农业生态产品的概念内涵和价值实现路径

目的 农业生态产品价值实现是全面推进乡村振兴和加快发展方式绿色转型的有效途径。方法 文章通过文献梳理,界定农业生态产品的概念与内涵,阐述农业生态产品价值的构成与特征,分析了农业生态产品价值实现面临的挑战,探讨实现路径。结果 （1）农业生态产品是以“农业”为主体、“生态”为核心、“产品”为目的生态友好型产品,本质是要坚持生态优先、绿色发展,促进农业发展方式转变,推动农业农村高质量发展。（2）农业生态产品范畴是基于农业生态系统提供的生产、生态和生活的“三生”功能,涵盖了物质供给类、调节服务类和文化服务类3类产品。（3）农业生态产品价值实现应以实现优质优价、推进生态购买、拓展农业多功能性等为手段,分类施策,推进3类产品价值实现。结论 建议推进农业生态产品价值实现的标准体系建设、生产补贴制度完善等,依托国家农业绿色发展先行区等建立农业生态产品价值实现试点,打造农业生态产品价值实现的典型样板。     

我国政府在市场化进程中的作用测度--31省市(自治区)的比较分析

本文对我国市场化过程中的政府作用进行研究,建立了一套符合我国政府市场化发展实际情况的统计指标体系,采用科学的统计分析方法,进行各分项评价及综合评价,并对31省市(自治区)进行了排序,得到了比较切合实际的结果。     

Can univariate models forecast turning points in seasonal economic time series?

Based on a particular class of recently developed unobserved component models with, time varying parameters, the objectives of this paper are two-fold. On the one hand, we propose an alternative measure of underlying growth based on our estimated trend derivative with no need for any further transformations. Additionally, using the information embedded on the trend derivative, we provide a simple method for improving quantitative point forecasts in the vicinity of turning points. Empirical applications are presented for a set of seasonal monthly economic indicators of the Spanish economy.     

.NET分布式构架方案及分布式应用设计

MicrosoftR○.NET远4程构架为在不同的应用域(AppDomains)、不同的进程和不同计算机中活动对象的交互提供了系统框架支持,使其相互之间能够进行畅通无阻的通信。分布式对象处理(Remoting)依托.NET远程处理框架运行库及编程模型的支持,使得分布式对象处理变得异常简捷透明。探索了可利用的.NET远程处理方案,并详细探讨了分布式对象处理实例。     

土地财政构成要素时空差异与调控政策研究土地财政构成要素时空差异与调控政策研究

研究目的：探明土地财政构成要素时空差异规律,构建差异化、具有现实可操作性的调控政策。研究方法：系统 分析方法、定量比较方法、聚类分析和定性分析方法。研究结果：31 省（市、区）2003 — 2013 年土地财政各构成要素数 值在时间与空间上存在的基本规律：（1）时间上土地财政各构成要素呈现快速上涨趋势;（2）空间上呈现与经济社会发 展水平相趋同的区域规律。将 31 省（市、区）土地财政构成划分为淡出、繁荣、发展、起步时期 4 类区域。横向比较 4 个 区域时间序列上的土地出让价格、土地财政贡献率、协议价格、招拍挂价格、招拍挂面积比例等相对指标变化情况,测 算土地财政构成要素总体活跃程度,发现其与各区域土地财政所处发展时期非常吻合。研究结论：应从控制高价“地 王”现象频出、征收持有环节房地产税、发展战略新兴产业创造税源、产业发展替代土地财政为动力的城镇化、允许发 行地方债券和加大中央财政转移支付力度等方面构建区域土地财政转型调控政策。     

江西省县域经济的综合评价与分析——基于主成分分析法

定量研究了江西省县域经济的综合绩效,建立了县域经济综合评价指标体系,以江西省80个县市为研究对象,运用主成分分析法,对江西省各县市的县域经济进行了综合评价,并得出了相关结论。     

Learning,disagreement and inflation forecasting

This paper studies inflation forecasting based on the Bayesian learning algorithm which simultaneously learns about parameters and state variables. The Bayesian learning method updates posterior beliefs with accumulating information from inflation and disagreement about expected inflation from the Survey of Professional Forecasters (SPF). The empirical results show that Bayesian learning helps refine inflation forecasts at all horizons over time. Incorporating a Student’s t innovation improves the accuracy of long-term inflation forecasts. Including disagreement has an effect on refining short-term inflation density forecasts. Furthermore, there is strong evidence supporting a positive correlation between disagreement and trend inflation uncertainty. Our findings are helpful for policymakers when they forecast the future and make forward-looking decisions.     

Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach

The present paper introduces a novel method for the construction of Socioeconomic Status (SES) indices that are specific to a target variable of interest. It is based on the Sufficient Dimension Reduction (SDR) paradigm and uses a factorized model-based approach to simultaneously deal with predictor variables of mixed nature (i.e. quantitative, binary, and ordinal), which are usual in microeconomic data. These SES indices also identify relevant predictor variables using a two-step regularized matrix factorization approach. Using data from household surveys for Argentina (Encuesta Permanente de Hogares-EPH), the proposed method is compared with other existing dimension reduction algorithms such as standard Principal Component Analysis (PCA) and its version for mixed variables, regression on the full set of variables and Least Absolute Shrinkage and Selection Operator regression (LASSO).     

Quantifying the uncertainty of long-term macroeconomic projections

We propose a practical approach to measuring the uncertainty of long-term economic projections. The presented method quantifies the uncertainty of economic variables by using simulations from a multivariate unobserved components model in which variables are formulated as sums of stationary and nonstationary components. The method captures the correlations between both the stationary and nonstationary components of the variables and offers a seamless analysis of short- and long-term uncertainty. Experiments on artificial data demonstrate that, despite its simplicity, the method performs fairly well compared with alternative methods in terms of long-term predictive accuracy and coverage.     

Real-time inflation forecasting using non-linear dimension reduction techniques

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower-dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive with linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic.