Strong orthogonal decompositions and non‐linear impulse response functions for infinite‐variance processes

The author proves that Wold‐type decompositions with strong orthogonal prediction innovations exist in smooth, reflexive Banach spaces of discrete time processes if and only if the projection operator generating the innovations satisfies the property of iterations. His theory includes as special cases all previous Wold‐type decompositions of discrete time processes, completely characterizes when non‐linear heavy‐tailed processes obtain a strong‐orthogonal moving average representation, and easily promotes a theory of non‐linear impulse response functions for infinite‐variance processes. The author exemplifies his theory by developing a non‐linear impulse response function for smooth transition threshold processes, and discusses how to test decomposition innovations for strong orthogonality and whether the proposed model represents the best predictor. A data set on currency exchange rates allows him to illustrate his methodology.     

Alpha as Ambiguity: Robust Mean‐Variance Portfolio Analysis

We derive the analogue of the classic Arrow–Pratt approximation of the certainty equivalent under model uncertainty as described by the smooth model of decision making under ambiguity of Klibanoff, Marinacci, and Mukerji (2005). We study its scope by deriving a tractable mean‐variance model adjusted for ambiguity and solving the corresponding portfolio allocation problem. In the problem with a risk‐free asset, a risky asset, and an ambiguous asset, we find that portfolio rebalancing in response to higher ambiguity aversion only depends on the ambiguous asset's alpha, setting the performance of the risky asset as benchmark. In particular, a positive alpha corresponds to a long position in the ambiguous asset, a negative alpha corresponds to a short position in the ambiguous asset, and greater ambiguity aversion reduces optimal exposure to ambiguity. The analytical tractability of the enhanced Arrow–Pratt approximation renders our model especially well suited for calibration exercises aimed at exploring the consequences of model uncertainty on equilibrium asset prices.     

我国能源效率与经济增长关系研究——基于PSTR模型的实证

实证结果表明,能源效率与经济增长之间存在机制转换效应,在经济发展处于低收入水平时,能源效率与经济增长表现为线性关系;在经济发展处于中等或较高收入水平时,能源效率与经济增长之间则表现为非线性关系,并以人均GDP的阈值为界,在不同机制之间进行平滑转换。     

我国工业增加值季节波动非线性研究——基于SEATV-STAR模型

多数宏观经济变量时间序列有季节波动,如果季节波动是非线性的,采用经季节调整过的数据或传统季节模型等线性处理季节波动的方法可能就不再适用。本文基于季节时变平滑转换自回归(SEATV-STAR)模型,运用"特殊到一般"的非线性检验策略对我国工业增加值季度增长率季节波动进行研究。结果表明:(1)工业增加值的季节波动兼有结构时变和非线性改变,工业增加值的周期波动是线性的。(2)技术进步、体制变迁等因素使得工业增加值季节波动发生连续的结构时变,它们是季节波动变化的主要影响因素。(3)工业增加值周期波动对其季节波动有非对称影响;在工业增加值的波峰阶段,其季节波幅会减小,且1、2季度工业增长率有明显提高。     

经济增长与能源强度：基于面板平滑转换回归模型的实证分析

选取1960-2009年间9国的面板数据,基于面板平滑转换回归（PSTR）模型及改进的算法,实证分析了经济增长与能源强度间的关系。实证结果不仅支持了经济增长与能源强度之间倒U型关系的存在,而且表明在一国经济的不同发展阶段,经济增长与能源强度之间存在连续平滑转换机制,机制转换效应使经济变量以阈值为界从一个机制转为另一个机制;目前,中国的经济增长与能源强度处于倒U型曲线的上行阶段,当人均GDP达13208.48美元时,能源强度的拐点或将出现,经济增长对能源的依赖程度或将逐步下降。     

Goodness of fit tests for Nakagami distribution based on smooth tests

ABSTRACT

Nakagami distribution is one of the most common distributions used to model positive valued and right skewed data. In this study, we interest goodness of fit problem for Nakagami distribution. Thus, we propose smooth tests for Nakagami distribution based on orthonormal functions. We also compare these tests with some classical goodness of fit tests such as Cramer–von Mises, Anderson–Darling, and Kolmogorov–Smirnov tests in respect to type-I error rates and powers of tests. Simulation study indicates that smooth tests give better results than these classical tests give in respect to almost all cases considered.     

基于LSTAR模型的中国股市泡沫风险识别

本文在非线性模型框架下拟合中国主要股价指数的真实数据生成过程,并提出股市泡沫风险识别方法,较Phillips et al.（2011）提出的上确界单位根(SADF)方法具备更好的效果,能够精准预判股市泡沫风险进而为防范化解金融风险的政策措施提供参考。实证检验发现,主要股价指数的波动均存在逻辑平滑转换自回归(LSTAR)模型描述的非线性特征,自推出以来,四大股价指数均存在泡沫风险,上证指数存在六个主要的持续期,深圳成指存在四个主要的持续期,沪深300指数存在两个主要持续期,而创业板指数存在三个持续期。总体来看,创业板指数的泡沫生成时间会先于其它三大指数,可以作为预警中国股票市场泡沫风险的先行指标,且2015年7月之后的中国股票市场并不存在泡沫风险。     

Nonparametric regression for dependent data in the errors-in-variables problem

We consider the nonparametric estimation of the regression functions for dependent data. Suppose that the covariates are observed with additive errors in the data and we employ nonparametric deconvolution kernel techniques to estimate the regression functions in this paper. We investigate how the strength of time dependence affects the asymptotic properties of the local constant and linear estimators. We treat both short-range dependent and long-range dependent linear processes in a unified way and demonstrate that the long-range dependence (LRD) of the covariates affects the asymptotic properties of the nonparametric estimators as well as the LRD of regression errors does.     

Shape-constrained Gaussian process regression for surface reconstruction and multimodal,non-rigid image registration

We present a new statistical framework for landmark ?>curve-based image registration and surface reconstruction. The proposed method first elastically aligns geometric features (continuous, parameterized curves) to compute local deformations, and then uses a Gaussian random field model to estimate the full deformation vector field as a spatial stochastic process on the entire surface or image domain. The statistical estimation is performed using two different methods: maximum likelihood and Bayesian inference via Markov Chain Monte Carlo sampling. The resulting deformations accurately match corresponding curve regions while also being sufficiently smooth over the entire domain. We present several qualitative and quantitative evaluations of the proposed method on both synthetic and real data. We apply our approach to two different tasks on real data: (1) multimodal medical image registration, and (2) anatomical and pottery surface reconstruction.     

具有平滑迁移的ARFIMA模型及其应用

本文基于描述长记忆性的ARFIMA模型和具有结构性转变的平滑迁移模型,提出了联合检验两种时间序列性质的STARFIMA模型,并给出了估计模型系数的估计方法和检验非线性的刀切似然比方法.应用我国通货膨胀率的时间序列数据,我们应用Logistic型STARFIMA模型进行经验分析时发现,STARFIMA模型具有比ARFIMA模型更好的模拟效果和精度,而且该模型分别捕捉到了以通货膨胀率自身和加速通货膨胀率为转移变量的结构性转变,并发现在引入结构转变之后的通货膨胀率序列的记忆性变强的特征.