中国洛阳牡丹文化节发展现状与对策研究——以第31届中国洛阳牡丹文化节为例

中国洛阳牡丹文化节的前身是始于1983年的洛阳牡丹花会,迄今已经连续举办了32届,尤其自2011年升格为国家级节会以来更是取得重大突破,其对地区经济社会发展的促进作用也日益凸显。然而,升级后中国洛阳牡丹文化节仍存在若干问题,突出表现在宣传工作、文化活动、节会瓶颈、旅游消费等方面。未来完善和提升牡丹文化节的对策主要有:加强创新、完善节会宣传模式;广泛参与、彰显民众主体地位;凝聚合力、突破节会发展瓶颈;积极引导、挖掘公众消费潜力等。     

Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations

This paper provides clear‐cut evidence that the out‐of‐sample VaR (value‐at‐risk) forecasting performance of alternative parametric volatility models, like EGARCH (exponential general autoregressive conditional heteroskedasticity) or GARCH, and Markov regime‐switching models, can be considerably improved if they are combined with skewed distributions of asset return innovations. The performance of these models is found to be similar to that of the EVT (extreme value theory) approach. The performance of the latter approach can also be improved if asset return innovations are assumed to be skewed distributed. The performance of the Markov regime‐switching model is considerably improved if this model allows for EGARCH effects, for all different volatility regimes considered. Copyright © 2014 John Wiley & Sons, Ltd.     

Forward Rates,Monetary Policy and the Economic Cycle

The short end of the yield curve incorporates essential information to forecast central banks' decisions, but in a biased manner. This article proposes a new method to forecast the Fed and the European Central Bank's decision rate by correcting the swap rates for their cyclical economic premium, using an affine term structure model. The corrected yields offer a higher out‐of‐sample forecasting power than the yields themselves. They also deliver forecasts that are either comparable or better than those obtained with a factor‐augmented vector autoregressive model, underlining the fact that yields are likely to contain at least as much information regarding monetary policy as a dataset composed of economic data series. Copyright © 2015 John Wiley & Sons, Ltd.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.     

Making the abstract concrete: The role of norms and values in experimental modeling

Experimental modeling is the construction of theoretical models hand in hand with experimental activity. As explained in Section 1, experimental modeling starts with claims about phenomena that use abstract concepts, concepts whose conditions of realization are not yet specified; and it ends with a concrete model of the phenomenon, a model that can be tested against data. This paper argues that this process from abstract concepts to concrete models involves judgments of relevance, which are irreducibly normative. In Section 2, we show, on the basis of several case studies, how these judgments contribute to the determination of the conditions of realization of the abstract concepts and, at the same time, of the quantities that characterize the phenomenon under study. Then, in Section 3, we compare this view on modeling with other approaches that also have acknowledged the role of relevance judgments in science. To conclude, in Section 4, we discuss the possibility of a plurality of relevance judgments and introduce a distinction between locally and generally relevant factors.     

Multicategory Purchase Incidence Models for Partitions of Product Categories

We analyze multicategory purchases of households by means of heterogeneous multivariate probit models that relate to partitions formed from a total of 25 product categories. We investigate both prior and post hoc partitions. We search model structures by a stochastic algorithm and estimate models by Markov chain Monte Carlo simulation. The best model in terms of cross‐validated log‐likelihood refers to a post hoc partition with two groups; the second‐best model considers all categories as one group. Among prior partitions with at least two category groups a five‐group model performs best. Effects on average basket value differ for the model with five prior category groups from those for the best‐performing model in 40% and 24% of the investigated categories for features and displays, respectively. In addition, the model with five prior category groups also underestimates total sales revenue across all categories by about 28%. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality‐forecasting models be associated with real‐world trends in health‐related variables? Does inclusion of health‐related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle‐related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models. Copyright © 2014 John Wiley & Sons, Ltd.     

矢量双轻子与大型强子对撞机上的Higgs数据

考虑到反常磁矩aμ对RM331模型自由参数的限制,本文计算了矢量双轻子V±,U±±对Higgs衰变过程h→γγ和h→Zγ的贡献.数值结果显示:在合理的参数范围内,矢量双轻子可解释LHCγγ产生道的相关数据.     

具有不可忽略退出的面板数据混合半参数变系数模型

由于面板数据经常出现由信息性退出而引起的缺失数据,故其统计分析比较复杂。本文假定响应变量服从部分线性半参变系数混合效应模型,其中非参数系数函数依赖于相应的退出时间。利用二步估计方法的思想求得参数与非参数部分的相合估计,减少了同时估计的参数的个数,避免了估计方程中由较多参数引起的多重共线性问题。     

音频贝叶斯谐波模型中参数的提取

由于音乐信号具有暂态特性、夹杂噪声、且它的基频在频域上具有复杂的结构,因此分析音乐信号是一项复杂的工作.本文在分析音乐信号时引入了音频贝叶斯谐波模型,并在此基础上提出了一个基于贝叶斯谐波模型的简化模型,然后采用可逆跳转蒙特卡罗(RJMCMC)采样算法对此模型中基频参数和谐波幅度进行了估计,仿真结果表明该算法取得了较好的效果.