电子商务发展与农村中小企业创新

基于阿里巴巴电子商务发展指数、中国工商注册企业年检数据库和专利数据库以及中国企业创新创业调查数据库,利用网商的特性和各个行业外部融资依赖程度的差异,探讨了电子商务发展对于农村中小企业创新的影响及其内在机制。实证分析结果表明,电子商务发展水平越高,农村中小企业的创新能力越强。电子商务发展通过缓解资金拖欠问题降低了融资约束,显著减少了外部融资依赖程度更高的农村中小企业的应收账款,提升了其现金流的充裕性和创新产出。分所有制类型的研究结果表明,电子商务的作用在外部融资依赖程度较高的私有农村中小企业中更为显著;而对于融资约束较小的国有村域企业,电子商务的发展并未在创新方面对其产生显著影响。     

基于DEPA探究亚太地区数字贸易治理前景

随着全球数字贸易的迅猛发展,当前WTO多边框架下数字贸易规则存在缺失,使得数字贸易治理呈现出区域化趋势。为探究亚太地区数字贸易治理前景,预判亚太经济体参与数字贸易治理的可能路径,基于《数字经济伙伴关系协定》(DEPA),采用文献分析、规则文本对比、逻辑推演等研究方法,对亚太地区数字贸易治理的议题广度和深度、谈判模式、模块特征、关注焦点及竞争格局进行全面系统的分析研判。研究认为,未来亚太数字贸易治理主要呈现以下趋向:数字贸易治理将涵盖更多新兴技术领域,传统议题也会趋于更高标准; 专门协定的高效率将驱使数字贸易议题逐渐与传统议题相剥离,协定的框架形式也将采取开放、灵活、包容的“主题模块”形式。数字包容性发展、数字便利化等议题将是亚太数字贸易治理的焦点议题; 亚太数字贸易治理的大国博弈将以中美两国为中心,中小经济体也会以“联合抱团”来争取某些议题的话语权。     

Coherent decision analysis with inseparable probabilities and utilities

This article explores the extent to which a decision maker's probabilities can be measured separately from his/her utilities by observing his/her acceptance of small monetary gambles. Only a partial separation is achieved: the acceptable gambles are partitioned into a set of belief gambles, which reveals probabilities distorted by marginal utilities for money, and a set of preference gambles, which reveals utilities reciprocally distorted by marginal utilities for money. However, the information in these gambles still enables us to solve the decision maker's problem: his/her utility-maximizing decision is the one that avoids arbitrage (i.e., incoherence or Dutch books).     

A Population Approach to Analysis of Variance Models

Several authors have contributed to what can now be considered a rather complete theory for analysis of variance in cases with orthogonal factors. By using this theory on an assumed basic reference population, the orthogonality concept gives a natural definition of independence between factors in the population. By looking upon the treated units in designed experiments as a formal sample from a future population about which we want to make inference, a natural parametrization of expectations and variances connected to such experiments arises. This approach seems to throw light upon several controversial questions in the theory of mixed models. Also, it gives a framework for discussing the choice of conditioning in models     

基于GRPLS 回归的 中国经济增长影响因素分析

构造一种新的方法———岭- 偏最小二乘回归方法(它既有效消除了因素变量之间的多 重共线性,又克服了传统方法的不足,且使模型更加稳健,具有更强的预测和分析能力) ;并运 用广义岭- 偏最小二乘回归方法分析了我国经济增长的影响因素,为我国制订持续、快速增长 的经济政策提供了有益的参考.     

Optimal B-robust estimators for the parameters of the Burr XII distribution

The Burr XII distribution offers a flexible alternative to the distributions that play important role for modelling data in reliability, risk and process capability. However, estimating the shape parameters of the Burr XII distribution is a challenging problem. The classical estimation methods such as maximum likelihood and least squares are often used to estimate the parameters of the Burr XII distribution, but these methods are very sensitive to the outliers in the data. Thus, a robust estimation method alternative to the classical methods is needed to find robust estimators that are less sensitive to the outliers in the data. The purpose of this paper is to use the optimal B-robust estimation method [Hampel FR, Ronchetti EM, Rousseeuw PJ, Stahel WA. Robust statistics: the approach based on influence functions. New York: Wiley; 1986] to obtain robust estimators for the shape parameters of the Burr XII distribution. The simulation results show that the optimal B-robust estimators generally outperform the classical estimators in terms of the bias and root mean square errors when there are outliers in data.     

An approximation of the distribution function of the LIML identifiability test statistic using the method of moments

This paper contains an application of the asymptotic expansion of a pFp() function to a problem encountered in econometrics. In particular we consider an approximation of the distribution function of the limited information maximum likelihood (LIML) identifiability test statistic using the method of moments. An expression for the Sth order asymptotic approximation of the moments of the LIML identifiability test statistic is derived and tabulated. The exact distribution function of the test statistic is approximated by a member of the class of F (variance ratio) distribution functions having the same first two integer moments. Some tabulations of the approximating distribution function are included.     

A new approach to statistical efficiency of weighted least squares fitting algorithms for reparameterization of nonlinear regression models

We study nonlinear least-squares problem that can be transformed to linear problem by change of variables. We derive a general formula for the statistically optimal weights and prove that the resulting linear regression gives an optimal estimate (which satisfies an analogue of the Rao-Cramer lower bound) in the limit of small noise.     

Practical procedures to deal with common support problems in matching estimation

This paper assesses the performance of common estimators adjusting for differences in covariates, such as matching and regression, when faced with the so-called common support problems. It also shows how different procedures suggested in the literature affect the properties of such estimators. Based on an empirical Monte Carlo simulation design, a lack of common support is found to increase the root-mean-squared error of all investigated parametric and semiparametric estimators. Dropping observations that are off support usually improves their performance, although the magnitude of the improvement depends on the particular method used.     

Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models

Outlier detection algorithms are intimately connected with robust statistics that down‐weight some observations to zero. We define a number of outlier detection algorithms related to the Huber‐skip and least trimmed squares estimators, including the one‐step Huber‐skip estimator and the forward search. Next, we review a recently developed asymptotic theory of these. Finally, we analyse the gauge, the fraction of wrongly detected outliers, for a number of outlier detection algorithms and establish an asymptotic normal and a Poisson theory for the gauge.