Forecasting with Micro Panels: The Case of Health Care Costs

Micro panels characterized by large numbers of individuals observed over a short time period provide a rich source of information, but as yet there is only limited experience in using such data for forecasting. Existing simulation evidence supports the use of a fixed‐effects approach when forecasting but it is not based on a truly micro panel set‐up. In this study, we exploit the linkage of a representative survey of more than 250,000 Australians aged 45 and over to 4 years of hospital, medical and pharmaceutical records. The availability of panel health cost data allows the use of predictors based on fixed‐effects estimates designed to guard against possible omitted variable biases associated with unobservable individual specific effects. We demonstrate the preference towards fixed‐effects‐based predictors is unlikely to hold in many practical situations, including our models of health care costs. Simulation evidence with a micro panel set‐up adds support and additional insights to the results obtained in the application. These results are supportive of the use of the ordinary least squares predictor in a wide range of circumstances. Copyright © 2016 John Wiley & Sons, Ltd.     

Revisiting Targeted Factors

This paper proposes new methods for ‘targeting’ factors estimated from a big dataset. We suggest that forecasts of economic variables can be improved by tuning factor estimates: (i) so that they are both more relevant for a specific target variable; and (ii) so that variables with considerable idiosyncratic noise are down‐weighted prior to factor estimation. Existing targeted factor methodologies are limited to estimating the factors with only one of these two objectives in mind. We therefore combine these ideas by providing new weighted principal components analysis (PCA) procedures and a targeted generalized PCA (TGPCA) procedure. These methods offer a flexible combination of both types of targeting that is new to the literature. We illustrate this empirically by forecasting a range of US macroeconomic variables, finding that our combined approach yields important improvements over competing methods, consistently surviving elimination in the model confidence set procedure. Copyright © 2016 John Wiley & Sons, Ltd.     

A Time‐Simultaneous Prediction Box for a Multivariate Time Series

A sample‐based method in Kolsrud (Journal of Forecasting 2007; 26 (3): 171–188) for the construction of a time‐simultaneous prediction band for a univariate time series is extended to produce a variable‐ and time‐simultaneous prediction box for a multivariate time series. A measure of distance based on the L_∞ ‐norm is applied to a learning sample of multivariate time trajectories, which can be mean‐ and/or variance‐nonstationary. Based on the ranking of distances to the centre of the sample, a subsample of the most central multivariate trajectories is selected. A prediction box is constructed by circumscribing the subsample with a hyperrectangle. The fraction of central trajectories selected into the subsample can be calibrated by bootstrap such that the expected coverage of the box equals a prescribed nominal level. The method is related to the concept of data depth, and thence modified to increase coverage. Applications to simulated and empirical data illustrate the method, which is also compared to several other methods in the literature adapted to the multivariate setting. Copyright © 2015 John Wiley & Sons, Ltd.     

信息可视化和可视分析：挑战与机遇——北戴河信息可视化战略研讨会总结报告

由天津大学软件学院发起并召集,东北大学秦皇岛分校和燕山大学承办的信息可视化战略研讨会于2012年6月9-10日在北戴河成功举行．本文总结了此次会议的讨论内容和达成的共识,对当前信息可视化和可视分析领域的挑战和国际上这方面的研究方向做了大致的分析,并对今后信息可视化和可视分析的大方向提出一些建议,以及对具体行动提出一系列倡议．     

How Informative are the Subjective Density Forecasts of Macroeconomists?

In this paper, we propose a framework to evaluate the subjective density forecasts of macroeconomists using micro data from the euro area Survey of Professional Forecasters (SPF). A key aspect of our analysis is the use of evaluation measures which take account of the entire predictive densities, and not just the probability assigned to the outcome that occurs. Overall, we find considerable heterogeneity in the performance of the surveyed densities at the individual level. However, it is hard to exploit this heterogeneity and improve aggregate performance by trimming poorly performing forecasters in real time. Relative to a set of simple benchmarks, density performance is somewhat better for GDP growth than for inflation, although in the former case it diminishes substantially with the forecast horizon. In addition, we report evidence of an improvement in the relative performance of expert densities during the recent period of macroeconomic volatility. However, our analysis also reveals clear evidence of overconfidence or neglected risks in expert probability assessments, as reflected in frequent occurrences of events which are assigned a zero probability. Copyright © 2014 John Wiley & Sons, Ltd.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

基于音乐基因的乐谱存储模型

本文提出一种基于音乐基因的乐谱存储模型S-MusicXML.将乐谱的存储和处理的基本单位由音阶提升到基因,有利于通过数据挖掘技术对音乐内涵的挖掘和存储.定义了旋律基因等概念,并通过实验进一步分析了挖掘音乐基因比挖掘音乐频繁模式更有优势.     

基于移动设备云迁移的节能决策算法

随着云计算的兴起,云迁移计算开始成为移动设备获取计算资源和降低功耗的有效方式.云迁移的主要想法是将移动终端的复杂任务经由无线网络迁移到云端执行,然后再接收计算结果.然而,无线网络的不稳定性和数据传输的高功耗限制了云迁移计算在移动设备中的应用.不同于已有工作,本文通过引入数据压缩的方法完善了云迁移计算决策模型,并且基于对未来时段网络期望的预测,提出了一种节能迁移计算决策算法——EPVAD.基于实际的3G网络带宽数据和开发测试平台,实验结果显示:EPVAD算法的节能效果较同类算法平均优14.9%,并且算法自身的系统开销可忽略.     

一种基于聚类分析的数值积分新算法

通过聚类分析找出一般数值求积公式的被积函数出现数值信息波动较大的区域,自适应地采用相应的高精度公式,构造出高精度的自适应数值求积公式.数值试验结果表明,这种新数值求积算法较一般数值求积公式,能显著地减少数据存储量和计算量,提高数值求积公式的稳定性和数值积分的精度.     

改进的多方向融合图像插值算法

为了解决数字图像插值过程中出现的边缘模糊和锯齿现象,提出一种改进的多方向融合图像插值算法.该算法将图像分为平坦区域和边缘区域,平坦区域采用双线性插值算法进行插值,边缘区域采用多方向融合算法进行插值.实验结果表明,该算法运算复杂度小,其结果在主观视觉效果和客观指标方面都有很大的改善,且适用于任意倍数的插值放大.