专题资料2021-2022年SAS经济时间序列分各种模型分析.docx

目录

实验一分析太阳黑子数序列··········································3
实验二模拟AR模型·················································4
实验三模拟MA模型和ARMA模型····································6
实验四分析化工生产量数据··········································8
实验五模拟ARIMA模型和季节ARIMA模型···························10
实验六分析美国国民生产总值的季度数据·····························13
实验七分析国际航线月度旅客总数数据·······························16
实验八干预模型的建模·············································19
实验九传递函数模型的建模·········································22
实验十回归与时序相结合的建模·····································25
太阳黑子年度数据··················································28
美国国民收入数据··················································29
化工生产过程的产量数据············································30
国际航线月度旅客数据··············································30
洛杉矶臭氧每小时读数的月平均值数据································31
煤气炉数据························································35
芝加哥某食品公司大众食品周销售数据································37
牙膏市场占有率周数据··············································39
某公司汽车生产数据················································44
加拿大山猫数据····················································44

实验一分析太阳黑子数序列

一、实验目的：了解时间序列分析的基本步骤，熟悉SAS/ETS软件使用方法。
二、实验内容：分析太阳黑子数序列。
三、实验要求：了解时间序列分析的基本步骤，注意各种语句的输出结果。
四、实验时间：2小时。
五、实验软件：SAS系统。
六、实验步骤
1、开机进入SAS系统。
创建名为exp1的SAS数据集，即在窗中输入下列语句：
保存此步骤中的程序，供以后分析使用（只需按工具条上的保存按钮然后填写完提问后就可以把这段程序保存下来即可）。
绘数据与时间的关系图，初步识别序列，输入下列程序：
odshtml;
odslistingclose;
run;提交程序，在graph窗口中观察序列，可以看出此序列是均值平稳序列。

识别模型，输入如下程序。
提交程序，观察输出结果。初步识别序列为AR(2)模型。
估计和诊断。输入如下程序：
提交程序，观察输出结果。假设通过了白噪声检验，且模型合理，则进行预测。
进行预测，输入如下程序：
提交程序，观察输出结果。

退出SAS系统，关闭计算机。
总程序：
dataexp1;
infile"D:\exp1.txt";
inputa1@@;
year=intnx('year','1jan1742'd,_n_-1);
formatyearyear4.;
;
procprint;run;

odshtml;
odslistingclose;
procgplotdata=exp1;
symboli=splinev=doth=1cv=redci=greenw=1;
plota1*year/autovreflvref=2cframe=yellowcvref=black;
title"太阳黑子数序列";
run;
procarimadata=exp1;
identifyvar=a1nlag=24minicp=(0:5)q=(0:5);
estimatep=3;
forecastlead=6interval=yearid=yearou