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时间:2018-12-07
《实验五序列相关模型》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、实验五:序列相关模型一、实验目的掌握序列相关模型的检验方法、处理方法分析。二、实验内容(一)选择模型和参数估计(二)自相关检验1、DW检验;2、偏相关系数检验;3、BG检验。(三)用迭代法处理序列相关问题三、实验步骤利用表5-1资料,试建立我W城乡居民储蓄存款的双对数模型,并检验模型的自相关性。表5-1我国城乡居民储蓄存款与GDP统计资料(1978年=100)年份存款余额YGDP指数X年份存款余额YGDP指数X1978210.60100.019895146.90271.31979281.00107.619907034.20281.71980399.50116.019919107.00307
2、.61981523.70122.1199211545.40351.41982675.40133.1199314762.39398.81983892.50147.6199421518.80449.319841214.70170.0199529662.25496.519851622.60192.9199638520.84544.119862237.60210.0199746279.80592.019873073.30234.0199853407.47638.219883801.50260.7(一)选择模型和参数估计1、图形分析输入命令:SCATXY,参看相关图发现GDP指数与居民储蓄存款二荠的
3、曲线相关关系较为明显。这里选择双对数模型进行参数估计。2、利用LS命令建立双对数方程:GENRLNY=LOG(Y)GENRLNX=LOG(X)LSLNYCLNX输入相应的数据后,得到回归方程:ln/=-8.0753+2.95881nxz=(-31.604)(64.189)^=0.9954;F=4120.223;S.E=0.1221(二)自相关检验1、DW检验因为n=21,k=l,取显著性水平a=0.05时,查表得心=1.22,么=1.42,而0<0.7062=DW4、tatistics,并输入滞后期为10,则会得到残差e,和滞后各期残差的相关系数和偏相关系数,如图5-1。AutocorrelationPartialCorrelationACPACQ-StatProb10.5370.5376.95430.0082-0.087•0.5277.14810.0283•□•3400.02710.2570.0174-0.300-0.15412.8170.0125•0.238•0.21214.529□0136•0.206-0.14915.8940.0147•0.106•0.06816.2810.02380.1120.08016.7480.03390.3440.1655、21.5160.01100.289-0.13125.1800.005图5-1相关系数图10.3180.3182.44280.1182•□•572-0.74910.7540.0053-0.681-0.31623.1970.0004-0.080-0.251233780.00050.450-0.22129.4970.00060.306•0.47532.5030.0007-0.052-0.17432.595□.0008-□.180-0.24433.8000.0009-0046-0.10933.8860.00010□046•0.15433.9780.000图5-2偏相关系数图3、BG检验在方程窗门屮6、点击View/ResidualTest/SeriesCorrelationLMTest,并选择滞后期2,则会得到如图5-3所示的信息。Breusch-GodfreySerialCorrelationLMTest:F-statistic9.931154Probability0.001390Obs*R-squared11.31531Probability0003491VariableCoefficientStd.Errort-StatisticProb.c-0.0195710.188281-0.1039450.9184LNX□0035210.0340550.1034060.9189RESID(7、-1)0.9062200.2050594.41931400004RESID(-2)-□.6016160.211596-2.8432300.0112R-squared0.538824Meandependentvar-1.40E-15AdjustedR-squaredo457440S.D.dependentvar0.119023S.E.ofregressionCL087671Akaikeinfocriterion-1.860
4、tatistics,并输入滞后期为10,则会得到残差e,和滞后各期残差的相关系数和偏相关系数,如图5-1。AutocorrelationPartialCorrelationACPACQ-StatProb10.5370.5376.95430.0082-0.087•0.5277.14810.0283•□•3400.02710.2570.0174-0.300-0.15412.8170.0125•0.238•0.21214.529□0136•0.206-0.14915.8940.0147•0.106•0.06816.2810.02380.1120.08016.7480.03390.3440.165
5、21.5160.01100.289-0.13125.1800.005图5-1相关系数图10.3180.3182.44280.1182•□•572-0.74910.7540.0053-0.681-0.31623.1970.0004-0.080-0.251233780.00050.450-0.22129.4970.00060.306•0.47532.5030.0007-0.052-0.17432.595□.0008-□.180-0.24433.8000.0009-0046-0.10933.8860.00010□046•0.15433.9780.000图5-2偏相关系数图3、BG检验在方程窗门屮
6、点击View/ResidualTest/SeriesCorrelationLMTest,并选择滞后期2,则会得到如图5-3所示的信息。Breusch-GodfreySerialCorrelationLMTest:F-statistic9.931154Probability0.001390Obs*R-squared11.31531Probability0003491VariableCoefficientStd.Errort-StatisticProb.c-0.0195710.188281-0.1039450.9184LNX□0035210.0340550.1034060.9189RESID(
7、-1)0.9062200.2050594.41931400004RESID(-2)-□.6016160.211596-2.8432300.0112R-squared0.538824Meandependentvar-1.40E-15AdjustedR-squaredo457440S.D.dependentvar0.119023S.E.ofregressionCL087671Akaikeinfocriterion-1.860
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