自适应Kalman滤波在地表沉降动态预报中的应用

开采沉陷是煤层采出后引起的一个时间和空间过程。随着工作面的推进,不同时间的回采工作面与地表点的相对位置不同,开采对地表点的影响也不同。本文以运用自适应Kalman理论为基础,对矿区地表观沉降进行动态预报,取得较好的成果。     

Modelling & Controlling Monetary and Economic Identities with Constrained State Space Models

The paper presents a method for modelling and controlling time series with identity structures. The approach is presented in the context of monetary targeting where the monetary identity (e.g. reserve money equals net foreign assets plus domestic credit) is modelled using a constrained state space model and next‐period changes in domestic credit (policy variable) are estimated to reach the target level of reserve money. The constrained modelling ensures that aggregation and identity relations among items are dynamically satisfied during estimation, leading to more accurate forecasting and targeting. Applications to Germany, UK and USA show that the constrained state space model provides significant improvements in targeting and forecasting performance over the AR(1) benchmark and the unconstrained model. Reduction in the mean square error of targeting over AR(1) is in the range of 76–95% for the three countries while the gain in targeting efficiency over unconstrained modelling is between 21% and 55%. Beyond monetary targeting, the method has wide application to the dynamic modelling and control of economic and financial time series with identity and aggregation constraints (e.g. balance of payment, national income, purchasing power parity, company balance sheet).     

基于HCMAC卡尔曼滤波器在组合导系统中的应用

针对经典卡尔曼滤波器要求组合导航系统的动态模型和观测模型的噪声统计特性已知,而组合导航系统的噪声具有非先验性的问题,利用超闭球小脑神经网络（Hyperbal Cerebel ar Model Articulation Control er,HCMAC）良好的非线性逼近能力、泛化能力和自学习能力,设计HCMAC辅助卡尔曼滤波器,并应用于组合导航系统。仿真试验结果表明,该辅助算法与经典卡尔曼滤波算法相比较,精度提高了2倍,收敛时间缩短近200s,且有效地克服了传统神经网络学习速度慢、泛化能力弱的缺点,使系统具有自适应能力以应付动态环境的扰动,增强了组合导航系统的鲁棒性。     

几种非线性滤波算法的性能分析

非线性随机动态系统的滤波问题是一类经常遇到的实际应用问题,本文分析了扩展卡尔曼(EKF)、无迹卡尔曼滤波(UKF)和粒子滤波(PF)这三种非线性滤波算法的基本原理和特点以及适应的条件。并通过一个强非线性系统的实验仿真,验证了各自算法的性能。     

MIMO系统中基于卡尔曼滤波的信道预测算法研究

多输入多输出(MIMO)技术是LTE系统中的关键技术之一,能够在不增加带宽的情况下成倍地提高了通信系统的容量和频谱利用率。本文提出了一种多用户MIMO下行链路中基于卡尔曼滤波的信道预测算法。在实际传输时,发射端根据接收端反馈回来的信道信息预测出下一时刻的下行信道信息,仿真结果验证了所提方法的有效性。     

实时OD估计中卡尔曼滤波算法研究

ITS研究中，动态OD矩阵估计是交通动态分配的关键因素。针对OD矩阵难以获得的问题，回顾动态OD矩阵估计理论的发展历程，明确该领域研究中的三个关键问题：状态变量的选择、系统方程的确定、OD量与路段流量的关系（分配矩阵）。在此基础上介绍一种基于历史OD矩阵数据的估计方法，卡尔曼滤波算法，为动态OD矩阵估计理论的进一步的研究奠定基础。     

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。     

The comparative forecast performance of univariate and multivariate models: an application to real interest rate forecasting

Does the use of information on the past history of the nominal interest rates and inflation entail improvement in forecasts of the ex ante real interest rate over its forecasts obtained from using just the past history of the realized real interest rates? To answer this question we set up a univariate unobserved components model for the realized real interest rates and a bivariate model for the nominal rate and inflation which imposes cointegration restrictions between them. The two models are estimated under normality with the Kalman filter. It is found that the error-correction model provides more accurate one-period ahead forecasts of the real rate within the estimation sample whereas the unobserved components model yields forecasts with smaller forecast variances. In the post-sample period, the forecasts from the bivariate model are not only more accurate but also have tighter confidence bounds than the forecasts from the unobserved components model.     

On new variance approximations for linear models with inequality constraints

In this paper, we examine the estimation of linear models subject to inequality constraints with a special focus on new variance approximations for the estimated parameters. For models with one inequality restriction, the proposed variance formulas are exact. The variance approximations proposed in this paper can be used in regression analysis, Kalman filtering, and balancing national accounts, when inequality constraints are to be incorporated in the estimation procedure.     

Penalized likelihood smoothing in robust state space models

In likelihood-based approaches to robustify state space models, Gaussian error distributions are replaced by non-normal alternatives with heavier tails. Robustified observation models are appropriate for time series with additive outliers, while state or transition equations with heavy-tailed error distributions lead to filters and smoothers that can cope with structural changes in trend or slope caused by innovations outliers. As a consequence, however, conditional filtering and smoothing densities become analytically intractable. Various attempts have been made to deal with this problem, reaching from approximate conditional mean type estimation to fully Bayesian analysis using MCMC simulation. In this article we consider penalized likelihood smoothers, this means estimators which maximize penalized likelihoods or, equivalently, posterior densities. Filtering and smoothing for additive and innovations outlier models can be carried out by computationally efficient Fisher scoring steps or iterative Kalman-type filters. Special emphasis is on the Student family, for which EM-type algorithms to estimate unknown hyperparameters are developed. Operational behaviour is illustrated by simulation experiments and by real data applications. Received: March 1998     

Inference of Dynamic Generalized Linear Models: On-Line Computation and Appraisal

The purpose of this paper is to provide a critical discussion on real-time estimation of dynamic generalized linear models. We describe and contrast three estimation schemes, the first of which is based on conjugate analysis and linear Bayes methods, the second based on posterior mode estimation, and the third based on sequential Monte Carlo sampling methods, also known as particle filters. For the first scheme, we give a summary of inference components, such as prior/posterior and forecast densities, for the most common response distributions. Considering data of arrivals of tourists in Cyprus, we illustrate the Poisson model, providing a comparative analysis of the above three schemes.     

Forecasting the New York State economy: The coincident and leading indicators approach

Currently there are no reliable summary indicators of the economic and fiscal condition of states and localities. This deficiency has hampered the efforts of policy makers at the sub-national level to monitor changes in the economic environment and predict how those changes will impact the fiscal health of governments. This paper attempts to fill this analytical vacuum by providing summary indicators of economic and fiscal health for New York State. The models developed are based on the single-index methodology developed by Stock and Watson [(1991). A probability model of the coincident economic indicators. In K. Lahiri and G. H. Moore (eds.), Leading economic indicators: new approaches and forecasting records (pp. 63–85). New York: Cambridge University Press]. This approach allows us to date New York business cycles and compare local cyclical behavior with the nation as a whole. We develop a leading index of economic indicators which predicts future movements in the coincident indicator. The Stock and Watson approach is used to create a fiscal indicator which acts as a summary indicator of revenue performance for New York. In addition, we explore the ability of our economic indicator series to predict future changes in state revenues. We find that changes in the leading indicator series have significant predictive power in forecasting changes in our revenue index.     

Comparison of Classical and Bayesian Approaches for Intervention Analysis

Intervention analysis has been recently the subject of several studies, mainly because real time series present a wide variety of phenomena that are caused by external and/or unexpected events. In this work, transfer functions are used to model different forms of intervention to the mean level of a time series. This is performed in the framework of state-space models. Two canonical forms of intervention are considered: pulse and step functions. Static and dynamic explanation of the intervention effects, normal and non-normal time series, detection of intervention, and study of the effect of outliers are also discussed. The performance of the two approaches is compared in terms of point and interval estimation through Monte Carlo simulation. The methodology was applied to real time series and showed satisfactory results for the intervention models used.     

Applying Kalman Filter on Solving Simultaneous Equations with Overidentifying Rank Restrictions: The Analysis of the Demand and Supply Model of Medium-size Scooter Market in Taiwan

Once the structure form of demand and supply is translated into areduced form, one can solve the reduced form with a state space modelof the Kalman filter method. This paper discusses an innovationrepresentation that links the structure form with the state space model.For the state space model, the recursive Expectation Maximization(EM) algorithm is used to estimate the parameters of a structure form.This research successfully applied the Kalman filter method to theestimation of the coefficients of simultaneous equations withoveridentifying rank restrictions. The empirical monthly data set camefrom the medium-size scooter market in Taiwan during 1987 to 1992period.     

Road to recovery: Managing an epidemic

Without widespread immunization, the road to recovery from the current COVID-19 lockdowns will optimally follow a path that finds the difficult balance between the social and economic benefits of liberty and the toll from the disease. We provide an approach that combines epidemiology and economic models, taking as given that the maximum capacity of the healthcare system imposes a constraint that must not be exceeded. Treating the transmission rate as a decreasing function of the severity of the lockdown, we first determine the minimal lockdown that satisfies this constraint using an epidemiology model with a homogeneous population to predict future demand for healthcare. Allowing for a heterogeneous population, we then derive the optimal lockdown policy under the assumption of homogeneous mixing and show that it is characterized by a bang–bang solution. Possibilities such as the capacity of the healthcare system increasing or a vaccine arriving at some point in the future do not substantively impact the dynamically optimal policy until such an event actually occurs.     

ERRORS IN VARIABLES IN ECONOMETRICS: NEW DEVELOPMENTS AND RECURRENT THEMES*

The paper reviews some old and new approaches to the analysis of linear models with errors in variables. The emphasis is on the identification problems that usually arise in errors–in–variables models and on the various types of additional information that econometricians have invoked to be able to estimate parameters consistently. The approaches discussed include instrumental variables, grouping, simultaneous equations, multiple equations and bounds on measurement error variances.     

A new business model for sustainable development: an exploratory study using the theory of constraints in Nordic organizations

The business model in use by many large companies has changed significantly from that of a decade ago and has incorporated environmental and social aspects of performance. However, given these achievements, are there unavoidable inhibitions in the contemporary business model that mean that even exemplar corporations cannot become sustainable? A key issue is consumption without limits, but can businesses do anything about this? The UK Government's Sustainable Development Commission identifies this as an issue. There is a need for an open‐minded consideration of business fundamentals to consider this issue as part of an identification of criteria for a sustainable business model. This is an account of an exploratory study undertaken to identify a new business model for sustainable development. The theory of constraints was adapted to provide the project's methodology that made use of semi‐structured interviews and secondary material. The cloverleaf account of sustainable development was used to structure and analyse sustainable development information. The organizations studied are all located in Nordic countries, since these countries are globally recognized for sustainable development achievements. Conclusions of the study acknowledge that, whilst specific new management tools and approaches of Nordic organizations do help sustainable development, it is the social context in which these organizations function that is a critical factor. Copyright © 2007 John Wiley & Sons, Ltd and ERP Environment.     

Inferential Causal Models: Integrating Quality & Quantity

In-depth data analysis plus statistical modeling can produce inferentialcausal models. Their creation thus combines aspects of analysis by close inspection,that is, reason analysis and cross-tabular analysis, with statistical analysis procedures,especially those that are special cases of the generalized linear model (McCullaghand Nelder, 1989; Agresti, 1996; Lindsey, 1997). This paper explores some of the roots of this combined method and suggests some new directions. An exercise clarifies some limitations of classic reason analysis by showing how the cross tabulation of variables with controls for test factors may produce better inferences. Then, given the cross tabulation of several variables, by explicating Coleman effect parameters, logistic regressions, and Poisson log-linear models, it shows how generalized linear models provide appropriate measures of effects and tests of statistical significance. Finally, to address a weakness of reason analysis, a case-control design is proposed and an example is developed.     

Trends, lead times and forecasting

The innovations representation for a local linear trend can adapt to long run secular and short term transitory effects in the data. This is illustrated by the theoretical power spectrum for the model which may possess considerable power at frequencies that might be associated with cycles of several years' duration. Whilst advantageous for short term forecasting, the model may be of less use when interest is in the underlying long run trend in the data. In this paper we propose a generalisation of the innovations representation for a local linear trend that is appropriate for representing short, medium and long run trends in the data.     

Collective Household Models: Principles and Main Results

In the traditional approach to consumer behaviour it is assumed that households behave as if they were single decision‐making units. This approach has methodological, empirical and welfare economic deficiencies. A valuable alternative to the traditional model is the collective approach to household behaviour. The collective approach explicitly takes account of the fact that multi‐person households consist of several members which may have different preferences. Among these household members, an intrahousehold bargaining process is assumed to take place. In addition to providing an introduction to the collective approach, this survey intends to show how different collective household models, each with their own aims and assumptions, are connected.