Henderson‐Trending of Macroeconomic Variables and Forecasting Accuracy

Using a structural time‐series model, the forecasting accuracy of a wide range of macroeconomic variables is investigated. Specifically of importance is whether the Henderson moving‐average procedure distorts the underlying time‐series properties of the data for forecasting purposes. Given the weight of attention in the literature to the seasonal adjustment process used by various statistical agencies, this study hopes to address the dearth of literature on ‘trending’ procedures. Forecasts using both the trended and untrended series are generated. The forecasts are then made comparable by ‘detrending’ the trended forecasts, and comparing both series to the realised values. Forecasting accuracy is measured by a suite of common methods, and a test of significance of difference is applied to the respective root mean square errors. It is found that the Henderson procedure does not lead to deterioration in forecasting accuracy in Australian macroeconomic variables on most occasions, though the conclusions are very different between the one‐step‐ahead and multi‐step‐ahead forecasts. Copyright © 2011 John Wiley & Sons, Ltd.     

Mortality effects of temperature changes in the United Kingdom

Temperature changes are known to affect the social and environmental determinants of health in various ways. Consequently, excess deaths as a result of extreme weather conditions may increase over the coming decades because of climate change. In this paper, the relationship between trends in mortality and trends in temperature change (as a proxy) is investigated using annual data and for specified (warm and cold) periods during the year in the UK. A thoughtful statistical analysis is implemented and a new stochastic, central mortality rate model is proposed. The new model encompasses the good features of the Lee and Carter (Journal of the American Statistical Association, 1992, 87: 659–671) model and its recent extensions, and for the very first time includes an exogenous factor which is a temperature‐related factor. The new model is shown to provide a significantly better‐fitting performance and more interpretable forecasts. An illustrative example of pricing a life insurance product is provided and discussed.     

基于时间序列的灰色预测技术在估产模型中的应用

在建立估产模型过程中，引进基于时间序列的灰色预测技术，通过对样本点建立基于时间序列的灰色预测模型和常规的多元线性回归气象模型的分析比较，试图找到一种计算简单，数据要求少而精度较高，时效性较好的建模方法，为时间序列预测在农作物估产方面的应用作出一点探索。     

Do Long‐Run Theory Restrictions Help in Forecasting?

Do long‐run equilibrium relations suggested by economic theory help to improve the forecasting performance of a cointegrated vector error correction model (VECM)? In this paper we try to answer this question in the context of a two‐country model developed for the Canadian and US economies. We compare the forecasting performance of the exactly identified cointegrated VECMs to the performance of the over‐identified VECMs with the long‐run theory restrictions imposed. We allow for model uncertainty and conduct this comparison for every possible combination of the cointegration ranks of the Canadian and US models. We show that the over‐identified structural cointegrated models generally outperform the exactly identified models in forecasting Canadian macroeconomic variables. We also show that the pooled forecasts generated from the over‐identified models beat most of the individual exactly identified and over‐identified models as well as the VARs in levels and in differences. Copyright © 2011 John Wiley & Sons, Ltd.     

风力发电节能技术的状况分析

本文介绍了国内外风力发电产业发展概况,从风力资源、电网情况、制造能力、公众环保意识等方面指出了影响我国风电产业的主要因素,并提出了相关的解决办法。     

Real‐Time Forecasts of Inflation: The Role of Financial Variables

We present a mixed‐frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed‐frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Copyright © 2012 John Wiley & Sons, Ltd.     

基于回归分析与三次指数平滑的组合预测

组合预测是把几种单一的预测方法进行组合后得到更好的预测结果的方法.文章根据国家财政用于科学研究支出的统计数据,运用回归预测与三次指数平滑预测,而后求出组合预测的权重,最后得出更优的预测结果.     

基于协整理论的人民币/美元汇率组合预测模型

在人民币/美元汇率预测中,单一模型往往难以全面反映汇率的变化规律,为更有效地利用各个模型的优点,将不同的单一模型进行组合可以产生更好的预测精度.对ARMA模型、GARCH(1,1)模型、无偏灰色马尔科夫模型利用协整关系建立线性组合模型.实证表明:组合模型较被组合的各单模型的预测精度高.     

大气环境预测中一类多源问题计算方法的探讨

本文应用等效点源法,导出关于多个(点、线或面)大气污染源在某一评价点造成的总浓度的计算公式。拓广了Gifford的结果,为大气环境预测中处理多源问题,提供一种较为方便的计算方法。     

城市公共交通客流量预测序列模型

本文通过广州市公交线路调整研究的实践,设计了一套在计算机上实现的简便实用的城市公交客流量预测序列模型。它包括公交出行产生与吸引、公交山行分布和公交线路分配等3个子模型。输入客流调查和线网的有关数据,便得到一系列评价和比较公交线网布局合理与否的统计指标。