A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition

This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions.     

粤港澳大湾区制造业集聚与区域创新

以2009—2020年粤港澳大湾区11个城市为研究对象，探讨制造业集聚与区域创新的关系。研究发现，制造业集聚显著提高区域创新水平，可通过提高企业竞争这一路径实现。进一步分析发现，金融业集聚对区域创新发展具有显著的正向关系，金融产业和制造业产融协同集聚显著提高区域创新水平。研究结果表明，制造业集聚加剧产业内部企业创新竞争，带动区域创新发展，且金融业在创新中起到积极的推动作用。指出应鼓励区域企业创新竞争，优化营商环境，为产业融合发展提供保障。     

Doing more and doing better are two different entities: Different patterns of family control and environmental performance

Family firms bear two types of agency costs, including type I and type II agency problems, in corporate environmental practices: (1) Outside executives at family firms hesitate to engage in environmental strategies, which can lead to drops in profits; (2) Controlling families employ opportunistically environmental management to achieve their interests. We argue that a primary cause for the agency problems lies on ineffective internal corporate governance at family firms, which can cause loss of managerial (or power) balance between outside executives and family executives. Our findings show that family firms with ownership and strategic control (FSC), which family executives and outside executives monitor and constrain each other, can achieve the highest environmental performance. Moreover, external controls, including product market competition and provincial environmental regulations, substitute effective internal control of FSC. The environmental performance premium of FSC is more prevalent when the production market competition is lower. Family firms with ownership, operational, and strategic control (FOSC) can achieve higher environmental performance within a province with more stringent environmental regulations.     

Does local government competition aggravate haze pollution? A new perspective of factor market distortion

Haze pollution has become a new threat to China's sustainable development, but it may be that local government behaviour can play an important role in the prevention and control of pollutants. A dynamic spatial autoregressive (SAR) model is used to study the relationship between local government competition and haze pollution. To further explore the indirect impact of factor market distortion on haze pollution and control potential endogeneity problems, a newly developed intermediary effect model that incorporates the characteristics of the generalized method of moments (GMM) is utilized to explore how factor market distortion indirectly affects haze pollution. The research results show that regional haze pollution in China is characterized by significant spatial correlation, and local government competition has a positive impact on haze pollution; that is, local government competition exacerbates haze pollution. In general, local government competition not only directly leads to an increase in haze pollution but also further intensifies it by distorting the local factor market, and the intermediary role of factor market distortion is approximately 7.04%. The results of the regional inspection found that competition among local governments in the eastern region did not lead to haze pollution, and distortion of the factor market did not exist as an intermediary effect. However, both direct and intermediary effects are significant in the central and western regions. Therefore, an official performance appraisal system that includes ecological constraints should be established to guide the benign transformation of local government competition, and an environmental management mechanism must be developed for joint prevention and control to reduce haze pollution. In addition, the free flow of factors and marketization are equally important.     

Knowledge capital,CEO power,and firm value: Evidence from the IT industry

Disruptive innovation dramatically changes the demand of a product market in the information technology (IT) industry. In response to the impact of disruptive innovation, IT firms that may be eliminated from the competitive race actively develop innovative products and adjust their operating strategies to strengthen their survivability in the fiercely competitive market. Thus, this study explores the factors that affect firm value in the IT industry under the impact of disruptive innovation. The empirical results reveal that knowledge capital and CEO power play crucial roles in explaining firm value. IT firms with powerful CEOs and increased knowledge capital have high firm values. The effects of knowledge capital and CEO power on firm value are especially significant for founder and duality CEO firms. Furthermore, the influence of CEO power is more prominent in periods of financial crisis.     

权力义务视角下完善向基层赋权的思考

2010年开始,向基层赋权作为行政体制改革的重要内容在全国范围铺开。从权力义务视角对其进行研究有现实的必要性。赋权的核心是行政权力的转移,以权力转移过程中涉及的诸要素为切入点,可以分析出多重权力关系和有关主体在其中的权力义务。在此基础上,通过保障行政相对人对于赋权的参与权、设计上级行政机关实施赋权的制度、消除上级行政机关赋权的依据问题和设计基层行政机关行使权力的制度等方式完善向基层赋权,既使行政机关能够有效推进行政体制改革,又能对行政权进行有效规范、保障行政相对人权利。     

浅谈基于大数据背景下的数字经济发展——以数字电网为例

我们已经进入了一个全新的大数据信息化时代,充满了更多的机遇与挑战。大数据及数字经济的出现极大程度地改变了传统技术及工作思维的方式。数字经济是继工业和农业之后的又一大经济形态,为社会的经济增长提供了新的动力。通过分析电网行业传统的科学技术及作业方式,论文简要阐述了基于大数据信息化发展下电网的影响,并认为信息化技术的发展对于数字经济有着至关重要的影响及重要性。最后论文讨论了数字经济发展存在的问题及方向。     

领导干部自然资源资产离任审计与企业绿色并购——来自中国重污染型上市公司的经验证据

基于2011~2017年我国A股重污染型上市公司经验数据，实证检验了领导干部自然资源资产离任审计对企业绿色并购行为的影响，研究发现：领导干部自然资源资产离任审计可显著促进辖区内企业实施绿色并购；而当地区市场竞争程度越高时，越会抑制企业在领导干部自然资源资产离任审计试点实施背景下的绿色并购扩张行为；进一步地，企业绿色并购会显著提高企业价值，且这一影响对并购后当年的企业价值影响最为显著；基于异质性分析方面，领导干部自然资源资产离任审计会约束不同产权性质企业的环境治理行为，促进其采取绿色并购，实施绿色发展；同时领导干部自然资源资产离任审计会更有利于东部地区企业实施绿色并购，而对中西部地区的企业影响不明显。     

M5 competition uncertainty: Overdispersion,distributional forecasting,GAMLSS, and beyond

The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data face strong overdispersion and sporadic demand, especially zero demand. We discuss modeling issues concerning adequate probabilistic forecasting of such count data processes. Unfortunately, the majority of popular prediction methods used in the M5 competition (e.g. lightgbm and xgboost GBMs) fail to address the data characteristics, due to the considered objective functions. Distributional forecasting provides a suitable modeling approach to overcome those problems. The GAMLSS framework allows for flexible probabilistic forecasting using low-dimensional distributions. We illustrate how the GAMLSS approach can be applied to M5 competition data by modeling the location and scale parameters of various distributions, e.g. the negative binomial distribution. Finally, we discuss software packages for distributional modeling and their drawbacks, like the R package gamlss with its package extensions, and (deep) distributional forecasting libraries such as TensorFlow Probability.     

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.