When are prediction market prices most informative?

Prediction markets are a popular platform for the elicitation of incentivised crowd predictions. This paper examines the variation in the information contained in prediction market prices by studying Intrade prices on U.S. elections around the release of opinion polls. We find that poll releases stimulate an immediate uptick in trading activity. However, much of this activity involves relatively inexperienced traders, meaning that the price efficiency declines in the immediate aftermath of a poll release, and does not recover until more experienced traders enter the market in the following hours. More generally, this suggests that information releases do not necessarily improve prediction market forecasts, but instead may attract noise traders who temporarily reduce the price efficiency.     

Anomalies in macroeconomic prediction errors–evidence from Chilean private forecasters

Part of a prediction is the judgment applied by the forecaster. This judgmental input may be affected by the forecaster’s mood swings, which have been shown to affect, for example, stock market returns. The present paper analyzes the extent to which mood (approximated by the development in sentiment indicators) affects macroeconomic prediction errors; i.e., whether it explains part of the prediction bias. The evidence suggests that mood can explain part of the error in inflation and output growth predictions, and hence, that anomalies should be taken into account when trying to understand expectation formation and assess the uncertainty related to private forecasters’ point predictions.     

应用组合模型对河南省碳排放量的预测

过多依赖化石能源的消费,进而引起全球气候的问题越来越严重。中国经济的快速发展所伴随的高耗能产业等对化石燃料的消耗,成为当前发展低碳经济的重要阻碍。河南省作为发展中原经济区的一个非常重要的成员,对于中国的低碳经济发展起到非常重要的作用。本文在对河南省碳排放量测算的基础上,并对未来几年的碳排放量的进行预测,并提出合理建议。     

限购令环境下基于灰色预测模型的地价预测

在限购令的环境下,以湖南省长沙市的城市住宅用地的地价水平为研究基础,运用灰色系统理论GM（1,1）模型,预测城市住宅用地的地价水平的发展趋势,从而得出相应的结论并为土地管理部门和房地产商提供参考。最后得出结论：近期长沙市住宅用地价格回落的可能性较小,并且还存在小幅上升的可能。     

支持向量机在财务危机预警中的应用评析

信息技术的迅速发展和世界经济全球化使很多企业面临各种各样的经营困难,最终体现为财务危机。提前对其进行预警以协助企业有效地规避财务危机是十分重要的。随着研究的深入,各种财务危机预警方法逐渐成熟。本文从样本选择、模型建立和预测性能3方面对支持向量机（SVM）在财务危机预警领域的应用进行了评析,认为SVM方法具有强大的分类功能和优良的学习性能,在财务危机预警领域有着广阔的应用前景。     

基于ARMA模型的我国国内旅游客源预测

运用ARMA(1,2)模型对我国1994~2009年游客人数进行分析并选取2004~2007年的游客人数作为原始数据建模,同时依据该模型计算2007~2009年我国的游客人数,并对2010~2012年的国内旅游人数进行预测,结果显示:预测结果与实际情况拟合得很好,游客数量会持续上升。旅游管理部门应根据实际情况和预测结果合理地安排旅游景点的建筑服务设施,提高旅游产品的质量与旅游接待水平,促进旅游业的健康发展。     

基于BP神经网络模型的宿州市人均生态足迹预测研究

利用人均生态足迹对宿州市生态形势进行评价,发现1997-2004年生态环境趋于恶化,经济快速发展过度消耗资源,并对环境造成一定程度危害,而2005～2007年生态环境趋于好转且态势平稳;并发现人均生态足迹的变化是宿州市生态安全形势的表征因子.在此基础上利用BP神经网络模型对宿州市1997-2005年生态足迹变化的影响因...     

基于ARIMA模型的黄金价格短期分析预测

2008年国际金融危机以来,国际黄金价格呈现了V型反转走势,已创下每盎司1400美元的历史最高纪录。黄金价格一路走强,对国家、企业、个人的投资决策都将产生深远影响。本文以1973年1月—2010年11月伦敦现货黄金月度价格为依据,通过建立ARIMA模型,对2011上半年的黄金价格走势进行预测分析,并得出短期内国际黄金价格将继续上涨的结论,为我国调整外汇储备结构、增加黄金储备提供了政策依据。     

生存分析在股市期市涨跌预测中的应用

运用生存分析（Survival Analysis）方法和台湾2009-2011年每日发布的公开数据,建立Cox回归模型（Cox regression model）,预测台湾隔日加权股价指数期货涨跌。实证分析显示:涨幅模型预测结果的正确率为74.47%,绩效验证中平均收益有35.73点;跌幅模型预测的正确率为75.32%,绩效验证中平均收益有37.99点;将涨跌幅模型综合在一起考虑时,在绩效验证中可知平均收益可达38.68点。     

Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor

Abstract

This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965–1994. The spatial autocorrelation due to neighbouring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states’ demand for liquor for 1 year and 5 years ahead. The estimators whose predictions are compared include OLS, fixed effects ignoring spatial correlation, fixed effects with spatial correlation, random-effects GLS estimator ignoring spatial correlation and random-effects estimator accounting for the spatial correlation. Based on RMSE forecast performance, estimators that take into account spatial correlation and heterogeneity across the states perform the best for forecasts 1 year ahead. However, for forecasts 2–5 years ahead, estimators that take into account the heterogeneity across the states yield the best forecasts.