Forecasting in long horizons using smoothed direct forecast

This paper constructs a forecast method that obtains long‐horizon forecasts with improved performance through modification of the direct forecast approach. Direct forecasts are more robust to model misspecification compared to iterated forecasts, which makes them preferable in long horizons. However, direct forecast estimates tend to have jagged shapes across horizons. Our forecast method aims to “smooth out” erratic estimates across horizons while maintaining the robust aspect of direct forecasts through ridge regression, which is a restricted regression on the first differences of regression coefficients. The forecasts are compared to the conventional iterated and direct forecasts in two empirical applications: real oil prices and US macroeconomic series. In both applications, our method shows improvement over direct forecasts.     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

城乡贫富差距中的人口因素探析

城乡人口差异是导致城乡贫富差距的重要根源,解决城乡贫富差距问题应该逐渐降低农村人口比例、提高农村人口素质。     

用带时序子模块的系统动力学模型预测Brent原油价格

提出在系统动力学模型中加入时序自回归AR(p)子模块，并且自动确定每个AR(p)子模块的阶数p。带时序子模块的系统动力学模型既体现了因素问的横向因果关系，又体现了每个因素的纵向关系。建立欧佩克(OPEC)石油产量、世界GDP、煤炭产量与价格、天然气、需求量、供求差额、消费系数、非欧佩克石油产量、非欧佩克供求差额，石油库存和OPEC组织的期望油价共十一个因素影响下的Brent原油价格预测模型，模型的预测结果表明：在系统动力学模型中加入时序自回归AR(p)子模块的方法是可行的、有效的，并且能提高模型的预测精度。     

小麦群体连续选择效应的综合分析

本试验对由信谷核不育基因构成的异效群体内４个性状进行连续两轮歧化选择，并将所得数据进行群体综合分析。结果表明：整个群体的平均表现和变异程度未发明显变化；但群体分布状况有一定改变，尤其是每穗粒数的分布数化较为明显；株高和千粒重的有效因子数估计基本符合小麦遗传实际表现，每穗粒数的有效有效因子数估计略低，说明此性状遗传背景较为简单。     

一种基于B/S的农业资源综合数据服务系统──通用农业资源数据服务系统开发设计

基于浏览器 /服务器 (B/S)结构的通用农业资源数据库系统 ,将农业统计中常用的 5 0 0多个技术指标分成三大类 ,17个小类 ,基本涵盖了农业生产和管理统计的主要领域 普通用户通过浏览器实现对数据库的组合查询或浏览 ,也可将数据文件下载 系统提供了 9种统计分析方法以及 5种预测方法 ,授权用户可进行数据文件上载 管理员通过浏览器可对数据库进行管理维护     

人口密度与中国封建小农经济的生成

中国封建小农经济的生成是社会发展中综合因素促成的结果.从被同人学者所忽略的人口密度与生产方式即经济形态的关系来论证，中国从战国以来就已经是一个人口密集的国家;正是这种人口密度大的状况，导致了中国封建社会没有走西欧封建社会那种领主制下的大庄园经济的道路，而是走了中国独特的地主制下的小农户经济的道路.     

能源供求予测模型

本文论述了三种予测能源需求的方法,并对其作了评价;提出了一种能源供应予测模型;最后对我省能源、经济、予测研究提出了建议。     

非线性生育率阶段结构的收获模型

建立并分析了一类非线性生育率阶段结构的单种群的收获模型，得到了正平衡点全局稳定的条件，给出了最优收获策略．     

A ground‐level ozone forecasting model for Santiago,Chile

A physically based model for ground‐level ozone forecasting is evaluated for Santiago, Chile. The model predicts the daily peak ozone concentration, with the daily rise of air temperature as input variable; weekends and rainy days appear as interventions. This model was used to analyse historical data, using the Linear Transfer Function/Finite Impulse Response (LTF/FIR) formalism; the Simultaneous Transfer Function (STF) method was used to analyse several monitoring stations together. Model evaluation showed a good forecasting performance across stations—for low and high ozone impacts—with power of detection (POD) values between 70 and 100%, Heidke's Skill Scores between 40% and 70% and low false alarm rates (FAR). The model consistently outperforms a pure persistence forecast. Model performance was not sensitive to different implementation options. The model performance degrades for two‐ and three‐days ahead forecast, but is still acceptable for the purpose of developing an environmental warning system at Santiago. Copyright © 2002 John Wiley & Sons, Ltd.