Sum squared resid 1557.457 Schwarz criterion Log likelihood -39.43051 F-statistic Durbin-Watson stat 3.579994 Prob(F-statistic)
①填上（1）、（2）、（3）、（4）位置所缺数据； ②以标准记法写出回归方程； ③你对分析结果满意吗？为什么？ 注意：coefficient= std.error * t-statistic
Adjusted r-squared= 1-(1-r-squared)(n-1)/(n-k-1)
8.807135
11.58741 0.006579
7. 根据下列Eviews应用软件的运行结果比较分析选择哪个模型较好？并说明理由；以标准形式写出确定的回归方程。 模型一
Dependent Variable: Y Method: Least Squares Sample: 1 12 Included observations: 12 Variable Coefficient Std. Error t-Statistic Prob. C 46.13828 7.356990 6.271352 0.0001 1/X 1335.604 171.2199 7.800522 0.0000 Adjusted R-squared 0.844738 Akaike info criterion 8.283763 Sum squared resid 1993.125 Schwarz criterion 8.364580 Log likelihood -47.70258 F-statistic 60.84814 Durbin-Watson stat 2.154969 Prob(F-statistic) 0.000015
模型二
Dependent Variable: Y Method: Least Squares Sample: 1 12 Included observations: 12 Convergence achieved after 6 iterations Y=C(1)*C(2)^X Coefficient Std. Error t-Statistic Prob. C(1) 195.1784 11.46600 17.02237 0.0000 C(2) 0.979132 0.001888 518.5842 0.0000 Adjusted R-squared 0.922179 Akaike info criterion 7.593063 Sum squared resid 999.0044 Schwarz criterion 7.673881 Log likelihood -43.55838 Durbin-Watson stat 2.818195
8. 下图一是yt的差分变量Dyt的相关图和偏相关图；图二是以Dyt为变量建立的时间序列模型的输出结果。（20分）
