standardized Self‐Perturbed Kalman Filter

We propose and study the finite‐sample properties of a modified version of the self‐perturbed Kalman filter of Park and Jun (Electronics Letters 1992; 28 : 558–559) for the online estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is weighted by the estimate of the measurement error variance. This avoids the calibration of a design parameter as the perturbation term is scaled by the amount of uncertainty in the data. It is shown by Monte Carlo simulations that this perturbation method is associated with a good tracking of the dynamics of the parameters compared to other online algorithms and to classical and Bayesian methods. The standardized self‐perturbed Kalman filter is adopted to forecast the equity premium on the S&P 500 index under several model specifications, and determines the extent to which realized variance can be used to predict excess returns. Copyright © 2016 John Wiley & Sons, Ltd.     

利用改进的卡尔曼滤波器预测退化设备可靠性

针对退化设备可靠性预测的问题,采用加入次优渐消因子的卡尔曼滤波器预测设备未来时刻退化量均值,进而预测未来时刻的可靠性。本文给出了退化设备的可靠性预测方法和具体算法,这可为确定停机维护时间、维修费用等提供依据,具有重要意义。仿真实验验证了该方法的有效性。     

利用改进的卡尔曼滤波器预测退化设备可靠性

针对退化设备可靠性预测的问题,采用加入次优渐消因子的卡尔曼滤波器预测设备未来时刻退化量均值,进而预测未来时刻的可靠性。本文给出了退化设备的可靠性预测方法和具体算法,这可为确定停机维护时间、维修费用等提供依据,具有重要意义。仿真实验验证了该方法的有效性。     

中国的潜在产出、产出缺口及产量--通货膨胀交替关系--基于"Kalman滤波"方法的研究

本文介绍并运用“Kalman滤波”方法估计1979-2004年间我国的潜在经济增长率和产出缺口,然后根据这些结果检验了我国经济增长与通货膨胀率之间的交替关系、社会的通货膨胀预期对经济的影响、决定产出缺口大小的影响因素等,最后利用研究结果对我国2005年的经济发展进行了预测。     

基于卡尔曼滤波和增量PID的双向DC-DC变换器

利用单片机控制锂电池的充放电以及升压和降压拓扑电路构成高效双向DC-DC变换器电路以提高能源利用率。单片机通过控制加法器和运算放大电路反馈端数字电位器阻值以实现实现输出电流步进可调,通过PID算法控制降压电路的反馈电压微调输出电压值提高输出电压稳定性,采用电流传感器和电压衰减电路,并结合单片机和卡尔曼滤波算法提高实时测量系统状态电压、电流测量精度。     

一种人工神经网络样本数据的对数螺旋线扰动方法

通过构建面向聚类的隐私保护数据扰动模型,利用对数螺线对原始数据进行扰动隐藏,维持原始数据的邻域关系稳定,实现数据集聚类可用性的有效维护;进一步提高BP神经网络的收敛速度,并且能够有效地避免数据隐私泄露,同时维持输出结果的可用性。     

Estimating restricted structural change models

This paper considers issues related to multiple structural changes, occurring at unknown dates, in the linear regression model when restrictions are imposed on the parameters. This includes, for example, the important special case where different nonadjacent regimes are the same. The estimates are constructed as global minimizers of the restricted sum of squared residuals and we provide an extension of the algorithm discussed in Bai and Perron [2003b, Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18, 1–22] to efficiently compute them. We show that the estimates of the break dates have the same asymptotic properties with or without the restrictions imposed; that is, in large samples, there is no efficiency gain from imposing valid restrictions as far as the estimates of the break dates are concerned. Of course, efficiency gains occur for the other parameters of the model. Simulations show that in small samples, all parameters are more efficiently estimated using the restrictions. We also consider tests of the null hypothesis of no structural change. These are also more powerful when the restrictions are imposed. A Gauss code for all the procedures discussed in this paper is available from the authors.     

Intercept corrections and structural change

Analyses of forecasting that assume a constant, time-invariant data generating process (DGP), and so implicitly rule out structural change or regime shifts in the economy, ignore an aspect of the real world responsible for some of the more dramatic historical episodes of predictive failure. Some models may offer greater protection against unforeseen structural breaks than others, and various tricks may be employed to robustify forecasts to change. We show that in certain states of nature, vector autoregressions in the differences of the variables (in the spirit of Box-Jenkins time-series modelling), can outperform vector ‘equilibrium-correction’ mechanisms. However, appropriate intercept corrections can enhance the performance of the latter, albeit that reductions in forecast bias may only be achieved at the cost of inflated forecast error variances.     

Macroeconomic forecasting and structural change

The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real‐time out‐of‐sample exercise using a time‐varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time‐varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd.     

滤波技术在卫星精密定轨应用中的关键问题分析

本文在分析了卡尔曼滤波技术在卫星精密卫星定轨中应用优势后,结合实际指出了该技术在实践中存在的问题,并具体给出了相应的解决方法。这些方法给卫星定轨工程实践带来一定的借鉴作用。     

改进的卡尔曼滤波算法在行驶汽车状态估计中的应用现状

卡尔曼滤波及其改进方法在行驶车辆状态估计中有着广泛的应用,并取得良好的效果;本文针对自适应卡尔曼滤波算法、无轨迹卡尔曼滤波算法的应用等进行了综合性阐述。     

A unified theory of structural change

This paper uses dynamic general equilibrium and computational methods, inspired by the multi-sector growth model structure in Stephen Turnovsky's work, to develop a theory that unifies two of the traditional explanations of structural change: sector-biased technical change and non-homothetic preferences. The theory is based on an overlapping-generations growth model with endogenous technical change and non-homothetic preferences. An expanding-variety setup with two different R&D technologies, agricultural, and non-agricultural, is employed. The analysis, based on numerical simulations, shows that the biased technical change hypothesis finds most support in the data. It also points to production-side specific factors, such as asymmetries in cross-sector knowledge spillovers, as explanatory factors of the bias in technical change.     

Monitoring structural change in dynamic econometric models

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation‐type tests in a monitoring situation—given a history period for which a regression relationship is known to be stable, we test whether incoming data are consistent with the previously established relationship. Procedures based on estimates of the regression coefficients are extended in three directions: we introduce (a) procedures based on OLS residuals, (b) rescaled statistics and (c) alternative asymptotic boundaries. Compared to the existing tests our extensions offer ease of computation, improved size in finite samples for dynamic models and better power against certain alternatives, respectively. We apply our methods to three data sets, German M1 money demand, US labour productivity and S&P 500 stock returns. Copyright © 2005 John Wiley & Sons, Ltd.     

Testing for structural change in regression quantiles

Most studies in the structural change literature focus solely on the conditional mean, while under various circumstances, structural change in the conditional distribution or in conditional quantiles is of key importance. This paper proposes several tests for structural change in regression quantiles. Two types of statistics are considered, namely, a fluctuation type statistic based on the subgradient and a Wald type statistic, based on comparing parameter estimates obtained from different subsamples. The former requires estimating the model under the null hypothesis, and the latter involves estimation under the alternative hypothesis. The tests proposed can be used to test for structural change occurring in a pre-specified quantile, or across quantiles, which can be viewed as testing for change in the conditional distribution with a linear specification of the conditional quantile function. Both single and multiple structural changes are considered. We derive the limiting distributions under the null hypothesis, and show they are nuisance parameter free and can be easily simulated. A simulation study is conducted to assess the size and power in finite samples.     

The structural implications of technological change in the manufacturing sector

This paper uses a three-sector, pseudo-general equilibrium model to investigate the implications of different types of technological progress in the manufacturing sector. We show that the increased relative importance of services that has occurred could be the consequence of such rapid technological advance in manufacturing that the relative price decline of manufactured goods has reduced spending on manufactures versus services. Furthermore, we show that only labor-saving technological change in manufacturing can explain the growth in the relative importance of investment. This adds indirect support to other studies which have shown that technological advance in manufacturing has generally been of a labor-saving variety.The refereeing process of this paper was handled through Murray Brown. The authors are respectively Professor, Faculty of Commerce and Business Administration, University of British Columbia, and Assistant Professor, Faculty of Business, University of New Brunswick. They are grateful to the Canadian International Development Agency and to Nomura Asset Management International Limited for generous financial support. The authors are also grateful for the helpful comments of James Brander and two anonymous referees of this journal. However, responsibility for any errors that may remain is borne solely by the authors.     

Modelling metropolitan population growth and change: The IIPS simulator

This paper describes selected aspects of a large scale urban simulation project (IIPS). Emphasis is on design and empirical evaluation of a model of metropolitan population growth and change. Functions for determining ‘behavior’ of fertility and net-migration are specified and an important linkage between the IIPS population and land use models is illustrated. In a final section, the reader is introduced to a relatively unique computer sumulation-supervisor.     

Modelling the effect of climate change on prevalence of malaria in western Africa

Malaria is a leading cause of infectious disease and death worldwide. As a common example of a vector‐borne disease, malaria could be greatly affected by the influence of climate change. Climate impacts the transmission of malaria in several ways, affecting all stages of the disease's development. Using various weather‐related factors that influence climate change, this study utilizes statistical analysis to determine the effect of climate change on reported malaria rates in an African region with endemic malaria. It examines the relationship between malaria prevalence and climate in western Africa using spatial regression modeling and tests for correlation. Our analysis suggests that minimal correlation exists between reported malaria rates and climate in western Africa. This analysis further contradicts the prevailing theory that climate and malaria prevalence are closely linked and negates the idea that climate change will increase malaria transmission in this region.