禽流感危机下肉鸡产业市场状态分析*——基于互联网大数据和MS-VAR模型

[目的]为了有效应对禽流感冲击、维持肉鸡产业稳定健康发展。[方法]基于互联网大数据构建了禽流感舆情指数,选取2012年1月至2017年3月周度数据,通过MS-VAR模型分析了禽流感危机下肉鸡市场状态的转换特征以及不同市场状态下产业链价格传导关系。[结果]2012年以来可将肉鸡产业市场状态划分为危机状态和正常状态,肉鸡产业市场在两个市场状态下转换频繁;肉鸡产业危机市场状态和正常市场状态平均持续期分别为4.65周和9.05周;肉鸡市场在区制转移概率上存在明显的非对称特征;肉雏鸡、活鸡和白条鸡价格之间在危机状态下相关性小于正常市场状态下相关性;不同市场状态下肉鸡产业链价格传导存在差异;肉鸡产业市场在危机状态下不稳定程度明显增加。[结论]提出了加大对禽流感疫情的监测以及疫情处理能力,密切关注公众禽流感舆情动态变动情况,做好舆情引导,加强禽流感疫情科学知识的宣传,增强公众对禽流感的认知等对策建议。     

生态文明建设背景财政扶持、林权改革对林业生产效率的影响

基于2000—2016年中国省际面板数据,利用DEA-Malmquist指数法对中国林业生产效率的区域差异和时序变化进行研究,运用面板Tobit模型检验财政扶持对林业生产效率的影响并验证林权改革对二者关系的调节作用。研究发现:在生态文明建设背景下,林业生产效率存在显著的区域差异;技术进步对于全要素生产率的提升起到重要推进作用;财政扶持与林权改革均对林业生产效率产生了正向影响,但林权改革并没有对财政扶持与林业生产效率的关系产生积极调节作用。因此,各省份应当通过加大政府扶持力度、优化投资方式、深化集体林权制度改革等措施促进林业生产效率提升。     

基于SARIMA-SVR组合模型的 空气质量指数预测

空气质量指数(AQI)在波动中既具有整体的时间序列线性特征和明显的季节性波动周期,又具有多种因素影响的不确定性,为了提高AQI的预测精度,基于Ri386 3.3.3和Matlab R2014a两种编程软件,提出了一种同时具有线性和非线性的复合特征的时间序列预测模型——SARIMA-SVR组合模型。以太原市2014年1月—2019年7月的AQI月均值数据为基础,利用SARIMA时间序列模型进行线性预测,利用SVR模型对残差进行非线性预测,加和得到组合预测模型的预测结果,分析比较SARIMA,SVR和SARIMA-SVR这3种模型的预测结果和平均绝对百分比误差。结果表明,组合预测模型发挥了2种模型各自的优势,相较于单一预测模型的预测结果而言,其预测精度更高,稳定性更好。通过此模型得到的空气质量预测结果不仅可为人们的日常生活提供指导,而且可为大气污染的防治工作提供科学依据和借鉴意义。     

Functional Itô calculus†

We extend some results of the Itô calculus to functionals of the current path of a process to reflect the fact that often the impact of randomness is cumulative and depends on the history of the process, not merely on its current value. We express the differential of the functional in terms of adequately defined partial derivatives to obtain an Itô formula. We develop an extension of the Feynman-Kac formula to the functional case and an explicit expression of the integrand in the Martingale Representation Theorem. We establish that under certain conditions, even path dependent options prices satisfy a partial differential equation in a local sense. We exploit this fact to find an expression of the price difference between two models and compute variational derivatives with respect to the volatility surface.     

Monetary and Financial Cooperation Between China and the One Belt One Road Countries

ABSTRACT

Based on the theory of optimal currency area (OCA), we calculate the OCA index between China and the OBOR partners with the expressed objective of identifying which partners exhibit monetary and financial compatibilities, and hence present the best potential in terms of cost and benefits. Our findings suggest that among South East Asia region, Malaysia exhibits the highest compatibility and profit potential, followed by Thailand and Vietnam. For the East European area, Poland and Croatia show the best potential in terms of monetary and financial cooperation; with the Czech Republic as close third. Based on our computation, currently Central Asia and the Middle East do not possess the condition or potential for beneficial financial and monetary collaboration.     

“Opt out” or kept out? The effect of stigma,structure, selection,and sector on the labor force participation of married women in India

Using the 61st and 68th rounds of National Sample Survey data, we investigate the role of stigma, the economy’s structure, potential selection bias, and sectoral differences in explaining the low labor force participation (LFP) of middle and secondary educated women in India relative to low‐ and high‐educated women (the “U‐shape”). Estimating LFP regressions on a sample of prime‐aged married women, we show that, controlling for background characteristics, if a woman’s husband works in a white‐collar job in a region with a high share of blue‐collar jobs, she is less likely to participate in the labor market (stigma). We also find a positive effect of an increase in an index of white‐collar job growth (structure) on married women’s LFP. These effects are present in both rural and urban sectors but are strongest in the rural sector. However, middle and secondary educated women are still found to have substantially lower LFP than low‐ and graduate‐educated women in both sectors. Indeed, over time, the U‐shape persists in the rural sector and deepens in the urban sector. Because unobservables are quite large in the urban sector, we use the method of instrumental variables and find an increasing return at middle levels but stagnation at higher levels.     

济南市古生物化石赋存特征及保护区划研究

为贯彻《古生物化石保护条例》,提高济南市古生物化石保护水平,在对区内古生物化石资源进行野外勘查和综合整理的基础上,依据古生物化石鉴定结果及其赋存地层分布特征,得出济南市古生物化石的分布特征,划分了古生物化石产地,选择化石完整程度、富集程度、剖面出露程度、周边地质环境、工程活动影响五个评价因子,运用综合指数法,建立了符合济南实际的古生物化石保护区划分体系,将区内古生物化石产地进行了保护区分级,并按照“保护优先,结合实际”的原则,制定了符合济南市实际的古生物化石保护措施。     

基于层次分析法的我国海水淡化开发利用潜力评价方法研究

海水淡化是缓解沿海地区水资源短缺的重要手段。利用层次分析法，开展了我国海水淡化开发利用潜力评价方法研究。从政策因素、社会经济因素、技术条件3个方面，构建了我国海水淡化开发利用潜力评价指标体系，对各指标影响作用权重进行了计算。以全国11个沿海省(区、市)为研究目标，对其海水淡化开发利用潜力进行了评价。研究结果表明，我国各沿海省(区、市)开发利用海水淡化的潜力由大到小分别是:河北、浙江、山东、天津、江苏、辽宁、海南、广东、广西、上海、福建。最后，结合影响我国海水淡化开发利用的主要因素，从战略定位、试点示范、财政补贴、自主创新4个方面，提出了进一步推进我国海水淡化开发利用的建议。     

Recovering Sociology from the Trash Heap: Of Waste Politics and the Spatialization of Local Representation in Lagos

This article excavates the debate that unfolded during the 1980s between Jacques Rancière and Pierre Bourdieu to shed light on the theoretical divide that still cuts across the field of urban studies today. Looking at contemporary Rancièrian scholarship through a Bourdieusian lens, it points to their main theoretical shortcomings and reasserts the value of relational, field‐based and empirically grounded approaches to urban politics. At the same time, this article engages seriously Rancière's critique of Bourdieu's failure to account for space in order to question the territoriality inherent to the notion of field. We put this theoretical discussion to the test of Lagos's garbage. Drawing on an ethnographic study conducted in Lagos from 2015 to 2016, we propose analyzing the process of spatialization of the field of local representation by looking at the ways solid waste—here conceived as a political opportunity—is mobilized by different actors. We argue that the deployment of a waste infrastructure in Lagos is congruent with a relative disinvestment in practices of territorial control that reshapes the structure of local representation, reconfiguring the ‘stage’ on which politics is played out in the Nigerian metropolis.     

Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes

This paper contributes to the nascent literature on nowcasting and forecasting GDP in emerging market economies using big data methods. This is done by analyzing the usefulness of various dimension-reduction, machine learning and shrinkage methods, including sparse principal component analysis (SPCA), the elastic net, the least absolute shrinkage operator, and least angle regression when constructing predictions using latent global macroeconomic and financial factors (diffusion indexes) in a dynamic factor model (DFM). We also utilize a judgmental dimension-reduction method called the Bloomberg Relevance Index (BRI), which is an index that assigns a measure of importance to each variable in a dataset depending on the variable’s usage by market participants. Our empirical analysis shows that, when specified using dimension-reduction methods (particularly BRI and SPCA), DFMs yield superior predictions relative to both benchmark linear econometric models and simple DFMs. Moreover, global financial and macroeconomic (business cycle) diffusion indexes constructed using targeted predictors are found to be important in four of the five emerging market economies that we study (Brazil, Mexico, South Africa, and Turkey). These findings point to the importance of spillover effects across emerging market economies, and underscore the significance of characterizing such linkages parsimoniously when utilizing high-dimensional global datasets.