第九章---案例分析(分布滞后模型)

第九章案例分析【案例7.1】为了研究1955—1974年期间美国制造业库存量Y和销售额X的关系，用阿尔蒙法估计如下有限分布滞后模型：ttttttuXXXXY3322110将系数i(i=0,1,2,3)用二次多项式近似，即002101210242210393则原模型可变为tttttuZZZY221100其中3212321132109432tttttttttttttXXXZXXXZXXXXZ在Eviews工作文件中输入X和Y的数据，在工作文件窗口中点击“Genr”工具栏，出现对话框，输入生成变量Z0t的公式，点击“OK”；类似，可生成Z1t、Z2t变量的数据。进入EquationSpecification对话栏，键入回归方程形式YCZ0Z1Z2点击“OK”，显示回归结果（见表7.2）。表7.2表中Z0、Z1、Z2对应的系数分别为210、、的估计值210ˆˆˆ、、。将它们代入分布滞后系数的阿尔蒙多项式中，可计算出3210ˆˆˆˆ、、、的估计值为：-0.522)432155.0(9902049.03661248.0ˆ9ˆ3ˆˆ0.736725)432155.0(4902049.02661248.0ˆ4ˆ2ˆˆ1.131142)432155.0(902049.0661248.0ˆˆˆˆ661248.0ˆˆ21012101210100从而，分布滞后模型的最终估计式为：32155495.076178.015686.1630281.0419601.6tttttXXXXY在实际应用中，Eviews提供了多项式分布滞后指令“PDL”用于估计分布滞后模型。下面结合本例给出操作过程：在Eviews中输入X和Y的数据，进入EquationSpecification对话栏，键入方程形式YCPDL(X,3,2)其中，“PDL指令”表示进行多项式分布滞后（PolynomialDistributedLags）模型的估计，括号中的3表示X的分布滞后长度，2表示多项式的阶数。在EstimationSettings栏中选择LeastSquares(最小二乘法)，点击OK，屏幕将显示回归分析结果（见表7.3）。表7.3需要指出的是，用“PDL”估计分布滞后模型时，Eviews所采用的滞后系数多项式变换不是形如（7.4）式的阿尔蒙多项式，而是阿尔蒙多项式的派生形式。因此，输出结果中PDL01、PDL02、PDL03对应的估计系数不是阿尔蒙多项式系数210、、的估计。但同前面分步计算的结果相比，最终的分布滞后估计系数式3210ˆˆˆˆ、、、是相同的。【案例7.2】货币主义学派认为，产生通货膨胀的必要条件是货币的超量供应。物价变动与货币供应量的变化有着较为密切的联系，但是二者之间的关系不是瞬时的，货币供应量的变化对物价的影响存在一定时滞。有研究表明，西方国家的通货膨胀时滞大约为2—3个季度。在中国，大家普遍认同货币供给的变化对物价具有滞后影响，但滞后期究竟有多长，还存在不同的认识。下面采集1996－2005年全国广义货币供应量和物价指数的月度数据（见表7.4）对这一问题进行研究。表7.41996－2005年全国广义货币供应量及物价指数月度数据月度广义货币M2(千亿元）广义货币增长量M2z（千亿元）居民消费价格同比指数tbzs月度广义货币M2(千亿元）广义货币增长量M2z（千亿元）居民消费价格同比指数tbzsJan-9658.401Oct-00129.522-0.9518100Feb-9663.7785.377109.3Nov-00130.99411.4721101.3Mar-9664.5110.733109.8Dec-00134.61033.6162101.5Apr-9665.7231.212109.7Jan-01137.54362.9333101.2May-9666.881.157108.9Feb-01136.2102-1.3334100Jun-9668.1321.252108.6Mar-01138.74452.5343100.8Jul-9669.3461.214108.3Apr-01139.94991.2054101.6Aug-9672.3092.963108.1May-01139.0158-0.9341101.7Sep-9669.643-2.666107.4Jun-01147.80978.7939101.4Oct-9673.15223.5092107Jul-01149.22871.419101.5Nov-9674.1420.9898106.9Aug-01149.94180.7131101Dec-9676.09491.9529107Sep-01151.82261.880899.9Jan-9778.6482.5531105.9Oct-01151.4973-0.3253100.2Feb-9778.9980.35105.6Nov-01154.08832.59199.7Mar-9779.8890.891104Dec-01158.30194.213699.7Apr-9780.8180.929103.2Jan-02159.63931.337499May-9781.1510.333102.8Feb-02160.93561.2963100Jun-9782.7891.638102.8Mar-02164.06463.12999.2Jul-9783.460.671102.7Apr-02164.57060.50698.7Aug-9784.7461.286101.9May-02166.0611.490498.9Sep-9785.8921.146101.8Jun-02169.60123.540299.2Oct-9786.6440.752101.5Jul-02170.85111.249999.1Nov-9787.590.946101.1Aug-02173.25092.399899.3Dec-9790.99533.4053100.4Sep-02176.98243.731599.3Jan-9892.21141.2161100.3Oct-02177.29420.311899.2Feb-9892.024-0.187499.9Nov-02179.73632.442199.3Mar-9892.015-0.009100.7Dec-02185.00735.27199.6Apr-9892.6620.64799.7Jan-03190.48835.481100.4May-9893.9361.27499Feb-03190.1084-0.3799100.2Jun-9894.6580.72298.7Mar-03194.48734.3789100.9Jul-9896.3141.65698.6Apr-03196.13011.6428101Aug-9897.2990.98598.6May-03199.50523.3751100.7Sep-9899.7952.49698.5Jun-03204.93145.4262100.3Oct-98100.87521.080298.9Jul-03206.19311.2617100.5Nov-98102.2291.353898.8Aug-03210.59194.3988100.9Dec-98104.49852.269599Sep-03213.56712.9752101.1Jan-99105.51.001598.8Oct-03214.46940.9023101.8Feb-99107.7782.27898.7Nov-03216.35171.8823103Mar-99108.4380.6698.2Dec-03221.22284.8711103.2Apr-99109.2180.7897.8Jan-04225.101933.87913103.2May-99110.0610.84397.8Feb-04227.050721.94879102.1Jun-99111.3631.30297.9Mar-04231.65464.60388103Jul-99111.4140.05198.6Apr-04233.627861.97326103.8Aug-99112.8271.41398.7May-04234.84241.21454104.4Sep-99115.0792.25299.2Jun-04238.427493.58509105Oct-99115.390.31199.4Jul-04234.8424-3.58509105.3Nov-99116.5591.16999.1Aug-04239.729194.88679105.3Dec-99119.8983.33999Sep-04243.7574.02781105.2Jan-00121.221.32299.8Oct-04243.74-0.017104.3Feb-00121.58340.3634100.7Nov-04247.135583.39558102.8Mar-00122.58070.997399.8Dec-04253.20776.07212102.4Apr-00124.12191.541299.7Jan-05257.752834.54513101.9May-00124.0533-0.0686100.1Feb-05259.35611.60327103.9Jun-00126.60532.552100.5Mar-05264.58895.2328102.7Jul-00126.3239-0.2814100.5Apr-05266.992662.40376101.8Aug-00127.791.4661100.3May-05269.22942.23674101.8Sep-00130.47382.6838100数据来源：中国经济统计数据库，。为了考察货币供应量的变化对物价的影响，我们用广义货币M2的月增长量M2Z作为解释变量，以居民消费价格月度同比指数TBZS为被解释变量进行研究。首先估计如下回归模型tttuZMTBZS20得如下回归结果（表7.5）。表7.5DependentVariable:TBZSMethod:LeastSquaresDate:07/03/05Time:17:10Sample(adjusted):1996:022005:05Includedobservations:112afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C101.43560.397419255.23580.0000M2Z0.0683710.1518720.4501900.6535R-squared0.001839Meandependentvar101.5643AdjustedR-squared-0.007235S.D.dependentvar2.911111S.E.ofregression2.921623Akaikeinfocriterion4.999852Sumsquaredresid938.9472Schwarzcriterion5.048396Loglikelihood-277.9917F-statistic0.202671Durbin-Watsonstat0.047702Prob(F-statistic)0.653460从回归结果来看，M2Z的t统计量值不显著，表明当期货币供应量的变化对当期物价水平的影响在统计意义上不明显。为了分析货币供应量变化影响物价的滞后性，我们做滞后6个月的分布滞后模型的估计，在Eviews工作文档的方程设定窗口中，输入TBZSCM2ZM2Z(-1)M2Z(-2