农地流转与农户金融需求

以四川省为例,基于647份农户调查数据,从农地流转、农户基本情况、农户经营特征、农村金融市场特征4个方面运用Logit模型实证分析其对农户金融需求的影响方向及强度,分析农地流转背景下农户金融需求的影响因素。研究结果表明,农地流转行为带来农户金融需求分化趋势,农地流转价格过快上涨对农户金融需求有明显抑制作用,加快创新农村金融产品已经十分紧迫,农户普遍面临缺乏抵押融资能力的困境。农地流转对农户金融需求的分化性发展产生重要影响,进而对我国农业的转型升级具有极为积极的促进作用。在进一步深化农村土地产权制度改革基础之上,重点针对扩大土地经营规模的农户进行农村金融产品创新,有效满足其不断增长的金融需求,则是当前我国推进现代农业发展必须破解的主要难题。     

关于推进我国由核大国向核强国迈进的探讨与思考

论文阐述了推进我国核大国向核强国迈进的背景、规划、核战略产业、军民融合海陆空领域的发展。首先题解我国核大国、强国的顶层设计、国外背景、10大产业的大国强国方向;进而阐明了我国已步入核电大国行列,在役在建设核电站进入世界第三位,但核电在国际国内能源、电力中比重很小,迈向核电强国任务艰巨;进而阐明举全国之力做强我国核工业,建设更先进的核潜艇,研发中国战略重器核航空母舰;建立强大有后处理的核燃料循环,打造“中国造”华龙1号,全产业链走出去;实施一批重大工程建设,推动核科技进步,继续研发、部署小堆项目建设;抓紧以核电代煤、治霾、减碳、供热的开发工作;加大核科技在农学、医学、科研、军工的应用。认真贯彻《核安全法》确保核工业安全发展,健全完善核应急体系,推动核安保能力提升,不断完善核安全结构和核安全系统的顶层设计,保障核工业安全万无一失。抓好军民深度融合国家战略发展,军民对接,使命承担,发展军民融合产业。     

关于如何完善土地流转的制度探讨---基于日本农地中间管理制度的分析与启示

土地流转是实现农业现代化的基础条件之一,但是,实现土地有序流转需要建立完善的制度体系。日本近年实施的农地中间管理制度旨在通过建立农地中间管理机构(农地银行)及相应的流转机制和流转补贴制度,促进土地向农业承担主体集中,提高农业用地的利用效率和集约化程度,进而提高农业生产力。通过对该制度的细致总结和效果分析,认为中国的土地流转制度有必要以目标为导向,完善制度设计、细化政策措施、调整农业补贴并注重基层和农民参与。     

Population structure and correlation between auxiliary and target variables may affect precision of estimates in forest inventory

Simulations of forest inventory in several populations compared simple random with “quick probability proportional to size” (QPPS) sampling. The latter may be applied in the absence of a list sampling frame and/or prior measurement of the auxiliary variable. The correlation between the auxiliary and target variables required to render QPPS sampling more efficient than simple random sampling varied over the range 0.3–0.6 and was lower when sampling from populations that were skewed to the right. Two possible analytical estimators of the standard error of the estimate of the mean for QPPS sampling were found to be less reliable than bootstrapping.     

Marginal likelihood estimation from the Metropolis output: tips and tricks for efficient implementation in generalized linear latent variable models

The marginal likelihood can be notoriously difficult to compute, and particularly so in high-dimensional problems. Chib and Jeliazkov employed the local reversibility of the Metropolis–Hastings algorithm to construct an estimator in models where full conditional densities are not available analytically. The estimator is free of distributional assumptions and is directly linked to the simulation algorithm. However, it generally requires a sequence of reduced Markov chain Monte Carlo runs which makes the method computationally demanding especially in cases when the parameter space is large. In this article, we study the implementation of this estimator on latent variable models which embed independence of the responses to the observables given the latent variables (conditional or local independence). This property is employed in the construction of a multi-block Metropolis-within-Gibbs algorithm that allows to compute the estimator in a single run, regardless of the dimensionality of the parameter space. The counterpart one-block algorithm is also considered here, by pointing out the difference between the two approaches. The paper closes with the illustration of the estimator in simulated and real-life data sets.     

A Method to Handle Zero Counts in the Multinomial Model

In the context of an objective Bayesian approach to the multinomial model, Dirichlet(a, …, a) priors with a < 1 have previously been shown to be inadequate in the presence of zero counts, suggesting that the uniform prior (a = 1) is the preferred candidate. In the presence of many zero counts, however, this prior may not be satisfactory either. A model selection approach is proposed, allowing for the possibility of zero parameters corresponding to zero count categories. This approach results in a posterior mixture of Dirichlet distributions and marginal mixtures of beta distributions, which seem to avoid the problems that potentially result from the various proposed Dirichlet priors, in particular in the context of extreme data with zero counts.     

Quantile regression and variable selection for the single-index model

In this paper, we propose a new full iteration estimation method for quantile regression (QR) of the single-index model (SIM). The asymptotic properties of the proposed estimator are derived. Furthermore, we propose a variable selection procedure for the QR of SIM by combining the estimation method with the adaptive LASSO penalized method to get sparse estimation of the index parameter. The oracle properties of the variable selection method are established. Simulations with various non-normal errors are conducted to demonstrate the finite sample performance of the estimation method and the variable selection procedure. Furthermore, we illustrate the proposed method by analyzing a real data set.     

Complex Latent Variable Modeling in Educational Assessment

Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data.     

Inferences on functions of Pareto parameters with application to income inequality measures

The Theil, Pietra, Éltetö and Frigyes measures of income inequality associated with the Pareto distribution function are expressed in terms of parameters defining the Pareto distribution. Inference procedures based on the generalized variable method, the large sample method, and the Bayesian method for testing of, and constructing confidence interval for, these measures are discussed. The results of Monte Carlo study are used to compare the performance of the suggested inference procedures from a population characterized by a Pareto distribution.