序列相关实验题目

1、“序列相关”实验报告学号： 姓名：操作说明：1、 修改原始数据：为避免抄袭书本及相互抄袭，调入原始数据后，马上对变量X、Y进行修改，以保证每人的上机数据都不相同。修改的具体做法是将被解释变量Y与解释变量X的数据都乘上各人的学号系数，例如，某同学学号尾号为21，则所有变量的数据都乘以1.21，即在Eviews命令栏中输入命令：Genr Y=Y*1.21其他变量相同的处理办法。2、 将文件名改格式为“长学号+姓名+序列相关”后再提交。3、将实验步骤中的输出结果以图形方式拷贝至每个题目下面，如果题目有其他要求，请按要求写出相应答案。提出问题：为了从总体上考察中国居民收入与消费的关系，建立居民总量消费

2、模型。 根据宏观经济学中的消费理论，结合对中国居民总消费的实际分析，可以假定居民总消费（Y）是由居民实际可支配收入（X）唯一决定的，即X是Y的唯一解释变量。采用19802013年消除价格因素的中国居民总量消费支出（Y）与收入（X）数据，建立居民总消费模型。 实验原始数据：表5.1.1中国居民总量消费支出与收入资料单位：亿元年份XY19807944.204605.5019818438.005063.9019829235.205482.40198310074.605983.20198411565.006745.70198511601.707729.20198613036.508210.901987

3、14627.708840.00198815794.009560.50198915035.509085.50199016525.909450.90199118939.6010375.80199222056.5011815.30199325897.3013004.70199428783.4013944.20199531175.4015467.90199633853.7017092.50199735956.2018080.60199838140.9019364.10199940277.0020989.30200042964.6022863.90200146413.6024480.4920025133

4、7.4426485.92200357512.9928436.74200464943.7030963.54200573987.9034026.07200686334.5537939.58200796877.3542232.572008112093.646232.672009120607.751530.082010133045.056817.072011146647.164712.022012155244.469002.392013173230.877198.39资料来源：根据中国统计年鉴（2001，2007，2014）整理。1、将原始数据转换为自己的处理数据，将数据处理后的表数据拷贝至下方：ob

5、sYX19809453.5984605.500198110041.225063.900198210989.895482.400198311988.775983.200198413762.356745.700198513806.027729.200198615513.448210.900198717406.968840.000198818794.869560.500198917892.259085.500199019665.829450.900199122538.1210375.80199226247.2411815.30199330817.7913004.70199434252.2513944

6、.20199537098.7315467.90199640285.9017092.50199742787.8818080.60199845387.6719364.10199947929.6320989.30200051127.8722863.90200155232.1824480.49200261091.5526485.92200368440.4628436.74200477283.0030963.54200588045.6034026.072006102738.137939.582007115284.042232.572008133391.446232.672009143523.251530

7、.082010158323.656817.072011174510.064712.022012184740.869002.392013206144.777198.392、将Y对X进行回归，将OLS输出结果拷贝至下方；Dependent Variable: YMethod: Least SquaresDate: 12/09/16 Time: 21:13Sample: 1980 2013Included observations: 34VariableCoefficientStd. Errort-StatisticProb.  X2.8188140.03171288.88678

8、0.0000C-7171.4241000.069-7.1709320.0000R-squared0.995966    Mean dependent var61956.97Adjusted R-squared0.995840    S.D. dependent var56840.89S.E. of regression3666.089    Akaike info criterion19.30866Sum squared resid4.30E+08  &#

9、160; Schwarz criterion19.39845Log likelihood-326.2472    Hannan-Quinn criter.19.33928F-statistic7900.860    Durbin-Watson stat0.379526Prob(F-statistic)0.0000003、画出resid对resid（-1）的散点图，并以文字说明是否存在序列相关；4、针对问题2的输出结果，DW=？是否存在一阶序列相关？如何判断？DW=0.379526=0.38，K=2，N=3

10、4，查表可知Dl=1.39，Du=1.51，因为c=-7171.424<DW=0.38<DL,所以存在一阶序列相关5、手工生成LM的滞后2阶的辅助回归，将检验的输出结果拷贝至下方，此时的LM=？卡方临界值=？并依此判断说明是否存在2阶序列相关；Dependent Variable: EMethod: Least SquaresDate: 12/09/16 Time: 21:32Sample (adjusted): 1982 2013Included observations: 32 after adjustmentsVariableCoefficientStd. Errort-St

11、atisticProb.  C72.65312656.90620.1105990.9127X-0.0101870.020532-0.4961190.6237E(-1)0.9498620.1878685.0560170.0000E(-2)-0.1637670.188874-0.8670710.3933R-squared0.654253    Mean dependent var-205.6696Adjusted R-squared0.617208    S.D. dependent var3622

12、.632S.E. of regression2241.328    Akaike info criterion18.38399Sum squared resid1.41E+08    Schwarz criterion18.56721Log likelihood-290.1439    Hannan-Quinn criter.18.44472F-statistic17.66134    Durbin-Watson stat2.19437

13、8Prob(F-statistic)0.0000016、利用Eviews的LM功能，将LM的滞后2阶的检验输出结果拷贝至下方，LM=?说明是否存在2阶序列相关；Breusch-Godfrey Serial Correlation LM Test:F-statistic9.927047    Prob. F(2,26)0.0006Obs*R-squared13.85549    Prob. Chi-Square(2)0.0010Test Equation:Dependent Variable: RESIDMethod

14、: Least SquaresDate: 12/09/16 Time: 21:41Sample: 1982 2013Included observations: 32Presample missing value lagged residuals set to zero.VariableCoefficientStd. Errort-StatisticProb.  C-839.8232710.0853-1.1827080.2476X0.0981930.0615761.5946610.1229E(-1)8.7424715.4423851.6063680.1203E(-2)-6.

15、8348544.100184-1.6669630.1075RESID(-1)-8.9589135.559210-1.6115440.1191RESID(-2)-0.9846991.091442-0.9022000.3752R-squared0.432984    Mean dependent var9.95E-14Adjusted R-squared0.323943    S.D. dependent var2130.117S.E. of regression1751.440   &#

16、160;Akaike info criterion17.94163Sum squared resid79756117    Schwarz criterion18.21645Log likelihood-281.0660    Hannan-Quinn criter.18.03272F-statistic3.970819    Durbin-Watson stat2.021610Prob(F-statistic)0.0082727、如果存在序列相关，将模型中加入适当的AR项，

17、将输出结果拷贝至下方，并写出此时的自相关系数r=？Dependent Variable: YMethod: Least SquaresDate: 12/09/16 Time: 22:12Sample (adjusted): 1981 2013Included observations: 33 after adjustmentsConvergence achieved after 6 iterationsVariableCoefficientStd. Errort-StatisticProb.  X0.7110320.01842538.591000.0000C4996.103

18、1836.8612.7199140.0108AR(1)0.8103910.1105717.3291580.0000R-squared0.998577    Mean dependent var42343.86Adjusted R-squared0.998482    S.D. dependent var33907.38S.E. of regression1321.166    Akaike info criterion17.29693Sum squared resid52364421