Fixed T dynamic panel data estimators with multifactor errors

This article analyzes a growing group of fixed T dynamic panel data estimators with a multifactor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels and common observed factors. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit being of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally (iv) the effect of sample size. We apply each of these estimators to a crime application using a panel data set of local government authorities in New South Wales, Australia; we find that the results bear substantially different policy implications relative to those potentially derived from standard dynamic panel GMM estimators. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.     

数据生产要素的结合机制——互补性资产视角

数据作为生产要素创造的价值备受关注,但目前对数据与其他生产要素的结合机制缺乏明确的解释。本文从互补性资产理论出发,分别探讨数据与技术、资本、劳动生产要素的结合机制。研究表明:在与技术要素的结合上,数据要素的加入能够帮助创新企业更有效地使用互补性资产,从而产生技术创新;在与资本要素的结合上,数据要素的加入降低了企业获取作为生产资源和营销资源的互补性资产的成本,从而产生成本创新;在与劳动要素的结合上,数据要素能够从产品设计和生产方面提升互补性资产的生成效率,从而产生流程创新。最终,数据要素在与核心生产要素的有机结合下,极大促进了数字经济的创新与发展。     

农机服务发展与粮食生产效率研究:2004—2016——基于变系数随机前沿分析

采用“两步法”研究了农机服务发展与中国粮食生产效率的关系.基于2004-2016年全国31省份粮食生产投入产出的面板数据,利用变系数随机前沿分析方法测算了中国粮食生产的技术变化、技术效率以及全要素生产率,然后构建农机服务发展影响粮食生产效率的OLS回归模型.结果表明:(1)31省份粮食生产的投入要素产出弹性和技术效率存在明显差异,2008年粮食技术变化最为明显,粮食全要素生产率的增长在2008年高达5%.(2)农机服务在2008年以后对粮食全要素生产率的增长具有显著的促进作用,农机服务对粮食TFP的作用存在一定的滞后效应.(3)农机服务发展与劳动产出弹性具有替代关系,与化肥和机械产出弹性具有互补关系.在三大粮食作物中,小麦机械化程度最高,水稻和玉米的机械化程度还有待提升.     

制约我国政策性农业保险发展的因素及对策

建立政策性农业保险制度对于确保农业经济的持续增长和农民收入的稳定增加具有重大的现实意义。由于我国农业保险制度的发展受到多种因素的制约,导致保险深度和保险密度尚处于较低水平。我们要借鉴发达国家的成功经验,结合中国的实际,探索政策性农业保险的发展对策。     

用因子分析法对农业上市公司进行财务评价

本文运用SPSS中的因子分析技术,选用2004年6月30日的财务报表数据为例,对我国农业类上市公司的15项财务评价指标进行了综合分析。此实例说明通过实证可以对我国农业类上市公司的经营现状进行综合评价,为决策者提供较为客观的决策依据。     

试论城市化进程中的民族关系--以对临夏市的调查为视点

临夏市作为西北民族地区的一个小城市,在城市化进程中,其民族关系也会受到影响.针对临夏市的实际情况及学术界的研究现状,我们选取临夏市城市化进程中变化较大的几个变量,即民族居住格局的变迁、人口流动的影响、民族通婚的增减以及宗教因素,作为研究重点来衡量城市化进程中的民族关系.在此基础上得出临夏市各民族之间的关系总体上是平等、团结、互助、合作的社会主义新型民族关系,和谐平等的民族关系占据了主导地位的结论,并归纳出城市化进程中临夏市的民族关系具有主从性、敏感性、易发性、历史性、隐蔽性、复杂性等特点,最后提出临夏市各族群众必须解决自身的定位问题,建立临夏市和谐民族关系的良性运转机制.     

影响蓖麻种子萌发因子的研究

对蓖麻种子萌发进行温度、水分、播种深度、种皮等处理试验，结果表明，温度和播种深度显著影响蓖麻种子萌发，其中土壤温度低和覆土厚是导致蓖麻种子萌发缓慢的主要因子;种皮的存在有利于菌麻种子萌发;土壤水分含量在10％以上能满足蓖麻种子萌发。     

我国少数民族构成要素的因子分析

本文用总人口、自治地方本民族人口、自治地方少数民族人口、人均GDP、农业人口、识字人口、在校生、母语人、单语人、双语人人数等 10项指标作为民族的地区、经济、教育和语言构成要素 ,对这些要素在我国现有少数民族中的构成情况作了描述、相关和因子提取等统计分析 ,以说明目前我国少数民族构成的基本社会状况 ,以及这些要素对少数民族整体和个体的构成的作用或代表的信息量有多大。文章还依据已有的经验知识对统计分析结果作了相应的说明和解释。结论是 ,目前我国少数民族之间的区别特征主要在于经济发展水平、人口的教育水平、居住地区、使用语言等方面的社会差别 ;但是从发展趋势看 ,传统意义上的各民族构成要素的作用将逐渐减弱 ,民族间的差别将逐渐缩小     

Blood Money: Bayer’s Inventory of HIV-Contaminated Blood Products and Third World Hemophiliacs

This article presents an overlooked case of research misconduct and violations of basic principles of medical and business ethics. When Bayer’s Cutter Laboratories realized that their blood products, Factor VIII and IX or antihemophiliac factor (AHF), were contaminated with human immunodeficiency virus (HIV), the financial investment in the product was considered too high to destroy the inventory. Cutter misrepresented the results of its own research and sold the contaminated AHF to overseas markets in Asia and Latin America without the precaution of heat treating the product recommended for eliminating the risk. As a consequence, hemophiliacs who infused the HIV-contaminated Factor VIII and IX tested positive for HIV and developed AIDS.     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.