Determining analogies based on the integration of multiple information sources

Forecasting approaches that exploit analogies require the grouping of analogous time series as the first modeling step; however, there has been limited research regarding the suitability of different segmentation approaches. We argue that an appropriate analytical segmentation stage should integrate and trade off different available information sources. In particular, it should consider the actual time series patterns, in addition to the variables that characterize the drivers behind the patterns observed. The simultaneous consideration of both information sources, without prior assumptions regarding the relative importance of each, leads to a multicriteria formulation of the segmentation stage. Here, we demonstrate the impact of such an adjustment to segmentation on the final forecasting accuracy of the cross-sectional multi-state Kalman filter. In particular, we study the relative merits of single and multicriteria segmentation stages for a simulated data set with a range of noise levels. We find that a multicriteria approach consistently achieves a more reliable recovery of the original clusters, and this feeds forward to an improved forecasting accuracy across short forecasting horizons. We then use a US data set on income tax liabilities to verify that this result generalizes to a real-world setting.     

Time Series Analysis of Repeated Surveys: The State–space Approach

Cross sectional estimates from repeated surveys form a time series { y_t }. These estimates can be viewed as the sum y _t = Y _t + e _t of two processes, { Y _t }, the population process and { e _t }, the survey error process. Serial correlations in the latter series are usually present, mainly due to sample overlap. Other sources of data such as censuses, administrative records and demographic population counts are also available. The state–space modelling approach to the analysis of repeated surveys allows combining information from different sources, incorporating benchmarking constraints in a natural way. Results from these methods seem to compare favourably with those from X-11-ARIMA in filtering out survey errors.     

Invertible and non-invertible information sets in linear rational expectations models

Rational expectations solutions are usually derived by assuming that all state variables relevant to forward-looking behaviour are directly observable, or that they are “…an invertible function of observables” (Mehra and Prescott, 1980). Using a framework that nests linearised DSGE models, we give a number of results useful for the analysis of linear rational expectations models with restricted information sets. We distinguish between instantaneous and asymptotic invertibility, and show that the latter may require significantly less information than the former. We also show that non-invertibility of the information set can have significant implications for the time series properties of economies.     

一个基于无迹卡尔曼滤波的传热反问题求解方法

准确估计材料的物性参数、初始条件、边界条件等对于热能工程领域具有重要意义。导热反问题(Inverse HeatConduction Problems,IHCP)提供了一种获取上述参数的有效途径,IHCP的成功应用在一定程度上取决于反演算法的效率。文章提出一种同时考虑被反演对象演化信息和测量信息的反演模型,原始IHCP问题被转化为一个状态-空间问题,无迹卡尔曼滤波(Unscented Kalman Filter,UKF)方法被发展用于求解该反演模型。数值模拟结果表明,该算法能够改善反演精度,而且,该算法的执行并不要求计算目标函数的梯度信息、雅克比矩阵和黑森矩阵,有效地降低了计算的复杂性与代价,从而为求解IHCP问题提供了一个有效途径。     

Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set

We evaluate conditional predictive densities for US output growth and inflation using a number of commonly-used forecasting models that rely on large numbers of macroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly-used normality assumption fit actual realizations out-of-sample. Our focus on predictive densities acknowledges the possibility that, although some predictors can cause point forecasts to either improve or deteriorate, they might have the opposite effect on higher moments. We find that normality is rejected for most models in some dimension according to at least one of the tests we use. Interestingly, however, combinations of predictive densities appear to be approximated correctly by a normal density: the simple, equal average when predicting output growth, and the Bayesian model average when predicting inflation.     

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.     

Forecasting macroeconomic variables using collapsed dynamic factor analysis

We explore a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis. Key economic variables are modeled jointly with principal components from a large time series panel of macroeconomic indicators using a multivariate unobserved components time series model. When the key economic variables are observed at a low frequency and the panel of macroeconomic variables is at a high frequency, we can use our approach for both nowcasting and forecasting purposes. Given a dynamic factor model as the data generation process, we provide Monte Carlo evidence of the finite-sample justification of our parsimonious and feasible approach. We also provide empirical evidence for a US macroeconomic dataset. The unbalanced panel contains quarterly and monthly variables. The forecasting accuracy is measured against a set of benchmark models. We conclude that our dynamic factor state space analysis can lead to higher levels of forecasting precision when the panel size and time series dimensions are moderate.     

Extracting a common stochastic trend: Theory with some applications

This paper investigates the statistical properties of estimators of the parameters and unobserved series for state space models with integrated time series. In particular, we derive the full asymptotic results for maximum likelihood estimation using the Kalman filter for a prototypical class of such models—those with a single latent common stochastic trend. Indeed, we establish the consistency and asymptotic mixed normality of the maximum likelihood estimator and show that the conventional method of inference is valid for this class of models. The models we explicitly consider comprise a special–yet useful–class of models that may be employed to extract the common stochastic trend from multiple integrated time series. Such models can be very useful to obtain indices that represent fluctuations of various markets or common latent factors that affect a set of economic and financial variables simultaneously. Moreover, our derivation of the asymptotics of this class makes it clear that the asymptotic Gaussianity and the validity of the conventional inference for the maximum likelihood procedure extends to a larger class of more general state space models involving integrated time series. Finally, we demonstrate the utility of this class of models extracting a common stochastic trend from three sets of time series involving short- and long-term interest rates, stock return volatility and trading volume, and Dow Jones stock prices.     

A Unified View of Signal Extraction, Benchmarking, Interpolation and Extrapolation of Time Series

Time series data are often subject to statistical adjustments needed to increase accuracy, replace missing values and/or facilitate data analysis. The most common adjustments made to original observations are signal extraction (e.g. smoothing), benchmarking, interpolation and extrapolation. In this article, we present a general dynamic stochastic regression model, from which most of these adjustments can be performed, and prove that the resulting generalized least square estimator is minimum variance linear unbiased. We extend current methods to include those cases where the signal follows a mixed model (deterministic and stochastic components) and the errors are autocorrelated and heteroscedastic.     

小型无人机导航系统的探索与研究

针对执行特定海域巡航的小型无人机,提出了GPS/INS组合导航系统的解决方案。首先介绍导航系统的算法和原理,根据任务需求,给出硬件构成及软件核心程序流程,通过仿真,证明在组合系统中采取PID控制以及卡尔曼滤波算法大大提高了导航的精度。     

供应链中牛鞭效应的模型与分析

对研究牛鞭效应时经常讨论到的三种模型:系统动力学模型、自回归分析的AR(1)模型及Kalman滤波器(KF)模型进行了分析和比较。     

Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection

This paper proposes an estimation strategy that exploits recent non-parametric panel data methods that allow for a multifactor error structure and extends a recently proposed data-driven model-selection procedure, which has its roots in cross validation and aims to test whether two competing approximate models are equivalent in terms of their expected true error. We extend this procedure to a large panel data framework by using moving block bootstrap resampling techniques in order to preserve cross-sectional dependence in the bootstrapped samples. Such an estimation strategy is illustrated by revisiting an analysis of international technology diffusion. Model selection procedures clearly conclude in the superiority of a fully non-parametric (non-additive) specification over parametric and even semi-parametric (additive) specifications. This work also refines previous results by showing threshold effects, non-linearities, and interactions that are obscured in parametric specifications and which have relevant implications for policy.     

北斗/CDMA组合导航数据融合及其在军事物流中的应用研究

提出了一种新的组合导航定位技术-北斗/CDMA导航定位,进一步研究了如何利用数据融合技术提高定位精度与可靠性。设计了一种简化的联邦卡尔曼滤波器并给出了相应的滤波算法流程。最后提出了北斗/CDMA组合导航定位技术在军事物流监控调度系统中的应用解决方案。     

发电机组励磁参数测试及仿真

文章介绍了发电机组励磁参数测试原理,确定了测试步骤,并依托某电厂实体工程,对发电机组励磁参数进行了现场测试,根据AVR和PSS模型进行了仿真计算,将现场测试结果与仿真计算结果进行比较,表明模型具有较高的精度和稳定性。     

Growth, cycles and convergence in US regional time series

This article reports the results of fitting unobserved components (structural) time series models to data on real income per capita in eight regions of the United States. The aim is to establish stylised facts about cycles and convergence. It appears that while the cycles are highly correlated, the two richest regions have been diverging from the others in recent years. A new model is developed in order to characterise the converging behaviour of the six poorest regions. The model combines convergence components with a common trend and cycles. These convergence components are formulated as a second-order error correction mechanism which allows temporary divergence while imposing eventual convergence. After fitting the model, the implications for forecasting are examined. Finally, the use of unit root tests for testing convergence is critically assessed in the light of the stylised facts obtained from the fitted models.