序列相关的检验和修正.doc

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1、序列相关的检验及修正例题：中国居民总量消费函数数据：年份GDPCONSCPITAXGDPCXY19783605.61759.146.21519.287802.66678.93806.819794092.62011.547.07537.828694.77552.14273.419804592.92331.250.62571.709073.37943.94605.319815008.82627.951.90629.899650.98437.25063.419825590.02902.952.95700.0210557.192

2、35.15482.319836216.23231.154.00775.5911511.510075.25983.519847362.73742.055.47947.3513273.311565.46746.019859076.74687.460.652040.7914965.711600.87728.6198610508.55302.164.572090.3716274.613037.28211.4198712277.46126.169.302140.3617716.314627.88840.0198815388.678

3、68.182.302390.4718698.215793.69560.3198917311.38812.697.002727.4017846.715034.99085.2199019347.89450.9100.002821.8619347.816525.99450.9199122577.410730.6103.422990.1721830.818939.510375.7199227565.213000.1110.033296.9125052.422056.111815.1199336938.116412.1.20425

4、5.3029269.525897.613004.8199450217.421844.2156.655126.8832057.128784.213944.6199563216.928369.7183.416038.0434467.531175.415467.9199674163.633955.9.666909.8237331.933853.717092.5199781658.536921.5204.218234.0439987.535955.418080.2199886531.639229.3202.599262.8042

5、712.738140.519363.9199991125.041920.4.7210682.5845626.440277.620989.6200098749.045854.6200.5512581.5149239.142965.622864.42001108972.449213.2201.9415301.3853962.846385.624370.22002120350.352571.3200.3217636.4560079.051274.926243.72003136398.856834.4202.7320017.31

6、67281.057407.128034.52004160280.463833.5210.6324165.6876095.764622.730306.02005188692.171217.5214.4228778.5488001.274579.633214.02006221170.580120.5217.6534809.72101617.585624.136811.61、建立回归模型，模型的OLS估计（1）录入数据打开EViews6，点“File”®“New”®“Workfile”选择“Dated-regularfrequ

7、ency”，在Frequency后选择“Annual”，在Startdata后输入1978，在Enddata后输入2006，点击“ok”。在命令行输入：DATAXY，回车将数据复制粘贴到Group中的表格中：（2）估计回归方程在命令行输入命令：LSYCX，回车或者在主菜单中点“Quick”®“EstimateEquation”，在Specification中输入YCX，点“确定”。得到如下输出：写出估计结果：（6.243）（47.）=0.98800.9875F=2214.537D.W.=0.2771、序列相关的检验（1）

8、图示检验法作残差序列的时序图：保存残差虚列：GENRE=RESID作图：PLOTE从图上可以看出，模型的最小二乘残差开始连续几期小于0，接着连续几期都大于0，这种模式的残差意味着模型可能存在正的序列相关性。做和的关系图：SCATE(-1)E从上面的散点图可以看出，和之间可以拟合一个线性模型：=且回归直线的斜率为正（r