Cryptocurrencies and Cagan’s model of hyperinflation

The drivers of the prices of Bitcoin and Ethereum are studied within a framework based on Cagan’s model of hyperinflation. In the model, the prices of the cryptocurrencies are driven by stochastic adoption and velocity shocks as well as endogenous expectations of future prices. The model is estimated with data for prices, transaction volumes, and money supplies. A majority of price fluctuations in both currencies can be attributed to shocks in adoption, velocity shocks are much less important. The money demand sensitivity to expected price changes is estimated to be larger for Bitcoin than for Ethereum, and both have higher sensitivity than fiat currencies during episodes of hyperinflation.     

Advertisement policy for dual-channel within emissions-controlled flexible production system

Nowadays, industrial firms are very much careful to build a green environment by reducing carbon emissions. The government imposes some rules and regulations to provide a better eco-friendly environment. In this study, the cap-and-trade mechanism has been considered in a production model to control the carbon emissions rate. The manufacturers invest in advanced green technology to reduce per unit emissions. As online and offline selling is crucial to any industry for increasing customer demand, the manufacturers sell their products by dual-channel and advertise their products by online channel to make more popular of their products. Keeping these in mind, a sustainable flexible production model with single-type substitutable product production is considered here by imposing a cap-and-tax policy, investing in green technology, and advertising for products. This model is divided into two cases: with and without investment in green technology. The demand of each manufacturer depends upon an online selling price, an offline selling price, and an online advertisement of the product. A classical optimization technique helps to get the optimum strategies for the online selling price, offline selling price, advertisement investment, green technology, cycle time, and production rate. From the numerical study, it is proved that the industry gets 6.97% more profit in the case of green technology investment and the proposed study gives 5.74% more profit than the traditional production system. Sensitivity analysis and managerial insights are performed.     

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.     

Debt Overhang and Deleveraging in the US Household Sector: Gauging the Impact on Consumption

Using a novel data set for the U.S. states, this paper examines whether household debt and the protracted debt deleveraging help explain the dismal performance of U.S. consumption since 2007 in the aftermath of the housing bubble. By separating the concepts of deleveraging and debt overhang—a flow and a stock effect—we find that excessive indebtedness exerted a meaningful drag on consumption over and beyond wealth and income effects. The overall effect, however, is modest—‐around one sixth of the slowdown in consumption between 2000–06 and 2007–12—and mostly driven by states with particularly large imbalances in their household sector. This might be indicative of non‐linearities, whereby indebtedness begins to bite only when misalignments from sustainable debt dynamics become excessive.     

Forecasting turning points in tourism growth

Tourism demand exhibits growth cycles, and it is important to forecast turning points in these growth cycles to minimise risks to destination management. This study estimates logistic models of Hong Kong tourism demand, which are then used to generate both short- and long-term forecasts of tourism growth. The performance of the models is evaluated using the quadratic probability score and hit rates. The results show that the ways in which this information is used are crucial to the models’ predictive power. Further, we investigate whether combining probability forecasts can improve predictive accuracy, and find that combination approaches, especially nonlinear combination approaches, are sensitive to the quality of forecasts in the pool. In addition, model screening can improve forecasting performance.     

面源污染视角下江西省耕地利用效率研究

[目的]在面源污染视角下考察耕地利用效率,可以为推动耕地可持续利用提供理论和参考依据。[方法]文章基于2000—2014年我国13个粮食主产省(区)耕地投入产出的面板数据,运用SBM方向性距离函数将耕地面源污染因素纳入到传统的效率分析框架,从省际比较的维度对江西省耕地利用效率进行了测度与分析。[结果]考虑面源污染因素对耕地利用效率测算产生了一定的影响;江西省耕地利用效率值呈现阶段性特征,耕地利用效率排名比较稳定,近年来排名有上升趋势;江西省耕地利用绩效一般,且与最佳实践者存在较大的差距。[结论]忽视面源污染因素的耕地利用效率评估是失真和不符合实际的,宜将面源污染因素纳入到耕地利用效率的测度框架,以此来更科学地量化评估耕地利用绩效;设计和完善相关政策,以有效控制耕地面源污染;加强与先进省(区)的交流与合作,学习其农业可持续耕作技术与管理经验,进一步推动江西省耕地产出与资源、环境协调发展。     

我国大麦价格波动特征及其影响因素分析

[目的]大麦价格剧烈波动会直接影响大麦种植户的生产积极性和大麦产业的平稳发展,研究大麦价格波动特征及其影响因素,有助于提升大麦产业链相关主体识别和应对市场风险的能力,促进大麦产业的健康发展。[方法]文章先采用HP滤波法和ARCH类模型分析了2011年4月至2017年2月我国大麦价格波动特征,然后采用脉冲响应函数分析了我国大麦价格波动影响因素。[结果]我国大麦价格波动存在明显的季节性和周期性,样本期内总体上呈现逐渐下降趋势;我国大麦价格具有显著的波动集聚性,我国大麦价格具有显著的不对称性;在该文选择的影响因素中,大麦进口量和国际大麦价格是影响我国大麦价格波动的主要因素。[结论]该文提出必须保障并提高国内大麦合理产能、完善大麦价格监测预警体系、加强国内大麦进口企业整合和推动大麦进口来源多元化的政策建议。     

基于遥感蒸散数据的农田灌溉水平评价和设施建设分区研究

[目的]利用遥感蒸散数据快速评价农田年度灌溉水平,及时获取基本农田建设成效,为农田灌溉设施分区建设提供参考。[方法]以黑龙江省海伦市为研究区,MOD16蒸散产品为数据源,计算有效灌溉量、灌溉需水量和作物缺水指数,分别对研究区2013年的旱地和水田灌溉水平进行评价,并将评价结果与农田水利设施作用分值图进行叠加分析,在此基础上进行基本农田灌溉设施建设分区。[结果]2013年海伦市耕地灌溉水平整体较高,区域间建设水平差别较大,可划分为4个级别。其中1级和2级灌溉水平的地块面积比例可达78.06%,地域分布上看,北部、西部、中部乡镇灌溉水平较高,东南部乡镇灌溉水平相对较低。叠加得出8种组合类型,分为设施修建型、设施提升型和设施维护型3种分区类型。[结论]研究为高标准农田建设提供了更详细的农田灌溉水平评价方法和更具差别化的农田灌溉设施建设策略。     

农业机械化发展及其服务外包的原因分析——源自冀豫鲁3省问卷调查数据的证明

[目的]在统计描述华北平原农民的机械采纳率和机械采纳方式的基础上,分析农民的机械化需求和农机手的机械服务供给行为及其内在逻辑,从理论和实证两个维度对华北平原地区农业机械技术的特殊实现形式——服务外包做出解释。[方法]运用描述性统计分析和成本收益核算等方法,利用冀豫鲁3省779个农户和45个农机手的实地调研数据分别对农业机械化发展的前提条件和实现方式做出解释。[结果]农业劳动力成本上升与农机购置成本下降,共同刺激了农民的机械技术需求,是华北平原农业机械化快速发展的前提条件;持有农业机械的农民基于理性选择而向其他农民提供农机作业服务,是推动华北平原农业机械化以服务形式实现的主要原因。[结论]相关部门应继续探索高效的农机补贴模式,刺激农民的机械技术需求;农业机械支持政策可适当向农机合作社倾斜,提高农机手的组织化程度和服务数量与质量。     

水源地突发水污染政府应急预留水量需求预测

近年来,随着工业化和产业化的快速发展,水源地突发水污染事件频发。为应对紧急情况下的水资源非常规需求,保障经济社会稳定发展,政府需要安排一部分应急预留水量。如何合理预测政府应急预留水量的规模是一个值得探讨的课题。提出了一种基于案例推理技术的政府应急预留水量需求预测方法,具体思路是:借助于案例库,利用主成分分析法从众多影响水源地突发水污染事件的因素中提取出两两不相关的因子,以此作为案例的特征属性,基于加权曼哈顿距离测算相似度,依此得到匹配案例,进而测算政府应急预留水量。以太湖流域常州地区水源地为例,进行政府预留水量需求量的预测与分析。该方法可以为政府应急预留水量的配置、储备等提供相关理论知识。