Structural Nonparametric Cointegrating Regression

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill‐posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near‐integrated time series, it is shown here that inverse and ill‐posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. It is further shown that use of augmented regression, as is common in linear cointegration modeling to address endogeneity, does not lead to bias reduction in nonparametric regression, but there is an asymptotic gain in variance reduction. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near‐integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required.     

Strong and weak consistency of LS estimators in the EV regression model with negatively superadditive-dependent errors

In this paper, the strong laws of large numbers for partial sums and weighted sums of negatively superadditive-dependent (NSD, in short) random variables are presented, especially the Marcinkiewicz–Zygmund type strong law of large numbers. Using these strong laws of large numbers, we further investigate the strong consistency and weak consistency of the LS estimators in the EV regression model with NSD errors, which generalize and improve the corresponding ones for negatively associated random variables. Finally, a simulation is carried out to study the numerical performance of the strong consistency result that we established.     

Present bias and health

This study uses a dynamic discrete choice model to examine the degree of present bias and naivete about present bias in individuals’ health care decisions. Clinical guidelines exist for several common chronic diseases. Although the empirical evidence for some guidelines is strong, many individuals with these diseases do not follow the guidelines. Using persons with diabetes as a case study, we find evidence of substantial present bias and naivete. Counterfactual simulations indicate the importance of present bias and naivete in explaining low adherence rates to health care guidelines.     

2m − p compromise plans with strongly clear specified main effects and two-factor interactions

This article studies two-level strongly clear compromise plans. We derive some necessary conditions for the existence of four classes of two-level strongly clear compromise plans which allow the estimations of some specified main effects and two-factor interactions, with the assumption that the fourth- or higher-order effects are negligible. Also, some methods for constructing 2^{m ? p} strongly clear compromise plans are introduced.     

Spatial autoregression with repeated measurements for social networks

Spatial autoregressive model (SAR) is found useful to estimate the social autocorrelation in social networks recently. However, the rapid development of information technology enables researchers to collect repeated measurements for a given social network. The SAR model for social networks is designed for cross-sectional data and is thus not feasible. In this article, we propose a new model which is referred to as SAR with random effects (SARRE) for social networks. It could be considered as a natural combination of two types of models, the SAR model for social networks and a particular type of mixed model. To solve the problem of high computational complexity in large social networks, a pseudo-maximum likelihood estimate (PMLE) is proposed. The asymptotic properties of the estimate are established. We demonstrate the performance of the proposed method by extensive numerical studies and a real data example.     

Variable selection and weighted composite quantile estimation of regression parameters with left-truncated data

In this paper, we consider the weighted composite quantile regression for linear model with left-truncated data. The adaptive penalized procedure for variable selection is proposed. The asymptotic normality and oracle property of the resulting estimators are also established. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.     

金融要素扭曲与企业创新活动

本文利用世界银行的企业调查数据并将四大国有商业银行的市场份额作为金融要素市场化的代理指标,分析了金融要素扭曲对企业创新活动的影响。实证研究发现：金融要素扭曲对企业的研发投入和创新成果都具有抑制作用,这种抑制作用在不同所有制企业间具有异质性,相比国有企业,私营企业的创新活动受金融要素扭曲的抑制作用更大。中小企业的研发投入对金融要素的扭曲的敏感性要高于大型企业,在过程创新中,金融要素扭曲对中小型企业的影响更大,但在产品创新过程中,金融要素的扭曲的对大型企业的影响则更强。本文的研究提供了金融要素扭曲对经济增长有负面的影响的来自企业创新的微观证据。因此,全面推进的金融要素市场化改革有助于企业的创新活动,从而为国家经济转型提供持续创新与发展的动力。     

基于自适应迭代更新的函数型数据聚类方法研究

函数型数据的稀疏性和无穷维特性使得传统聚类分析失效。针对此问题,本文在界定函数型数据概念与内涵的基础上提出了一种自适应迭代更新聚类分析。首先,基于数据参数信息实现无穷维函数空间向有限维多元空间的过渡;在此基础上,依据变量信息含量的差异构建了自适应赋权聚类统计量,并依此为函数型数据的相似性测度进行初始类别划分;进一步地,在给定阈值限制下,对所有函数的初始类别归属进行自适应迭代更新,将收敛的优化结果作为最终的类别划分。随机模拟和实证检验表明,与现有的同类函数型聚类分析相比,文中方法的分类正确率显著提高,体现了新方法的相对优良性和实际问题应用中的有效性。     

红山女神(下)

牛河梁女神庙中的红山女神,究竟是谁?既然学界对男性的英雄王身份存疑,推断他们中间有一位"大人物",甚至旁证博引论述那"大人物"应该是黄帝。那么,比黄帝身份地位还高的红山女神,我们该往哪里对应?毫无疑问,女性在原始社会为人类生存起到特殊作用。她们凝聚着召唤力、慈爱,勇于斗争,富有奉献精神,造就了母系社会。人类沿着这条     

Technology Spillovers in the Multinational Outsourcing System and Technological Innovation in Recipient Countries

跨国外包并不像FDI或国际贸易通过单一路径发生技术溢出,而是通过进口溢出、出口溢出和纯知识溢出等多条路径共同作用。基于知识生产函数方法建立国际R＆D溢出回归方程,分别检验跨国外包的各种技术溢出渠道对我国制造业技术创新的影响。研究发现,在跨国外包的三条技术溢出渠道中仅有进口溢出效应是显著的,并且存在较大的行业差异：高技术行业在低位点上显著,中等技术行业和低技术行业在中高位点处显著。实证结果说明了我国当地企业难以通过“出口中学习”、“交流中学习”实现技术创新。