Bootstrapping Noncausal Autoregressions: With Applications to Explosive Bubble Modeling

In this article, we develop new bootstrap-based inference for noncausal autoregressions with heavy-tailed innovations. This class of models is widely used for modeling bubbles and explosive dynamics in economic and financial time series. In the noncausal, heavy-tail framework, a major drawback of asymptotic inference is that it is not feasible in practice as the relevant limiting distributions depend crucially on the (unknown) decay rate of the tails of the distribution of the innovations. In addition, even in the unrealistic case where the tail behavior is known, asymptotic inference may suffer from small-sample issues. To overcome these difficulties, we propose bootstrap inference procedures using parameter estimates obtained with the null hypothesis imposed (the so-called restricted bootstrap). We discuss three different choices of bootstrap innovations: wild bootstrap, based on Rademacher errors; permutation bootstrap; a combination of the two (“permutation wild bootstrap”). Crucially, implementation of these bootstraps do not require any a priori knowledge about the distribution of the innovations, such as the tail index or the convergence rates of the estimators. We establish sufficient conditions ensuring that, under the null hypothesis, the bootstrap statistics estimate consistently particular conditionaldistributions of the original statistics. In particular, we show that validity of the permutation bootstrap holds without any restrictions on the distribution of the innovations, while the permutation wild and the standard wild bootstraps require further assumptions such as symmetry of the innovation distribution. Extensive Monte Carlo simulations show that the finite sample performance of the proposed bootstrap tests is exceptionally good, both in terms of size and of empirical rejection probabilities under the alternative hypothesis. We conclude by applying the proposed bootstrap inference to Bitcoin/USD exchange rates and to crude oil price data. We find that indeed noncausal models with heavy-tailed innovations are able to fit the data, also in periods of bubble dynamics. Supplementary materials for this article are available online.     

Bayesian analysis of agricultural field experiments

The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast frequentist literature. Adoption of the Bayesian paradigm simplifies the interpretation of the results, especially in ranking and selection. Also, complex formulations can be analysed with comparative ease, by using Markov chain Monte Carlo methods. A key ingredient in the approach is the need for spatial representations of the unobserved fertility patterns. This is discussed in detail. Problems caused by outliers and by jumps in fertility are tackled via hierarchical t formulations that may find use in other contexts. The paper includes three analyses of variety trials for yield and one example involving binary data; none is entirely straightforward. Some comparisons with frequentist analyses are made.     

两惯性系相对运动速度方向任意时的波矢变换

用简便方法给出了两惯性系相对运动速度方向任意时的波矢变换规律。     

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.     

Adaptive kernel scaling support vector machine with application to a prostate cancer image study

The support vector machine (SVM) is a popularly used classifier in applications such as pattern recognition, texture mining and image retrieval owing to its flexibility and interpretability. However, its performance deteriorates when the response classes are imbalanced. To enhance the performance of the support vector machine classifier in the imbalanced cases we investigate a new two stage method by adaptively scaling the kernel function. Based on the information obtained from the standard SVM in the first stage, we conformally rescale the kernel function in a data adaptive fashion in the second stage so that the separation between two classes can be effectively enlarged with incorporation of observation imbalance. The proposed method takes into account the location of the support vectors in the feature space, therefore is especially appealing when the response classes are imbalanced. The resulting algorithm can efficiently improve the classification accuracy, which is confirmed by intensive numerical studies as well as a real prostate cancer imaging data application.     

基于动态VECM的我国铜期货的价格发现功能研究

通过采用ADF检验、协整性检验及Granger因果检验对近年来上海铜期货市场的价格发现功能进行全面深入的动态实证分析,表明铜期货价格具有良好的价格发现功能。通过动态向量误差修正模型(vector error correc-tion model,VECM)检验,铜现货市场和期货市场价格波动相关性趋于加强,但呈现出非线性规律,说明企业在进行套期保值决策时能以期货价格波动作为决策依据之一,但不能对期货价格波动反应过度,致使套期保值行为失当,需要综合分析外部市场环境和期货市场运行规律,做出全面科学的套期保值决策。     

改进PSO-SVM在说话人识别中的应用

为了加快粒子群优化算法的收敛速度,增强全局的搜索能力,通过对粒子群优化算法中惯性权重和全局最优值的分析,提出了一种根据迭代次数而自适应变化的惯性权重的粒子群优化方法。改进后的粒子群算法在防止陷入局部最优的能力方面有了明显的增强,同时,给出了应用粒子群优化算法训练支持向量机的方法,并将其应用于说话人识别。实验结果证实了在说话人识别中改进PSO-SVM方法比其他传统方法能获得更好的识别精度和识别速度。     

财政教育支出与经济增长关系的动态研究

2012年,我国财政性教育经费支出终于达到占GDP4％比重目标,这与国家多年来持续加大对教育的投入密切相关,也表明了国家对教育投入的重视.本文通过建立向量误差修正模型、Granger因果检验、脉冲响应函数以及方差分解方法实证分析财政教育支出与经济增长的动态关系.通过实证分析得到的主要结论：经济增长是引起财政教育支出增长的Granger原因,而固定资本增长是引起经济增长的Granger原因.经济增长对财政教育支出存在长期正向响应,而对固定资本的扰动是短期的响应;财政教育支出对经济增长的冲击贡献率持续增大.固定资本投资存在着短期的较好贡献,但长期影响小于财政教育支出的影响.     

基于舞弊三角理论的财务舞弊识别模型研究——支持向量机与Logistic回归的耦合实证分析

文章基于舞弊三角理论,对舞弊风险因素进行T检验,选取显著特征运用支持向量机算法和Logistic回归分析方法构建了舞弊识别模型,并进行比较。研究结果表明,财务稳定性越差、监督部门的监督积极性越低、曾经获得非标准审计意见次数越多,公司发生财务舞弊可能性越高。应制定合理的报酬激励机制,降低报酬契约冲突带来的舞弊压力;建立健全内部控制和风险管理评价,减少舞弊的机会;加强内外监督机制,杜绝舞弊的借口。     

我国高速公路建设对经济增长效应的实证研究

基于我国1990—2011年的相关数据,采用协整、Granger因果检验以及向量误差纠正模型等计量手段实证检验我国高速公路建设对经济增长的影响。研究发现:我国的高速公路建设对经济增长具有显著推动作用;VECM估计表明短期内我国经济增长的短期动态调整机制存在,而高速公路建设的短期动态调整机制微弱。