A diagnostic statistic for functional-coefficient autoregressive models

In this paper, functional coefficient autoregressive (FAR) models proposed by Chen and Tsay (1993) are considered. We propose a diagnostic statistic for FAR models constructed by comparing between parametric and nonparametric estimators of the functional form of the FAR models. We show asymptotic properties of our statistic mathematically and it can be applied to the estimation of the delay parameter and the specification of the functional form of FAR models.     

政府采购与经济增长的实证研究——基于ARMA最优模型

本文采用基于ARMA模型适用于小样本的最优预测的建模方法,来分析政府采购和经济增长之间的因果关系。研究结果表明政府采购和经济增长之间存在非对称、单向因果关系,即政府采购对经济增长的影响不显著,而经济增长对政府采购具有促进作用。因此,从政府采购影响路径的角度分析认为,在后金融危机时代应该采取一些可行的措施扭转政府采购对经济增长的不显著状况。     

人口较少民族经济向现代经济转变的路径分析

在阐述路径依赖理论的基础上,结合人口较少民族经济的特点对人口较少民族经济向现代经济转变的路径进行了分析,提出要辩证地看待人口较少民族传统经济在这一转变中的影响和作用,认为人口较少民族实现经济转变的首要问题是打破经济封闭和信息封闭,在此基础上,将自身的特色优势与区域优势有效结合起来,将传统经济中的积极因素与现代经济因素有效结合起来,走因地制宜、循序渐进的转变路径。     

单指标非参数期权定价——改进的非参数定价方法

本文建立一种改进的非参数期权定价模型,称为单指标非参数期权定价模型。相比现有非参数回归期权定价模型是期权价格关于各个因素的多元回归函数,本模型通过变量变换把期权价格多个因素指标转换为一个综合变量——单指标,得到期权价格关于单指标的一元非参数回归方程。改进的模型实现了多元非参数期权定价模型的降维和简化了模型计算;还通过多个期限期权的单指标组合解决了非参数估计的样本数量问题;以及通过期限平滑解决了现有非参数定价模型中的日历效应问题。选取上证50ETF期权数据实证分析表明,无论是样本内的估计结果还是样本外的预测结果都比传统的Black-Scholes模型、半参数Black-Scholes模型和多元非参数回归期权定价模型估计效果有提高。     

与实验紧密结合的工科大学物理小班化探索

针对工科大学物理在小班化进程中的师生角色转换和课时不足等问题,分析了教师角色、教学内容和模式,并结合高校培养目标,从物理理论和实验的关系着手,提出一种将两者融为一体的课程模式,经由多角色教师团队、“全”和“精”的教学内容和开放的教学模式,搭建起理论和实验“承上启下”的桥梁。学生在实验平台上的具体实践表明,这种小班化教学效果明显。     

金融发展影响我国中小企业出口规模的实证研究

基于2001—2013年我国30个省市面板数据,从金融规模、金融结构和金融效率3个角度,详细探究了金融发展与我国中小企业出口规模的关系以及影响效应。结果表明：金融规模对中小企业出口规模扩张存在显著的负向影响,金融结构对中小企业出口规模扩张产生促进作用,而金融效率指标对中小企业出口的影响不显著;政府财政支出和外商直接投资显著促进中小企业出口活动,基础设施水平一定程度上抑制了中小企业出口活动。     

西南山区小城市城镇文化建设SWOT分析——以重庆市城口县为例

文化建设带动区域发展对西南山区小城镇具有一定参考,是实现新形势下的特色城镇化发展的有益尝试。文章选择重庆市城口县为研究对象,运用SWOT研究方法,系统分析城口县在建设城镇文化进程中的优势、局限、机遇和挑战,归纳西南山区小城镇发展城镇文化的内外部环境,研究结果显示,城口县发展城镇文化总体上具有资源丰富的优势和空间受限等局限,国内政策、地方发展机遇等略大于区域竞争的挑战,且个别挑战和局限可以转化为发展机遇和优势。因此,以文化建设带动西南山区小城镇新型城镇化发展的思路具有一定可行性,但要结合小城市当地实际,打造有特色的文化空间格局和构建地方文化体系。     

The k-factor GARMA Process with Infinite Variance Innovations

In this article, we develop the theory of k-factor Gegenbauer Autoregressive Moving Average (GARMA) process with infinite variance innovations which is a generalization of the stable seasonal fractional Autoregressive Integrated Moving Average (ARIMA) model introduced by Diongue et al. (2008 Diongue, A.K., Guégan, D. (2008). Estimation of k-Factor GIGARCH Process: A Monte Carlo Study. Communications in Statistics-Simulation and Computation 37:2037–2049.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Stationarity and invertibility conditions of this new model are derived. Conditional Sum of Squares (CSS) and Markov Chains Monte Carlo (MCMC) Whittle methods are investigated for parameter estimation. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques. Finally, the usefulness of the model is corroborated with the application to streamflow data for Senegal River at Bakel.     

In-Sample Inference and Forecasting in Misspecified Factor Models

This article considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension-reduction devices: principal components, ridge, Landweber Fridman, and partial least squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross-validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods outperform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors’ forecasting ability.     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.