基于ARIMA模型的产业集群稳定性研究

通过统计分析产业集群月产出量数列平稳性,运用时间序列分析方法,建立产业集群ARIMA模型,预测在某一时间点的产业集群产出情况,为生态园产业链稳定性建设提供理论依据。通过实际数据研究,运用EViews软件,验证产业集群ARIMA模型可行性。为解决产业集群稳定性预测中"外部扰动"因素对稳定性的影响,提供了理论途径。为产业集群建设稳定持续的发展提供了科学有效的理论方法。     

Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon

This work aims to compare the forecast efficiency of different types of methodologies applied to Brazilian consumer inflation (Índice de Preços ao Consumidor Amplo; IPCA). We will compare forecasting models using disaggregated and aggregated data from IPCA over 12 months ahead. We used IPCA in a monthly basis, over the period between January 1996 and March 2012. Out-of-sample analysis will be made through the period of January 2008 to March 2012. The disaggregated models were estimated by Seasonal Autoregressive Integrated Moving Average (SARIMA) and will have different levels of disaggregation from IPCA as groups and items, as well as disaggregation with more economic sense used by Brazilian Central Bank as: (1) services, monitored prices, food and industrials and (2) durables, non-durables, semi-durables, services and monitored prices. Aggregated models will be estimated by time series techniques as SARIMA, state-space structural models and Markov-switching. The forecasting accuracy among models will be made by the selection model procedure known as Model Confidence Set developed by Peter Hansen, Asger Lunde and James Nason. We were able to find evidence of forecast accuracy gains in models using more disaggregated rather than aggregate data.     

基于ARIMA模型对南康家具产业劳动力就业趋势预测

对江西省赣州市南康家具产业在1993~2014年间的从业人数、就业弹性系数、结构偏离度等指标,采用ARIMA模型预测分析该产业劳动力就业的发展趋势。结果显示,它们分别为ARIMA(1,0,4)、ARIMA(3,0,5)、ARIMA(1,0,2)较理想的预测模型,这些模型提供了较准确的未来预测结果,为南康家具产业升级结构调整及其社会劳动保障部门提供一些参考依据。     

Trends in statistically based quarterly cash-flow prediction models

This paper provides a succinct review and synthesis of the literature on statistically based quarterly cash-flow prediction models. It reviews extant work on quarterly cash-flow prediction models including: (1) complex, cross-sectionally estimated disaggregated-accrual models attributed to 0170 and 0175 and Bernard and Stober (1989), (2) parsimonious ARIMA models attributed to Hopwood and McKeown (1992), (3) disaggregated-accrual, time-series regression models attributed to Lorek and Willinger (1996), and (4) parsimonious ARIMA models with both adjacent and seasonal characteristics attributed to 0120 and 0130. Due to the unavailability of long-term cash-flow forecasts attributed to analysts, increased importance has been placed upon the development of statistically based cash-flow prediction models given their use in firm valuation. Specific recommendations are also provided to enhance future research efforts in refining extant statistically based quarterly cash-flow prediction models.     

小波分析结合ARIMA组合模型的2014~2017年石油贸易量预测研究

选取2002~2013年我国石油进出口贸易量的数据进行建模分析。首先运用小波分析理论将贸易量数据进行分解,识别出数据的主要特征和细节特征,针对不同特征进行识别和平稳性检测和参数估计,建立相应的ARIMA模型,并进行预测加权合成。仿真结果表明,小波分析结合ARIMA组合模型的预测精度远远大于为改进的ARIMA预测模型,从而为科学合理的决策提供更为精确的预测模型。     

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。     

理性预期学派货币中性理论在中国的实证检验

利用中国1994年以来的经济数据,通过建立ARIMA模型和二阶段OLS模型对理性预期学派货币中性理论进行了检验。实证结果显示,短期内中国预期的货币供给冲击与非预期的货币供给冲击对产出都有显著影响,且非预期的货币供给冲击对产出影响更明显;但从长期看,预期货币与非预期货币对产出的正负效应相互抵消,中国货币政策表现为长期渐近中性。     

基于ARIMA模型的中国社会消费品零售总额季度预测

本文介绍了自回归单整平均移动模型即ARIMA模型的基本原理及其构建与应用的方法;并分析中国2003年-2010年社会消费品零售总额的季度数据,运用ARIMA(p,d,q)(P,D,Q)s模型,对历史数据-2010年四个季度社会消费品零售总额进行预测,其预测值与实际值拟合效果较好;在此基础上,预测出了2011年四个季度的社会消费品零售总额。     

基于BP神经网络和ARIMA组合模型的中国入境游客量预测

游客量的预测和分析是旅游规划与管理的基础性、关键性工作.目前,游客量预测主要采用基于传统研究方法或人工神经网络技术的单项预测方法.近年来的研究表明,组合预测方法比单项预测具有更高的预测精度.本文提出了一种基于BP神经网络和ARIMA组合模型的游客量预测新方法,对中国入境旅游人次数的变化趋势进行了综合分析与预测,预测结果表明这种方法相对于单一的预测方法具有更高的精度,该模型在旅游预测中的应用是可行、有效的.