Semiparametric time series regression modeling with a diverging number of parameters

Variable selection and error structure determination of a partially linear model with time series errors are important issues. In this paper, we investigate the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation penalty for a partially linear model with a divergent number of covariates and finite order autoregressive time series errors. Both consistency and asymptotic normality of the proposed penalized estimators are derived. The oracle property of the resultant estimators is proved. Simulation studies are carried out to assess the finite‐sample performance of the proposed procedure. A real data analysis is made to illustrate the usefulness of the proposed procedure as well.     

Microfoundations of companies' dynamic capabilities for environmentally sustainable innovation: Case study insights from high‐tech innovation in science‐based companies

To achieve sustainable development, companies are increasingly putting an emphasis on the creation and the promotion of environmentally sustainable innovations. Environmentally sustainable innovation often involves a significant shift in a new strategic direction. This paper studies this shift from a dynamic capabilities perspective and aims to identify the microfoundations of science‐based companies' dynamic capabilities for high‐tech environmentally sustainable innovations. It investigates the development of high‐tech environmentally sustainable innovations in two distinctive science‐based companies. To scholars, this study provides an in‐depth process analysis, over time, of how and why microfoundations of dynamic capabilities influence the development of a science‐based company's high‐tech environmentally sustainable innovations. To practitioners in science‐based firms, this process study can function as a frame of reference, enabling the tailoring of a strategy for high‐tech environmentally sustainable innovation.     

X频段高频谱纯度频率合成器设计

针对高灵敏度接收机对频率合成器的高技术指标要求，构建了一种融合了直接模拟、直接数字以及间接数字的频率合成技术方案，根据该方案，成功实现了频率合成器的工程研制。通过测试，频率合成器相位噪声达-112 dBc/Hz@5 kHz，杂散抑制优于-75 dBc，频率分辨率小于1 kHz，10 MHz跳频时间约为13 μs，满足了高灵敏度接收机对频率合成器的高技术指标要求，为高纯度频率合成器的实现提供了一条新途径。     

城市轨道交通直流牵引供电系统接触网残压

对于城市轨道交通来讲,在运行的过程中,直流牵引供电系统是使用最为主要的部件之一。在该系统应用的过程中,接触网残压问题是较为严重的问题,也是当前所出现问题当中比较普遍的一种。对于直流牵引供电系统来讲,当出现接触网残压问题时,可能会对人的生命安全造成严重威胁,同时也会使检修施工和接触网送电保护闭锁等相关工作受到影响,严重拖慢工作进度。因此,论文通过测试、仿真分析和模拟验证等方法,对城市轨道交通直流牵引供电系统接触网残压问题进行了研究与论述。     

Alternative balanced scorecards built from paradigm models in strategic HRM and employment/industrial relations and used to measure the state of employment relations and HR system performance across U.S. workplaces

This paper constructs alternative balanced scorecards based on high‐performance work system (HPWS) and employment relations system (ERS) models. The models are depicted and compared in diagrams and used as framework skeletons for building separate HPWS and ERS scorecards, intended to provide a detailed data picture of the operational health and performance of an organization's employment/HR system and its operations, processes, and inputs/outputs. The scorecards are filled in with nationally representative data from 2,000+ U.S. workplaces using more than 50 employment/HR indicators, as reported by separate panels of managers and employees. The indicators for each workplace are aggregated into an overall HR/employment system score, ranked from low‐to‐high, and graphed as frequency distributions. These distributions provide a unique snapshot picture of the mean and dispersion of the state of employment relations and HR system performance for companies across the United State. They also reveal that “models matter” since the HPWS and ERS scorecards provide distinctly different evaluation assessments.     

优化营商环境,推动发展高质量经济

促进中国经济高质量发展的必由之路是优化营商环境,提升区域竞争力。目前我国面临着国际内外的复杂局势,经济未来的发展趋势不容乐观,为此我们要深入落实新的发展理念,建立新的发展思路,构建新的发展格局。优化营商环境应实现建立服务型政府,实现企业经营环节的全覆盖,降低制度性交易成本、建立完善的商业系统生态链、打造具有中国特色的营商环境。     

Residential segregation and educational attainment of children from regional migrant families in China

This paper studies the residential segregation effects on educational attainment of children from regional migrant families in China. We find that if migrant families live in segregated communities with fewer local residents, the school dropout rate of the children living with their parents in the host cities is higher and their high school enrollment rate is lower. We employ a unique set of nationwide survey data of regional migrants in China in 2012 and 2013 that comprises more than 150,000 individuals in each year. We first use the ratio of migrants over the total population residing in a community to measure segregation. When the ratio increases by 10%, the school dropout rate of migrant children increases by 4.7% from the mean value. Secondly, we identify segregation from the housing type of the migrant family. When they reside in the dorms provided by their employers or in the workplace, they will also be surrounded by many migrant colleagues. We find that living in such places increases the “not‐going‐to‐high‐school rate” of migrant children by 35% from the mean value. Our subsample analysis indicates that the segregation effects only exist in the samples of male migrant children and rural hukou families.     

基于熵值法的高质量发展综合评价研究

探索高质量发展评价指标体系是当前社会各界面临的极富挑战性和开创性工作。从生产、分配、交换和消费四个环节选取23个指标构建高质量发展指标体系,利用熵值法对2000—2015年我国经济高质量发展进行评价。在此基础上从提升全要素生产率、调节国民收入分配格局、助力文化消费以及防范市场风险等层面提出政策建议。     

结构性去杠杆下的中国宏观经济*——2018年中期中国宏观经济分析与预测

2018年以来中国经济平稳运行，物价水平稳定，就业形势整体向好，经济增长质量稳步提升。不过，当前中国经济仍然面临一定的下行压力，尤其需要警惕消费增速过快下滑、宏观税负进一步加重、民间投资复苏乏力、部分企业效益状况显著恶化、去杠杆过程中金融体系不稳定性加剧等主要风险点。此外，杠杆率高企是现阶段中国经济面临的突出问题，而且中国的杠杆率在不同部门之间以及各部门内部均体现出明显的结构性特点。为此，中央在2018年专门提出了“结构性去杠杆”的新思路。在“结构性去杠杆”稳步推进的大背景下，宏观政策既要积极应对经济下行压力，谨防去杠杆带来的经济增速超预期下滑风险，又要激发经济内生增长动力，促使经济实现长期可持续发展和“高质量发展”。     

Selection of influential variables in ordinal data with preponderance of zeros

Presence of excess zero in ordinal data is pervasive in areas like medical and social sciences. Unfortunately, analysis of such kind of data has so far hardly been looked into, perhaps for the reason that the underlying model that fits such data, is not a generalized linear model. Obviously some methodological developments and intensive computations are required. The current investigation is concerned with the selection of variables in such models. In many occasions where the number of predictors is quite large and some of them are not useful, the maximum likelihood approach is not the automatic choice. As, apart from the messy calculations involved, this approach fails to provide efficient estimates of the underlying parameters. The proposed penalized approach includes ?₁ penalty (LASSO) and the mixture of ?₁ and ?₂ penalties (elastic net). We propose a coordinate descent algorithm to fit a wide class of ordinal regression models and select useful variables appearing in both the ordinal regression and the logistic regression based mixing component. A rigorous discussion on the selection of predictors has been made through a simulation study. The proposed method is illustrated by analyzing the severity of driver injury from Michigan upper peninsula road accidents.