Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009

In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem into a linear model selection and estimation problem. To this end, we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.     

Forecasting commodity price indexes using macroeconomic and financial predictors

Using a long sample of commodity spot price indexes over the period 1947–2010, we examine the out-of-sample predictability of commodity prices by means of macroeconomic and financial variables. Commodity currencies are found to have some predictive power at short (monthly and quarterly) forecast horizons, while growth in industrial production and the investment–capital ratio have some predictive power at longer (yearly) horizons. Commodity price predictability is strongest when based on multivariate approaches that account for parameter estimation error. Commodity price predictability varies substantially across economic states, being strongest during economic recessions.     

新疆艾维尔沟矿区焦煤井田地质灾害评估及防治措施

对拟建矿区进行地质灾害危险性现状评估,预测工程建设引发或加剧地质灾害的危险性程度,提出了相关的防治措施。     

国际工程承包项目失败因素的三角模糊数分析

本文分析了影响国际承包项目失败的因素并建立了相应的指标评价体系,介绍了运用三角模糊数互反判断矩阵[3]确定因素权重的方法。结合实例描述了此方法在确定国际承包项目失败因素权重上的应用,进而明确主要失败原因,不断认识项目在承包过程中出现的问题,使我国建筑企业在国际承包领域不断进步。     

基于灰色系统理论的江苏省入境旅游研究

入境旅游是旅游业的重要组成部分,本文运用灰色系统理论中的灰色关联度分析方法对江苏省旅游外汇收入与主要客源国入境旅游人次数之间的关联性进行分析,并建立了GM(1,1)灰色预测模型对江苏省典型创汇国未来的入境旅游客流规模进行预测,为江苏省入境旅游提供决策依据和方法参考。     

岩滩水电站水文预报方案研究

文章介绍了红水河岩滩水电站水文预报的方案、内容及作业方法。     

基于人机环境的链杆平衡模型在施工中的应用

根据在建筑工程的施工进度项目管理中人、机、环境等因素对施工进度的影响程度,应用项目管理中决策树的数学思想,建立人、机、环境一体化的链杆平衡管理建筑工程施工进度控制模型,研究了我国施工企业在施工项目进度控制方面的综合资源平衡问题,发现利用综合能力数来评价人、机、环境一体化对施工项目进度的影响,能有效地发挥人、机、环境一体化管理对施工过程进行控制的效应,从而使得施工项目进度最大限度的得以实现,并通过调整各分项工程的综合能力数来优化及合理分配资源。     

论如何提高地面气象测报工作质量

文章根据地面气象测报工作现状,结合笔者多年测报工作经验,谈几点提高地面测报工作质量的体会。     

Scoring rules and survey density forecasts

This article provides a practical evaluation of some leading density forecast scoring rules in the context of forecast surveys. We analyse the density forecasts of UK inflation obtained from the Bank of England’s Survey of External Forecasters, considering both the survey average forecasts published in the Bank’s quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank. The density forecasts are collected in histogram format, and the ranked probability score (RPS) is shown to have clear advantages over other scoring rules. Missing observations are a feature of forecast surveys, and we introduce an adjustment to the RPS, based on the Yates decomposition, to improve its comparative measurement of forecaster performance in the face of differential non-response. The new measure, denoted RPS*, is recommended to analysts of forecast surveys.     

基于Web和数据仓库的房地产市场预警预报系统研究

介绍了基于Web和数据仓库的房地产市场预警预报系统的体系结构，探索设计了房地产市场预警预报系统的技术与方法，并提出了基于Web和数据仓库的房地产市场预警预报系统的完整解决方案。