计量经济学第八章答案(第二版_庞皓_科学)

1第八章答案8.1Sen和Srivastava（1971）在研究贫富国之间期望寿命的差异时，利用101个国家的数据，建立了如下的回归模型：2.409.39ln3.36((ln7))iiiiYXDX(4.37)(0.857)(2.42)R2=0.752其中：X是以美元计的人均收入；Y是以年计的期望寿命；Sen和Srivastava认为人均收入的临界值为1097美元（ln10977），若人均收入超过1097美元，则被认定为富国；若人均收入低于1097美元，被认定为贫穷国。括号内的数值为对应参数估计值的t-值。1）解释这些计算结果。2）回归方程中引入ln7iiDX的原因是什么？如何解释这个回归解释变量？3）如何对贫穷国进行回归？又如何对富国进行回归？4）从这个回归结果中可得到的一般结论是什么？练习题8.1参考解答：1.结果解释依据给定的估计检验结果数据，对数人均收入对期望寿命在统计上并没有显著影响，截距和变量ln7iiDX在统计上对期望寿命有显著影响；同时，2.403.3679.393.36ln((ln7))12.409.39ln0iiiiiiiXDXDYXD富国时穷国时表明贫富国之间的期望寿命存在差异。2.回归方程中引入ln7iiDX的原因是从截距和斜率两个方面考证收入因素对期望寿命的影响。这个回归解释变量可解释为对期望寿命的影响存在截距差异和斜率差异的共同因素。3.对穷国进行回归时，回归模型为12ln1097iiiiiiYXYX，其中，为美元时的寿命；对富国进行回归时，回归模型为12ln1097iiiiiiYXYX，其中，为美元时的寿命；4.一般的结论为富国的期望寿命药高于穷国的期望寿命，并且随着收入的增加，在平均意2义上，富国的期望寿命的增加变化趋势优于穷国，贫富国之间的期望寿命的确存在显著差异。8.2个人所得税起征点调整对居民消费支出会产生重要的影响。为研究个人所得税起征点调整对城镇居民个人消费支出行为的效应，收集相关的数据如表8.4和表8.5所示。表8.4个人所得税起征点调整情况1987年1994年2006年2008年最低的起征点400元800元1600元2000元表8.5城镇居民收入与消费的有关数据城镇家庭平均每人可支配收入（元）城镇家庭平均每人全年消费性支出（元）平均每户城镇家庭就业人口数（人）城镇家庭平均每一就业者负担人数（含本人）（人）1985739.1673.22.151.811986900.97992.121.819871002.1884.42.091.7919881180.211042.031.7919891373.9121121.7819901510.161278.891.981.7719911700.61453.81.961.7519922026.61671.71.951.7319932577.42110.81.921.7219943496.22851.31.881.7419954282.953537.571.871.7319964838.93919.51.861.7219975160.34185.61.831.7419985425.14331.61.81.75199958544615.91.771.7720006279.9849981.681.8620016859.65309.011.651.8820027702.86029.921.581.9220038472.26510.941.581.9120049421.67182.11.561.912005104937942.881.511.96200611759.458696.551.531.93200713785.819997.471.541.89200815780.811242.91.481.97若模型设定为：Consumet=Ct+α1Incomet+α2Consumet-1+α3Employmentt+α4Burdent+α5d1t+α6d2t+α7d3t+α8d4t+εt3其中Consumet表示t期城镇居民家庭人均消费支出，Incomet表示t期城镇居民家庭人均可支配收入,Employmentt表示t期城镇居民家庭平均每户就业人口,Burdent表示t期城镇居民家庭平均每一就业者负担人数，dit(i=1,2,3,4)相应的虚拟变量。1）构造用于描述个人所得税调整的虚拟变量，并简要说明其理由；2）用散点图描述两两变量之间的关系，并给出你对模型设定的结论；3）依据测算，选择你认为更能描述客观实际的模型，并简要说明其理由；4）根据分析结果，你对提高个人所得税起征点影响居民消费的有效性能得出什么结论?练习题8.2参考解答：0200040006000800010000120000400080001200016000INCOMECONSUME0200040006000800010000120001.701.751.801.851.901.952.00BURDENCONSUME0200040006000800010000120001.41.51.61.71.81.92.02.12.2EMPLOYMENTCONSUME录入如下数据obsCONSUMEINCOMEEMPLOYMENTD1D2D3D41985673.2000739.10002.1500000.0000000.0000000.0000000.0000001986799.0000900.90002.1200000.0000000.0000000.0000000.0000001987884.40001002.1002.0900001.0000000.0000000.0000000.00000019881104.0001180.2002.0300001.0000000.0000000.0000000.00000019891211.0001373.9002.0000001.0000000.0000000.0000000.00000019901278.8901510.1601.9800001.0000000.0000000.0000000.00000019911453.8001700.6001.9600001.0000000.0000000.0000000.0000004分别作如下回归：DependentVariable:CONSUMEMethod:LeastSquaresDate:08/24/09Time:13:14Sample(adjusted):19862008Includedobservations:23afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C744.7966378.06621.9700170.0676CONSUME(-1)0.0848730.0509071.6672210.1162INCOME0.6331180.03519817.987290.0000LOG(EMPLOYMENT)-762.9720478.5280-1.5944140.1317D137.4346050.234450.7451980.4677D2221.076538.308405.7709660.0000D3-122.049373.81439-1.6534610.1190D4-178.868865.87071-2.7154520.0160R-squared0.999861Meandependentvar4428.906AdjustedR-squared0.999796S.D.dependentvar3060.917S.E.ofregression43.70477Akaikeinfocriterion10.66100Sumsquaredresid28651.61Schwarzcriterion11.05595Loglikelihood-114.6015F-statistic15413.79Durbin-Watsonstat2.977604Prob(F-statistic)0.000000DependentVariable:CONSUMEMethod:LeastSquaresDate:08/24/09Time:13:14Sample(adjusted):19862008Includedobservations:23afteradjustments19921671.7002026.6001.9500001.0000000.0000000.0000000.00000019932110.8002577.4001.9200001.0000000.0000000.0000000.00000019942851.3003496.2001.8800001.0000001.0000000.0000000.00000019953537.5704282.9501.8700001.0000001.0000000.0000000.00000019963919.5004838.9001.8600001.0000001.0000000.0000000.00000019974185.6005160.3001.8300001.0000001.0000000.0000000.00000019984331.6005425.1001.8000001.0000001.0000000.0000000.00000019994615.9005854.0001.7700001.0000001.0000000.0000000.00000020004998.0006279.9801.6800001.0000001.0000000.0000000.00000020015309.0106859.6001.6500001.0000001.0000000.0000000.00000020026029.9207702.8001.5800001.0000001.0000000.0000000.00000020036510.9408472.2001.5800001.0000001.0000000.0000000.00000020047182.1009421.6001.5600001.0000001.0000000.0000000.00000020057942.88010493.001.5100001.0000001.0000000.0000000.00000020068696.55011759.451.5300001.0000001.0000001.0000000.00000020079997.47013785.811.5400001.0000001.0000001.0000000.000000200811242.9015780.801.4800001.0000001.0000001.0000001.0000005VariableCoefficientStd.Errort-StatisticProb.C871.9310332.66272.6210670.0185CONSUME(-1)0.0835760.0501651.6660170.1152INCOME0.6299220.03444718.286760.0000LOG(EMPLOYMENT)-889.4616441.1508-2.0162300.0609D2226.036137.197916.0765790.0000D3-110.888471.26752-1.5559460.1393D4-171.692464.25105-2.6722110.0167R-squared0.999856Meandependentvar4428.906AdjustedR-squared0.999802S.D.dependentvar3060.917S.E.ofregression43.09316Akaikeinfocriterion10.61040Sumsquaredresid29712.33