实验十分布滞后模型

1、实验九分布滞后模型【实验目的】掌握分布滞后模型的建立和估计【实验内容】表9.1给出了中国电力行业基本建设投资X与发电量Y的相关资料，拟建立 一多项式分布滞后模型来考察两者的关系。表9.1中国电力行业基本建设投资与发电量年份基本建设投发电量（亿千年份基本建设投发电量（亿千资（亿元）X瓦时）Y资（亿元）X瓦时）Y197530.6519581986161.64495197639.9820311987210.884973197734.7222341988249.735452197850.9125661989267.855848197950.9928201990334.556212198048.1430

2、061991377.756775198140.1430931992489.697539198246.2332771993675.138395198357.46351419941033.429218198476.99377019951124.15100701985107.864107由于无法预知电力行业基本建设投资对发电量影响的时滞期，需取不同的滞后期试算。经过试算发现，在2阶阿尔蒙多项式变换下，滞后期数取到第6期， 估计结果的经济意义比较合理。估计过程如下：输出结果如下：Dependent Variable: Y Method: Least Squares Date: 07/04/00 Tim

3、e: 16:14 Sample (adjusted): 1981 1995Included observations: 15 after adjustmentsVariableCoefficientStd. Error t-StatisticProb.C3319.534243.746313.610010.0000PDU013.0611091.6437691.8622500.0895PDU020.1006950.6932920.1452420.8871PDU03-0.2706400.404731-0.6687100.5175R-squared0.953279Vlean dependent var

4、5702.533Adjusted R-squared0.940537S.D. dependent var2223.354S.E. of regression542.1650 Akaike info criterion15.65220Sum squared resid3233381.Schwarz criterion15.84101Log likelihood-113.3915-statistic74.81340Durbin-Watson stat0.422552mbIF-statistic)0.000000Lag Distribution of XiCoefficientStd. Errort

5、-Statistic100.323201.737240.18604111.777130.831282.13701122.689771.432441.87775i33.061111.643771.862251J42.891161.474461.96002152.179911.982981.0993160.927373.920710.23653Sum of Lags13.84964.365903.17223输出结果的下边部分给出了分布滞后模型的各滞后期的参数。最后得到分布滞后模型估计式为：Y = 3319.5 + 0.323X +1.777X + 2.69X + 3.061X + 2.891X +

6、 2.18X + 0.927 Xttt1t2t3t4t5t6(13.62) (0.19)(2.14)(1.88)(1.86)(1.96)(1.1)(0.24)表5.3给出了中国1978-2000年按当年价测度的GDP与居民消费CONS数 据，检验两者的因果关系。表5.3中国GDP与消费支出单位：亿元年份CONSGDP年份CONSGDP19781759.1003605.60019909113.20018319.5019792005.4004074.000199110315.9021280.4019802317.1004551.300199212459.8025863.7019812604.100

7、4901.400199315682.4034500.7019822867.9005489.200199420809.8046690.7019833182.5006076.300199526944.5058510.5019843674.5007164.400199632152.3068330.4019854589.0008792.100199734854.6074894.2019865175.00010132.80199836921.1079003.3019875961.20011784.70199939334.4082673.1019887633.10014704.00200042911.90

8、89112.5019898523.50016466.00取两阶滞后，过程如下:SEViews日回国File Edi + Ubj ect Vi ew Froc quick Oiti ore jVindow HelpS：dJTiple.GerLerate Seri es.Shu,ili,.Cj-rap hL： consresid Ernjity Group Edit Series)Series StatisticsGrout Stati sti csEstimate Equation.Estimate VAR.| Untitled X New Page / CONSF a th = c: docu

9、irientE :and e e 11 i ngs zhuy i x i my docuiriHntEHB = nijrieWF = untitled输入要检验的变量。输入滞后阶数。输出结果如下：Pairwise Granger Causality TestsDate: 07/04/08 Time: 16:37Sample: 1978 2000Lags: 2Null Hypothesis:ObsF-StatisticProbabilityGDP does not Granger Cause CONS CONS does not Granger Cause GDP214.297491.82325

10、0.03208D.193 即从上面的输出结果可以看出，根据伴随概率值知道，在5%的显著水平下: 拒绝GDP不是CONS的格兰杰检验，即GDP是CONS的格兰杰检验。 接受CONS不是GDP的格兰杰检验。已知1970-1991年美国制造业固定厂房设备投资Y和销售量X的相关数据 如表5.4所示。（1） 假定销售量对厂房设备支出有一个分部滞后效应，使用4期滞后和2次 多项式去估计此分布滞后模型。（2） 检验销售量与厂房设备支出的Granger因果关系，使用直至6期为止的 滞后并评述结果。表5.4单位：10亿美元年份厂房开支Y销售额X年份厂房开支Y销售额X197036.9952.8051981128.

11、68168.129197133.655.9061982123.97163.351197235.4263.0271983117.35172.547197342.3572.9311984139.61190.682197452.4884.791985152.88194.538197553.6686.5891986137.95194.657197668.5398.7971987141.06206.326197767.48113.2011988163.45223.547197878.13126.9051989183.8232.724197995.13143.9361990192.61239.459198

12、0112.6154.3911991182.81235.142估计分布滞后模型，过程如下:100.832420.189974.3818410.317420.097923.241512-0.011740.13431-0.087413-0.155060.09237-1.67865 i4-0.112530.19627-0.57334Lag Distribution of XSum of Lags 0.870520.0327226.6025估计结果如下:Dependent Variable: YMethod: Least SquaresDate: 07/04/08 Time: 19:33Sample (

13、adjusted): 1974 1991Included observations: 18 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.C-30.825548.916420-3.4571660.0038PDU01-0.0117400.134307-0.0874100.9316PDU02-0.2362370.067956-3.4763120.0037PDU030.0929210.0678101.3703170.1922R-squared0.981227Mean dependent var1217878Adjusted

14、 R-squared0.977204S.D. dependent var44.87987S.E. of regression6.776057Akaike info criterion6.857798Sum squared resid642.8093Schwarz criterion7.055658Log likelihood-57.72018F-statistic243.9194Durbin-Watson stat1.358472Prob(F-statistic)0.000000i Coefficient Std. Error t-Statistic对应的分布滞后模型的表达式为:Y =30.8

15、3 + 0.83X + 0.32X 1 -0.01X 2-0.16X 3-0.11X 4做格兰杰检验，以一阶滞后为例，过程如下:结果如下:Pairwise Granger Causality TestsDate: 07/04/08 Time: 19:44Sample: 1970 1991Lags: 1Null Hypothesis:ObsF-StatisticProbabilityX does not Granger Cause Y2131.90612.3E-05Y does not Granger Cause X23.83390.00012从上面F检验的伴随概率值可以知道，X与Y互为因果关系

16、。按上述过程分别做从1直到6期滞后的Granger因果关系检验，结果分别如下:2阶:Pairwise Granger Causality TestsDate: 07/04/08 Time: 19:48Sample: 1970 1991Lags: 2Null Hypothesis:ObsF-StatisticProbabilityY does not Granger Cause X X does not Granger Cause Y2013.165318.46840.000509.CIE-D53阶:Paiiwise Granger Causality TestsDate: 07/04/08 T

17、ime: 19:49Sample: 1970 1991Lags: 3Null Hypothesis:ObsF-StatisticProbabilityY does not Granger Cause X197.190290.00509X does not Granger Cause Y6.161960.008874阶:Pairwise Granger Causality TestsDate: 07/04/08 Time: 19:50Sample: 1970 1991Lags: 4Null Hypothesis:ObsF-StatisticProbabilityY does not Granger Cause X X does not Granger Cause Y184.446783.717610.02946D.D4T195阶:Pairwise Granger Causality TestsDate: 07/04/08 Time: 19:51Sample: 1970 1991Lags: 5Null Hypothesis:ObsF-StatisticProbabilityY does not Granger Cause X X does not Granger Cause Y172.772972.280540.123270.171246阶:Paiiwise Gra