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.     

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。     

光引发丙烯酰胺沉淀聚合研究

以丙酮/水为溶剂，采用紫外光（UV）引发丙烯酰胺（AM）沉淀聚合，考察了聚合反应的特征以及溶剂、初始单体浓度、引发剂浓度、表面入射光强、液层厚度等参数对聚合物分子量的影响。结果表明，紫外光引发丙烯酰胺沉淀聚合过程中单体转化率和聚合物分子量都随反应时间的延长而增加，光照80min时，单体转化率可达90％以上。所得聚合物分子量为10⁵～10⁶，提高单体浓度可使聚合物分子量增加，增加引发剂浓度和提高光强导致聚合物分子量降低，液层厚度对聚合物的分子量影响不大。所得聚合物粒子粒径约为200～300nm左右，粒径分布较为均匀，溶剂组成对聚合物粒子相貌影响较大，增加水含量可使粒子粒经变大但分散性变差。     

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     

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。     

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.     

我国居民消费水平的中长期预测

根据全国1992－1999年居民消费水平的统计数据，采用灰色系统理论的数据预测及缓冲算子公理，结合定性分析，建立了GM（1，1）模型，预测了我国2000－2005年居民消费水平的发展前景。     

Forecasting trend output in the Euro area

This paper is an applied study about forecasting trend output and the output gap in the Euro area. The need for trend output forecasts is justified by an analysis of the monetary strategy of the European Central Bank. Trend output serves as a direct inflation indicator and helps to determine the reference value for money. For both purposes, trend output has to be forecasted. A permanent–transitory decomposition based on cointegration restrictions gives an estimate of trend output in the Euro area. Ex‐ante point forecasts of trend output are computed and bootstrap simulation is employed to construct prediction intervals that take estimation uncertainty into consideration. The uncertainty of trend output and the output gap is quite large and raises questions about their usefulness as indicators for monetary policy. Copyright © 2002 John Wiley & Sons, Ltd.     

地理信息系统在滑坡预测预报中的应用

以滑坡监测数据为基础，采用地理信息系统技术，建立实用的滑坡预测预报软件系统。