55 80.3352 133.4800 -181.2807 341.9511 56 55.5269 143.5955 -225.9151 336.9690 57 73.8673 153.0439 -226.0932 373.8279 58 75.2471 161.9420 -242.1534 392.6475 59 70.0053 189.8525 -302.0987 442.1094 60 120.4639 214.1559 -299.2739 540.2017 61 184.8801 235.9693 -277.6112 647.3714 62 275.8466 255.9302 -225.7674 777.4606
掠食者预测值为：
Forecasts for variable y
Obs Forecast Std Error 95% Confidence Limits
49 32.7697 14.7279 3.9036 61.6358 50 40.1790 16.3381 8.1570 72.2011 51 42.3346 21.8052 -0.4028 85.0721 52 58.2993 25.9832 7.3732 109.2254 53 78.9707 29.5421 21.0692 136.8722 54 106.5963 32.7090 42.4879 170.7047 55 66.4836 35.5936 -3.2787 136.2458 56 41.9681 38.6392 -33.7634 117.6996 57 46.7548 41.4617 -34.5085 128.0182 58 39.7201 44.1038 -46.7218 126.1619 59 44.9342 46.5964 -46.3930 136.2614 60 45.3286 48.9622 -50.6356 141.2928 61 43.8411 56.4739 -66.8456 154.5279 62 58.1725 63.0975 -65.4964 181.8413
6.4 （1）进出口总额序列均不平稳，但对数变换后的一阶差分后序列平稳。所以对这两个序列取对数后进行单个序列拟合和协整检验。
（2）出口序列拟合的模型为lnxt~ARIMA(1,1,0),具体口径为：
?lnxt=0.14689+1?t
1-0.38845B 进口序列拟合的模型为lnyt~ARIMA(1,1,0),具体口径为：
?lnyt=0.14672+ （3）lnyt和lnxt具有协整关系
1?t
1-0.36364B （4）协整模型为：lnyt=0.99179lnxt+?t-0.69938?t-1 （5）误差修正模型为：?lnyt=0.97861?lnxt-0.22395ECMt-1
时间序列分析基于R - 习题答案
