广东省农村基础设施建设与农业经济增长的关系探究

作为提高农业生产效率、改善农民生活质量及促进农村经济增长的重要物质基础,农村基础设施建设对扩大农村地区内需增长点,解决农村剩余劳动力就业问题以及全面推进城乡统筹发展具有至关重要的意义。广东省作为我国南部地区经济发达省份,具有丰富的农业生产自然资源,但其农村基础设施发展程度相对来说却比较低,其成为农村经济发展的"瓶颈",不利于农村经济结构优化升级。为了保障农村基础设施的供给与农村经济发展水平保持协调一致,文章以广东地区农村为例,采用多元非线性回归分析的方法,在论证农村基础设施建设对农村经济增长的作用机制的基础上,从农村基础设施建设与农业生产、非农生产和农民人均纯收入3方面的内在关系进行计量分析,结果表明,首先农村基础设施对影响农业经济增长的3方面因素都发挥了显著的规模经济效益;其次农村基础设施建设中的教育变量对农业经济增长的作用最为显著;再次,农业机械设施在农业生产中的作用相对于其他影响因素而言不太显著;最后,道路、通讯设施对非农生产和农民人均收入收入的贡献率相对其他变量数值较低。并提出健全农村基础设施建设投融资机制、加大农村基础设施的资金投入力度、提高农村基础设施建设和利用水平、全面推进农村基础设施建设进程等优化策略。     

Global value chains,large-scale farming,and poverty: Long-term effects in Senegal

This paper presents panel data evidence on the impact of expansion of global value chains and large-scale export-oriented farms in developing countries over almost a decade. We estimate the income effects of wage employment on large-scale farms in the horticultural export sector in Senegal, using data from two survey rounds covering a seven-years period of rapid expansion of the sector. We estimate average income effects as well as heterogeneous income effects, using fixed effects and quantile fixed effects regressions. We find that poverty and inequality reduced much faster in the research area than elsewhere in Senegal. Employment in the horticultural export sector is associated with higher household income and the income effect is strongest for the poorest households. Expansion of the horticultural export sector in Senegal has been particularly pro-poor through creating employment that is accessible and creates substantial income gains for the poorest half of the rural population. These pro-poor employment effects contrast with insights in the literature on increased inequality from rural wage employment.     

A Meta‐Analysis on the Elasticity of Demand for Pesticides

There is an increasing policy interest in pesticide taxation schemes as a measure to reduce harmful effects of pesticide use. The effectiveness of such tax depends, however, on the price elasticity of demand for pesticides. Moreover, information on these demand elasticities and their determinants is of crucial relevance for policy‐making and normative modeling approaches. In this article, we present a meta‐analysis based on studies that have estimated pesticide demand elasticities in Europe and North America. Our meta‐analysis reveals that the own‐price elasticities of demand for pesticides are, with a median of ?0.28, significantly smaller than zero, but also significantly larger than ?1, i.e. to be inelastic. We find that the demand for pesticides for special crops is less elastic than that for arable and grassland. In addition, the demand for herbicides is more elastic than for other pesticides. Studies that consider only short‐term horizons and little flexibility for farmers to adjust to price changes generate significantly less elastic pesticide demands. The results also indicate that more recent studies identify lower pesticide price elasticities of demand. Furthermore, we find that peer‐reviewed studies tend to find more inelastic results compared to grey literature.     

空间分析在住宅价值评估中应用研究综述

住宅是一种异质性商品，它的构成使用价值的各个特征之间有明显的差异，因此特征价格定价模型广泛用于住宅的价值评估。但是由于住宅数据中常常表现出空间问题，使得传统的特征价格模型不再适用，空间效应在不动产领域的存在引起了学者们的广泛关注。对空间分析在住宅价值评估中的应用研究进行了回顾和阐述，首先分析空间效应的来源和影响，讨论了在传统模型中纳入空间效应的重要性，接着阐述如何对这些空间效应进行有效建模和检验，并分析了进一步研究的方向。     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

Climate change adaptation actions by fish farmers: evidence from the Niger Delta Region of Nigeria

This paper examined climate change adaptation strategies in fish farming and the effect of such methods on the profit of fish farmers in the Niger Delta region of Nigeria, Africa’s most populous country. Using cross‐sectional data obtained from 420 fish farmers from the region and applying multivariate probit and instrumental variable regressions, the study found that fish farmers have adopted a broad range of strategies to address climate risk and that these have significantly increased farmers’ profit. Our findings indicated important relationships between certain farm, socio‐economic and institutional characteristics and the adaptation actions. The study provides useful insight into factors that potentially encourage the adoption of livelihood‐enhancing climate risk adaptation strategies by fish farmers in the Niger Delta region and similar contexts.     

Factors Influencing Organic and Fair Trade Coffee Growers Level of Engagement with Cooperatives: The Case of Coffee Farmers in Mexico

We study the factors influencing the percentage of organic and fair trade certified coffee sold through a cooperative by growers of five cooperatives in Mexico. The percentage of coffee sold through the cooperative was used as a proxy of growers’ engagement with a cooperative. Using factor analysis and a fractional probit regression, we evaluated the proposition that the level of engagement can be explained by transaction cost economics, social norms and connections, and farmer and farm business characteristics. We found that farm size, uncertainty regarding cooperative time of payment to the members and cooperative commitment on price to be paid negatively influence the level of engagement. In contrast, asset specificity, relational commitment, and price have a positive impact on engagement. Our results may help cooperatives and policy makers to build strategies aiming to increase this level of engagement. This is relevant because lower grower engagement has been found to be positively correlated with weak performing cooperatives.     

利用频移动态特征的辐射源目标盲分选

利用辐射源目标的频移动态特征进行盲分选时，针对已有的分选方法不能很好解决非合作接收数据非周期、间断、交叠的问题，提出了一种改进的逻辑回归分选算法,将信号分选转化为轨迹分类的问题来处理。首先，借鉴多目标航迹起始模型中的M/N逻辑法的框架，设计新的数据关联门限，以建立目标起始曲线；其次，采用最小二乘拟合方法替代卡尔曼滤波，设计新的外推规则，建立载频变化预测曲线；分析新的接收数据与预测曲线的关联性，不断迭代回归或形成新的分支，最后获得目标分选结果。仿真与实测试验表明，利用逻辑回归分选算法，对不同运动目标搭载的同频辐射源可进行自动分选，4个仿真目标与2个真实目标的分选正确率达到100%。同时，该方法拓展了辐射源目标盲分选中的动态频移特征应用，为辐射源个体识别提供了新的支持。     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.