基于ARIMA模型的我国旅游外汇收入状况研究
发布时间：2018-08-03 21:52
【摘要】：我国有着丰富的旅游业资源，巨大的旅游客源市场。因此,为了制定更为合理、高效的旅游业相关政策，我们有必要对我国的旅游市场进行充分的研究。伴随着我国经济的快速发展，国际影响力的提升，越来越多的外国人都渴望进一步了解中国。由此，旅游行业逐年升温，，其收益已经占据我国经济收入的很大份额，还有更大的发展潜力。外国旅游者来我国进行游览、观光、休闲度假等活动，称之为入境旅游。它是一个国家获取外汇和解决就业的重要途径。 本文介绍了时间序列预测基本理论，重点说明了ARMA模型与ARIMA模型的建立过程，其中包括模型的平稳性检验，模型的定阶，模型的参数估计与检验。以及非平稳时间序列化为平稳时间序列的方法与模型的预测。 本文分别以我国旅游外汇收入的年度数据和月度数据为研究对象，利用时间序列分析技术,基于模型对旅游外汇收入数据进行分析，利用Eviews6.0软件设定模型及参数估计，得到我国旅游外汇收入的年度预测值与月度预测值，并与实际值进行对比，对模型做出评价，并对未来旅游外汇收入进行预测，对旅游政策的制定具有很好的指导意义。
[Abstract]:Our country has rich tourism resources, huge tourist source market. Therefore, in order to make more reasonable and efficient tourism related policies, it is necessary for us to study the tourism market in our country. With the rapid development of China's economy and the improvement of international influence, more and more foreigners are eager to learn more about China. As a result, tourism industry heats up year by year, its income has already occupied the very big share of our country economy income, also has the bigger development potential. Foreign tourists come to our country for sightseeing, leisure vacation and other activities, called inbound tourism. It is an important way for a country to obtain foreign exchange and solve employment. In this paper, the basic theory of time series prediction is introduced, and the establishment process of ARMA model and ARIMA model is emphasized, including the stationary test of the model, the determination of the order of the model, and the parameter estimation and test of the model. And the method and model prediction of non-stationary time series into stationary time series. In this paper, the annual and monthly data of China's tourism foreign exchange income are taken as the research objects, using time series analysis technology, based on the model to analyze the tourism foreign exchange income data, using Eviews6.0 software to set up the model and parameter estimation. The annual forecast value and monthly forecast value of China's tourism foreign exchange income are obtained, and compared with the actual value, the model is evaluated and the future tourism foreign exchange income is forecasted, which has a good guiding significance for the formulation of tourism policy.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F592;F832.6;F224
本文编号：2163102
[Abstract]:Our country has rich tourism resources, huge tourist source market. Therefore, in order to make more reasonable and efficient tourism related policies, it is necessary for us to study the tourism market in our country. With the rapid development of China's economy and the improvement of international influence, more and more foreigners are eager to learn more about China. As a result, tourism industry heats up year by year, its income has already occupied the very big share of our country economy income, also has the bigger development potential. Foreign tourists come to our country for sightseeing, leisure vacation and other activities, called inbound tourism. It is an important way for a country to obtain foreign exchange and solve employment. In this paper, the basic theory of time series prediction is introduced, and the establishment process of ARMA model and ARIMA model is emphasized, including the stationary test of the model, the determination of the order of the model, and the parameter estimation and test of the model. And the method and model prediction of non-stationary time series into stationary time series. In this paper, the annual and monthly data of China's tourism foreign exchange income are taken as the research objects, using time series analysis technology, based on the model to analyze the tourism foreign exchange income data, using Eviews6.0 software to set up the model and parameter estimation. The annual forecast value and monthly forecast value of China's tourism foreign exchange income are obtained, and compared with the actual value, the model is evaluated and the future tourism foreign exchange income is forecasted, which has a good guiding significance for the formulation of tourism policy.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F592;F832.6;F224
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