西南交通大学本科毕业设计(论文) 第Ⅴ页
Abstract
CPI in China has been going up and down like never before since 2007. Affected by many factors, it rocketed by 108.5%, the biggest increase ever, in Februray of 2008, yet tumbled fast in the second half year of 2008 and the year of 2009. In July of 2009, it broke the record of the lowest price for the last decade by a decrease of 98.2%. The trend is hard to anticipate, for in 2010 and 2011, CPI in China again tended to go up. The increase and decrease of CPI have a deep and long-term influence on our people's life, the stability of our economy system and our national policies. This thesis is to study and analyze CPI based on its importance. Using the knowledge about time series I have learnt, basing on the CPI data from January of 2000 to December of 2010, I build an ARIMA forecast model. Then I analyze the short-term trend of CPI in 2011 in China and give some relating advice about future polices, which is meaningful and can help understand our country's macro-economy trend.
key words：ARIMA model,CPI,forcecast,analyze.
西南交通大学本科毕业设计(论文) 第Ⅵ页
目录
第1章 绪论 ····························· 1 1．1 论文的研究背景 ·························· 1
1.1.1 居民消费价格指数的概念介绍 ················· 2 1.1.2 居民消费价格指数的计算公式 ················· 3 1.2 研究目的 ····························· 3 1.3 研究的思路和内容 ························· 3 第2章 ARIMA模型理论概述 ······················ 5 2．1 ARIMA模型理论以及方法概述 ···················· 5
2．1．1 时间序列模型的含义 ···················· 5 2．1．2 随机时间序列模型 ····················· 5 2．1．3 自回归求积移动平均模型 ·················· 5 2．1．4 非平稳时间序列 ······················ 5 2．1．5 随机平稳时间序列样本的数字特征 ·············· 6 2．2 时间序列模型的建立过程 ······················ 7
2．2．1 数据的预处理（时间序列平稳性的判断） ··········· 7 2．2．2 模型的识别 ························ 9 2．2．3 模型参数的估计 ····················· 10 2．2．4 模型的定阶 ······················· 10 2．2．5 模型的检验 ······················· 10 第3章 ARIMA模型在居民消费价格指数中的定量分析 ·········· 13 3．1 数据的预处理 ·························· 13
3．1.1 序列的直方图及相关统计量 ················ 13 3．1．2 序列与正态分布之间的Q-Q图 ··············· 13 3．2 相关分析 ···························· 14 3．3 对序列X1(t)作描述性统计 ···················· 16 3．4 序列X2(t)的相关分析 ······················ 17 3．5 模型识别及参数估计 ······················· 18 3．6 模型建立及初步定阶 ······················· 19 3．7 适应性检验 ··························· 21
西南交通大学本科毕业设计(论文) 第Ⅵ页 3．8 模型预测值与真实值对比 ····················· 23 3．9 对未来三个月CPI的预测 ····················· 24 第4章 中国居民消费价格指数短期走势的定性分析 ··········· 25 4.1 2011年物价水平仍然大致可控 ··················· 25 4.2 政策建议 ···························· 26 结论： 致谢
参考文献附录
27 28
29 30
······························ ······························ ······························ ······························ 西南交通大学本科毕业论文 第1 页
第1章 绪论
1．1 论文的研究背景
根据国家统计局发布的数据来看，2011年3月居民消费价格指数(为了方便，在下文中将直接写作其英文缩写CPI)同比上涨5.4%，创下了自2008年以来的最高值。2011年4月CPI同比上涨5.3%，食品价格上涨11.5%。CPI持续高涨，已经到了影响居民大众的日常生活水平的严峻形势。我们不禁就要问了，这种通货膨胀现象还会持续多久？面对通货膨胀国家、社会、民众应该如何应对？近期央行是否会再次作加息调整？未来几个月的CPI是否还会创出CPI同比新高？
我们不妨对自2000年至2010年期间的CPI历史统计数据进行一个简单的直观分析，如下图。
11010810610410210098000102030405060708091011x 图1：中国2000年1月至2010年12月CPI数据的直观图
从图1我们可以很明显的看出：
1．CPI指数大起大落，波动幅度在历史上绝无仅有
2007年6月前的CPI数据波动不大，是因为央行采取稳健的财政政策和稳定的货币政策，经济水平较为稳定，保持着高经济增长，低通胀的良好经济大局。 2．第一次CPI的高调上涨
1
相关推荐：
loading