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实验目的：掌握自相关问题的检验以及相关的Eviews的操作方法。实验内容：消费总量的多少主要有GDP决定。为了考察GDP对消费总额的影响，可使用如下模型：Y=；其中，X表示GDP，Y表示消费总量。下表列出了中国1990-2000的GDP的X与消费总额Y的统计数据。
| 年份 | GDP（X） | 消费总额（Y） | 年份 | GDP（X） | 消费总额（Y） |
| 1990 | 18319.5 | 11365.2 | 1998 | 79003.3 | 405.9 |
| 1991 | 21280.4 | 13145.9 | 1999 | 82673.2 | 49722.8 |
| 1992 | 25863.7 | 15952.1 | 2000 | 112.5 | 54617.2 |
| 1993 | 34500.7 | 20182.1 | 2001 | 98592.9 | 527.4 |
| 1994 | 46690.7 | 26796 | 2002 | 1077.6 | 62798.5 |
| 1995 | 58510.5 | 33635 | 2003 | 121730.3 | 67493.5 |
| 1996 | 68330.4 | 40003.9 | 2004 | 142394.2 | 75439.7 |
| 1997 | 744.2 | 43579.4 |
OLS法的估计结果如下：
Y=2329.401+0.546950X
（1.954322）（36.71110）
R=0.990446，=0.9711，SE=2091.475，D.W.=0.478071。
二、进行序列相关性检验
（1）图示检验法
（2）回归检验法
一阶回归检验
二阶回归检验
=1.144406e－0.343796e+ε
3）拉格朗日乘数（LM）检验法
| Breusch-Godfrey Serial Correlation LM Test: | ||||
| F-statistic | 29.41781 | Probability | 0.000038 | |
| Obs*R-squared | 12.63731 | Probability | 0.001802 | |
| Test Equation: | ||||
| Dependent Variable: RESID | ||||
| Method: Least Squares | ||||
| Date: 12/17/12 Time: 21:51 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | 37.31393 | 4.3315 | 0.057911 | 0.9549 |
| X | -0.002008 | 0.009377 | -0.214144 | 0.8344 |
| RESID(-1) | 1.744086 | 0.234326 | 7.442998 | 0.0000 |
| RESID(-2) | -1.088243 | 0.315853 | -3.445408 | 0.0055 |
| R-squared | 0.842487 | Mean dependent var | 4.37E-12 | |
| Adjusted R-squared | 0.799529 | S.D. dependent var | 2015.396 | |
| S.E. of regression | 902.3726 | Akaike info criterion | 16.67111 | |
| Sum squared resid | 57040. | Schwarz criterion | 16.85992 | |
| Log likelihood | -121.0333 | F-statistic | 19.61188 | |
| Durbin-Watson stat | 2.360720 | Prob(F-statistic) | 0.000101 | |
三、序列相关的补救
| Dependent Variable: DY | ||||
| Method: Least Squares | ||||
| Date: 12/17/12 Time: 22:07 | ||||
| Sample(adjusted): 1991 2004 | ||||
| Included observations: 14 after adjusting endpoints | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | 2369.885 | 7.9844 | 2.999914 | 0.0111 |
| DX | 0.465880 | 0.029328 | 15.88520 | 0.0000 |
| R-squared | 0.954604 | Mean dependent var | 13875.68 | |
| Adjusted R-squared | 0.950821 | S.D. dependent var | 5320.847 | |
| S.E. of regression | 1179.971 | Akaike info criterion | 17.11593 | |
| Sum squared resid | 16707973 | Schwarz criterion | 17.20722 | |
| Log likelihood | -117.8115 | F-statistic | 252.3397 | |
| Durbin-Watson stat | 0.521473 | Prob(F-statistic) | 0.000000 | |
| Dependent Variable: Y | ||||
| Method: Least Squares | ||||
| Date: 12/17/12 Time: 22:09 | ||||
| Sample(adjusted): 1991 2004 | ||||
| Included observations: 14 after adjusting endpoints | ||||
| Convergence achieved after 16 iterations | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | 55169.41 | 54542.80 | 1.011488 | 0.3335 |
| X | 0.345292 | 0.057754 | 5.978675 | 0.0001 |
| AR(1) | 0.961253 | 0.042004 | 22.88491 | 0.0000 |
| R-squared | 0.998047 | Mean dependent var | 43478.53 | |
| Adjusted R-squared | 0.997691 | S.D. dependent var | 19591.16 | |
| S.E. of regression | 941.3171 | Akaike info criterion | 16.71985 | |
| Sum squared resid | 9746856. | Schwarz criterion | 16.85679 | |
| Log likelihood | -114.03 | F-statistic | 2810.040 | |
| Durbin-Watson stat | 0.941831 | Prob(F-statistic) | 0.000000 | |
| Inverted AR Roots | .96 | |||
