百度指数、混频模型与三亚旅游需求

Baidu Index,Mixed-frequency Model and Sanya Tourism Demand

旅游需求的预测对地区旅游业的发展具有重要意义。随着互联网的普及,游客出行前对目的地的搜索行为和关注度与该地区旅游需求密切相关,因此,可以运用反映搜索行为和关注度的百度指数数据对区域旅游需求进行预测。为克服同频模型仅能基于相同频率数据建模预测的局限性,文章以三亚市为例,根据混频模型的建模理论,分别构建基于百度指数周数据的单变量MIDAS模型和多变量MIDAS模型,对三亚市月度旅游需求预测。预测结果表明:百度指数周数据的加入能够改善同频模型的预测效果,且总体而言,多变量MIDAS模型的预测结果更具有准确性,同时证实了在短期伪样本外预测时主成分分析法与混频预测相结合能够进一步改善预测效果。使用百度指数与混频模型相结合的最优模型对2018年7月和8月的三亚旅游人数进行预测,预测结果显示三亚旅游人数呈现高于10%的较高速增长趋势。

Tourism can reflect the living standards and social development of a country.Predicting and accurately forecasting tourism demand can not only ensure efficient resource allocation and safe highquality services,but also help timely adjust the supply of related products or services to avoid imbalance between supply and demand.In order to obtain higher economic benefits,the forecast of tourism demand is particularly important.Forecast models chosen by early scholars often ignore relevant variables which have indicative roles in tourism demand.Moreover,these models do not make full use of relevant information to improve the forecast effect.In recent years,with the popularity of the Internet,search behavior and attention to destination before travel have grown closely related to the tourism demand of a given region.Therefore,the Baidu Index data which reflect the search behavior and attention to destination can be used to forecast regional tourism demand.Since the data frequency of the Baidu Index data often differ from regional tourism demand,traditional models cannot forecast regional tourism demand with mixed-frequency data.Doing so may lead to loss of information or inflated information,and then result in inaccurate analysis results and forecast.Hence,in order to overcome the limitations of the same-frequency model which can only be built with same-frequency data,according to the modeling theory of the mixed-frequency model,eight weekly Baidu Index data reflecting tourism demand for Sanya have been selected to construct univariate MIDAS models and multivariate MIDAS models respectively to forecast the monthly tourism demand for Sanya.The forecast results show that:first,since the Baidu Index has certain indications for tourism demand,the addition of Baidu Index data can improve the forecast effect;second,since the mixed-frequency model can make full use of data information,the forecast effect of the mixed-frequency models are more accurate than that of samefrequency models,and the univariate MIDAS models with different weekly Baidu Index data have different forecast effects.Furthermore,since multivariate models combine more information from Baidu Index data,in general,the forecast results of the multivariate models are better,and the forecast results of the C-MIDAS models with different weighting are different.This also proves that the combination of principal component analysis and mixed-frequency forecast can further improve the forecast effect when the number of forward forecast steps is small.At the same time,the MIDAS model can now forecast regional tourism demand according to the newly released weekly Baidu Index data.Through the combination of the Baidu Index and the mixed-frequency model,it is possible to forecast the number of tourists in Sanya for July and August 2018.The forecast results are in line with reality,showing that the number of tourists in Sanya is still growing at a higher rate than 10%.The MIDAS model can make real-time regional tourism demand forecasts more accurate.Therefore,the combination of the Baidu Index and the mixed-frequency model provides new ideas for the forecast of regional tourism demand,as well as a decision-making basis for tourism departments,which can now forecast regional tourism demand in advance to realize vigorous and healthy development of regional tourism.