百度指数与旅游景区游客量的关系及预测研究——以北京故宫为例

发布时间：2018-01-12 19:26

本文关键词：百度指数与旅游景区游客量的关系及预测研究——以北京故宫为例　出处：《旅游学刊》2013年11期 　论文类型：期刊论文

【摘要】：网络搜索数据记录了用户的搜索关注与需求,为研究旅游经济行为提供了必要数据基础。文章基于百度指数,以北京故宫为例,利用计量经济学中的协整理论和格兰杰因果关系分析了百度关键词与北京故宫实际游客量间的关系,建立了没有百度关键词和加入百度关键词的两种预测模型并进行了预测精度比较。结果表明:故宫实际游客量与百度关键词存在长期均衡关系和格兰杰因果关系;加入百度关键词后的自回归分布滞后模型的样本期内的预测精度比没有百度关键词的ARMA模型提高了12.4%,样本期外的预测精度提高了14.5%。运用带有百度关键词的模型可以实现利用当天及滞后1~2天的百度指数数据预测故宫当天的游客量,不仅增强了预测的时效性,还可以更加及时、准确地为故宫景区管理部门提供决策的依据。
[Abstract]:Record search concerns and needs of users of web search, and provide data basis for the study of tourism economic behavior. Based on the Baidu index, the Imperial Palace in Beijing as an example, using econometric cointegration theory and Grainger causality analysis of the relationship between the Baidu keyword and actual amount of tourists between Beijing the Imperial Palace, built without Baidu keywords and join Baidu keyword two prediction model and the prediction accuracy. The results show that the actual the Imperial Palace tourist amount and Baidu keyword there is a long-term equilibrium relationship and Grainger causality; autoregressive distributed lag model prediction accuracy of the sample period was 12.4% higher than the ARMA model without Baidu keyword to join Baidu keyword. The prediction accuracy of sample period increased by 14.5%. by using a Baidu keyword model can be achieved by day and lag 1~2 The prediction of Baidu's index data on the day of the Imperial Palace not only enhances the timeliness of prediction, but also provides more timely and accurate basis for decision-making of the Imperial Palace scenic area management department.

【作者单位】：北京联合大学旅游学院;
【分类号】：F592.7
【正文快照】： 一、前言随着我国的旅游事业的迅速发展,国内一些著名的旅游城市、景区景点的游客量日益攀升,特别是“五一”、“十一”长假,国内多个地区的旅游景点人满为患,客流量爆棚,远超出景区的承载能力。2012年,“十一”长假陕西华山旅游景区甚至因此爆发了轰动全国的伤人事件,引起了

【参考文献】