Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.     

一种基于重复博弈的频谱分配策略

针对动态频谱接入,提出了一种基于重复博弈的频谱共享分配策略。通过基于惩罚的防欺骗策略,参与用户将约束自己的行为以合作方式共享频谱、实现频谱分配,保证合作竞争的诚实性。仿真结果显示,该合作规则方法能够很好地降低用户间的干扰,防欺骗方式也能很好地约束参与人保持合作。     

创业投资辛迪加网络中控制权的演化博弈分析

创业投资的高风险性和信息不对称性使创业投资中控制权分配问题逐渐成为关系到风险投资项目成败的关键。本文运用演化博弈方法,分析控制权在辛迪加网络中两类创业投资家之间分配的博弈过程,得出在不同控制权分配比例下相应的演化稳定策略。在此结果上表明即期博弈中,创业投资家的私人收益和项目产出分配值的对比关系与控制权的分配密切相关。     

基于博弈论的开放环境下场景规约监控语义

在开放环境中,环境和系统本身行为的改变可能使得软件系统的实现不再满足原来规约,从而最终导致软件失效的发生.运行时监控是一种轻量级的形式化动态验证技术,已成为开放环境下检测软件失效的基本手段.针对基于场景的规约属性序列图,从博弈论的角度定义其多值监控语义:满足、无限可控、系统有限可控、系统紧急可控、环境有限可控、环境紧急可控和违例.通过多值监控语义的定义,监控器能够根据当前轨迹尽可能早地检测到系统失效或异常,并提供足够信息为失效的预防和恢复服务.实例研究表明了属性序列图多值监控语义的实用价值,并显示了其广泛的应用前景.     

Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis

In this study we evaluate the forecast performance of model‐averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold. First, the predictive likelihood does always better than the traditionally employed ‘marginal’ likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (RMSE) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995: Predictive model selection. Journal of the Royal Statistical Society B 57 : 247–262) with a hold‐out sample size of 25% to minimize the RMSE (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries, beating for almost all countries the AR(1) benchmark model. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models

This paper uses the dynamic factor model framework, which accommodates a large cross‐section of macroeconomic time series, for forecasting regional house price inflation. In this study, we forecast house price inflation for five metropolitan areas of South Africa using principal components obtained from 282 quarterly macroeconomic time series in the period 1980:1 to 2006:4. The results, based on the root mean square errors of one to four quarters ahead out‐of‐sample forecasts over the period 2001:1 to 2006:4 indicate that, in the majority of the cases, the Dynamic Factor Model statistically outperforms the vector autoregressive models, using both the classical and the Bayesian treatments. We also consider spatial and non‐spatial specifications. Our results indicate that macroeconomic fundamentals in forecasting house price inflation are important. Copyright © 2010 John Wiley & Sons, Ltd.     

基于混合先验分布的贝叶斯因子分析模型

针对现有因子分析模型不能充分融合模型参数信息问题,通过研究因子分析模型的统计结构,构造了参数的混合先验分布;利用贝叶斯定理证明了模型因子载荷阵的条件后验分布为矩阵t分布,协方差阵的条件后验分布为逆Wishart分布.实证研究表明:由于参数先验分布的作用,贝叶斯因子分析结果与传统的因子分析之间存在明显的差异.     

基于故障树分析和贝叶斯网络方法的液压举升机构故障诊断

为应对液压举升机故障原因复杂,诊断方法准确性不高等问题,提出一种基于故障树和贝叶斯网络的液压举升机故障诊断方法。首先建立液压举升机构故障树,然后将故障树转换为贝叶斯网络,利用三角模糊函数表示举升机底事件发生概率,得到底事件模糊概率,将其做为先验概率计算叶节点发生概率,进而求得根节点后验概率以及概率重要度,可快速诊断出故障点。     

针对无人潜航器的反潜策略研究

近年来无人潜航器对国家海洋国土安全带来的威胁逐渐增大,其低噪声特性和隐蔽入侵方式也给反潜行动带来极大困难。为此,提出了一种两阶段规划算法,用以学习优化反潜策略,在部署阶段,建立了基于不确定性马尔可夫决策过程的反潜资源分配模型,并设计了鲁棒性部署策略强化学习算法,用以求解不确定条件下分配模型的纳什均衡解。在搜索阶段,建立了基于部分可观察马尔可夫决策过程的搜潜模型,并设计了基于多智能体强化学习的搜潜策略学习算法。最后,通过仿真实验验证了本算法与比对算法相比具有更高的性能。