The effect of foreign trade and investment liberalization on spatial concentration of economic activity

I examine the varying responses of countries to foreign trade and direct investment liberalization on spatial concentration of their economic activity by taking into consideration moderating factors such as their market size and level of economic development. I argue that liberalization increases the concentration under normal conditions but large market size, and underdevelopment can disperse economic activity. Using data from 168 countries for the period of time starting in 1980s, I found support for all hypotheses.     

RIDGE ESTIMATORS FOR PROBIT REGRESSION: WITH AN APPLICATION TO LABOUR MARKET DATA

In this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data are collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower mean squared error than the ML method for all different situations that have been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba dataset which is based on a labour market experiment.     

Robust influence diagnostics for generalized linear models with continuous responses

This article introduces two parametric robust diagnostic methods for detecting influential observations in the setting of generalized linear models with continuous responses. The legitimacy of the two proposed methods requires no knowledge of the true underlying distributions so long as their second moments exist. The performance of the two proposed influence diagnostic tools is investigated through limited simulation studies and the analyses of an illustration.     

The Impact of Agricultural Import Tariff on Economic Growth： Evidence From Mercosur Countries

This research paper attempts to determine the relationship between agricultural import tariff and economic growth of Mercosur countries over the period 1996-2007 using regression analysis as well as evaluates the gains and losses from the group＇s trade policy over the same period. The introductory part of this paper focuses on the dynamics of changes in economic growth, trade, and import tariff of these countries over the last two decades. The results of the performed regression analysis of panel data suggest that trade liberalization has a quantitatively significant positive effect on growth. By using the coefficient estimates on tariff, the authors perform a quantitative evaluation of gains and losses from trade policy, for which tariff measures to trade （imports） are used as a proxy.     

天津设施蔬菜种植户的生产决策行为及其影响因素分析

[目的]目前我国蔬菜已经超过粮食总产值,成为我国第一大农产品。蔬菜产业对促进农民增收,提高国民生活质量具有重要的战略意义。天津把"减粮增菜"作为"十三五"现代都市农业发展的重要目标之一,这一目标实现的关键是设施蔬菜种植户的经营决策行为。通过对天津32个村的223户设施蔬菜种植户的实地调研,掌握了天津设施蔬菜经营现状。[方法]构建了农户生产经营决策的评价指标体系,运用有序Logistic回归模型,分析蔬菜种植户的经营决策行为,并得出各影响因素的重要程度。[结果]农户年龄、生产年限、技术培训和政府的农业规划对农户设施生产决策行为具有显著性影响,而其他因素均不显著。[结论]文章得出政府应在农业技术培训、新型农业经营主体培育、财政支农政策、蔬菜产业发展规划等方面加大投入力度,以期推进设施蔬菜的规模化、科技化和专业化,从而促进农民增收、农业增效。     

Exchange rate returns and volatility: the role of time-varying rare disaster risks

This paper provides empirical evidence to the theoretical claim that rare disaster risks have predictability for exchange rate returns and volatility using a nonparametric quantile-based methodology. Using dollar-based exchange rates for Brazil, Russia, India, China, and South Africa, the quantile-causality test shows that indeed rare disaster-risks affects both returns and volatility over the majority of their respective conditional distributions. In addition, these effects are much stronger when compared to those using the British pound, especially in terms of currency returns.     

Impact of an urban growth boundary across the entire house price spectrum: The two-stage quantile spatial regression approach

This study provides evidence for the impact of an urban growth boundary (UGB) on house prices. The study employs a two-stage quantile spatial regression method on a dataset that inventories sales transactions of single-family houses within two miles of either side of the eastern boundary of the primary UGB in King County, Washington. The results show that while the UGB decreases house prices across the entire house price spectrum, the impact is uneven; it is most pronounced for houses in the 5th to 8th decile of prices. These findings should encourage policy makers to adopt sub-housing-market-level policy approaches to address UGB and other urban and regional development policies’ potential impacts on house prices.     

新疆棉农残膜回收行为影响因素及实证研究*——基于棉农的调研数据

［目的］为响应农业农村部“农业绿色发展五大行动”，加强农业面源污染防治，缓解新疆棉田残膜危害，基于新疆棉农的问卷调查研究棉农的残膜回收行为。［方法］采用描述性统计和Logistic回归模型分析影响新疆棉农残膜回收行为的因素。［结果］（1）新疆农用地膜回收率低，地膜污染形势严峻。（2）棉农对残膜危害认知缺乏，对残膜回收的间接价值认识不足，影响新疆残膜回收效率。（3）残膜回收的成本收益是影响棉农残膜回收行为的关键因素，其他如棉农年龄、种棉年限、对残膜危害的认知等自身特征，以及农户劳动力数量、政府对残膜污染的关注程度等因素对棉农的残膜回收行为有重要影响。［结论］网络及电视广播等现代媒体对棉农的残膜危害认知水平有较大影响，棉农更愿意采用成本更节约的焚烧、堆地的残膜处理方式。因此提出引导棉农残膜回收、提高棉田残膜回收效率的对策建议。     

农业农村双创工作的成效机制研究

[目的]近年来,创新创业的热潮大力兴起,如何对农民双创工作的成效机制进行研究,成为文章研究的重点。通过对农民双创工作中的创业者特征、创业者关系、政策适应与农业农村双创收益机理的研究,找出创业企业绩效演变机理。[方法]收集2017年2—5月重庆24个地区1 855位农民的调查问卷,采用专家评价法对双创收益、创业培训、创业者社会关系和创业政策适应的测量项目进行因子分析。[结果]对农民双创收益的影响因素进行分析讨论,得出学历水平、创业动机、创业风险意识、创业经营模式以及创业培训均对双创收益的提升有促进作用;创业者特征回归分析中R~2值由0.22提升为0.32,ΔR~2的值为0.34,说明自变量对因变量有预测作用;创业者特征对双创收益值为0.329,说明创业者的特征越高,其创业的绩效就越高;创业政策回归分析中R~2值由0.12增加到0.22,ΔR~2的值为0.14,说明自变量对模型有预测能力,且政策越全面有效,对农民的双创收益提升效果越好;创业者社会关系回归分析中R~2值由0.098提升为0.175,ΔR~2由0.175增长为0.433,说明创业者的社会关系越好,农民的双创收益越高。[结论]研究结果说明农业农村双创工作中创业者特征、创业者社会关系以及政策适应可提升创新创业机制的完善,提升双创收益。     

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.