中级计量经济学-第四章-习题以及解答思路(EViews)(共10页).docx

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1、精选优质文档-倾情为你奉上第4章 习题一表1给出了19651970年美国制造业利润和销售额的季度数据。假定利润不仅与销售额有关，而且和季度因素有关。要求对下列二种情况分别估计利润模型：（1）如果认为季度影响使利润平均值发生变异，应如何引入虚拟变量？（2）如果认为季度影响使利润对销售额的变化率发生变异，如何引入虚拟变量？表1利润（Y）销售额（X）利润（Y）销售额（X）1965-I105031968-I12539II12092II14849III10834III13203IV12201IV149471966-I122451969-I14151II14001II15949III12213III140

2、24IV12820IV143151967-I113491970-I12381II12615II13991III11014III12174IV12730IV10985Quarterly 65-70Quick-Equation EstimationY c x seas(1) seas(2) seas(3)Dependent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 18:38Sample: 1965Q1 1970Q4Included observations: 24VariableCoefficientStd. Errort-Stat

3、isticProb.C6868.0151892.7663.0.0018X0.0.3.0.0035SEAS(1)-182.1690654.3568-0.0.7837SEAS(2)1140.294630.68061.0.0865SEAS(3)-400.3371636.1128-0.0.5366R-squared0.Mean dependent var12838.54Adjusted R-squared0.S.D. dependent var1433.284S.E. of regression1086.160Akaike info criterion17.00174Sum squared resid

4、Schwarz criterion17.24716Log likelihood-199.0208F-statistic5.Durbin-Watson stat0.Prob(F-statistic)0.T和P在5%情况下都不通过，第二季度相对还好一点假设第二季度显著，结果的经济含义是什么？Y c x seas(2) seas(3) seas(4)Dependent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 18:47Sample: 1965Q1 1970Q4Included observations: 24VariableCoeff

5、icientStd. Errort-StatisticProb.C6685.8461711.6183.0.0009X0.0.3.0.0035SEAS(2)1322.463638.42582.0.0522SEAS(3)-218.1681632.1991-0.0.7338SEAS(4)182.1690654.35680.0.7837R-squared0.Mean dependent var12838.54Adjusted R-squared0.S.D. dependent var1433.284S.E. of regression1086.160Akaike info criterion17.00

6、174Sum squared residSchwarz criterion17.24716Log likelihood-199.0208F-statistic5.Durbin-Watson stat0.Prob(F-statistic)0.第二季度依旧显著影响四种都试一下（去掉一个季节），选一个最显著的124Dependent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 18:51Sample: 1965Q1 1970Q4Included observations: 24VariableCoefficientStd. Errort-

7、StatisticProb.C6467.6781789.1783.0.0018X0.0.3.0.0035SEAS(1)218.1681632.19910.0.7338SEAS(2)1540.632628.34192.0.0241SEAS(4)400.3371636.11280.0.5366R-squared0.Mean dependent var12838.54Adjusted R-squared0.S.D. dependent var1433.284S.E. of regression1086.160Akaike info criterion17.00174Sum squared resid

8、Schwarz criterion17.24716Log likelihood-199.0208F-statistic5.Durbin-Watson stat0.Prob(F-statistic)0.134Dependent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 18:52Sample: 1965Q1 1970Q4Included observations: 24VariableCoefficientStd. Errort-StatisticProb.C8008.3091827.5434.0.0003X0.0.3.0.0035

9、SEAS(1)-1322.463638.4258-2.0.0522SEAS(3)-1540.632628.3419-2.0.0241SEAS(4)-1140.294630.6806-1.0.0865R-squared0.Mean dependent var12838.54Adjusted R-squared0.S.D. dependent var1433.284S.E. of regression1086.160Akaike info criterion17.00174Sum squared residSchwarz criterion17.24716Log likelihood-199.02

10、08F-statistic5.Durbin-Watson stat0.Prob(F-statistic)0.（2）Y=c+x+1D1X+2D2X+3D3XD1=1（第一季度）0（其他）Y c x seas(1)*x seas(2)*x seas(3)*xDependent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 19:00Sample: 1965Q1 1970Q4Included observations: 24VariableCoefficientStd. Errort-StatisticProb.C6965.8521753.

11、6423.0.0008X0.0.3.0.0033SEAS(1)*X-0.0.-0.0.8362SEAS(2)*X0.0.1.0.0665SEAS(3)*X-0.0.-0.0.5774R-squared0.Mean dependent var12838.54Adjusted R-squared0.S.D. dependent var1433.284S.E. of regression1082.323Akaike info criterion16.99466Sum squared residSchwarz criterion17.24009Log likelihood-198.9359F-stat

12、istic5.Durbin-Watson stat0.Prob(F-statistic)0.Dependent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 19:10Sample: 1965Q1 1970Q4Included observations: 24VariableCoefficientStd. Errort-StatisticProb.C8008.3091827.5434.0.0003X0.0.3.0.0035SEAS(1)-1322.463638.4258-2.0.0522SEAS(3)-1540.632628.3419

13、-2.0.0241SEAS(4)-1140.294630.6806-1.0.0865R-squared0.Mean dependent var12838.54Adjusted R-squared0.S.D. dependent var1433.284S.E. of regression1086.160Akaike info criterion17.00174Sum squared residSchwarz criterion17.24716Log likelihood-199.0208F-statistic5.Durbin-Watson stat0.Prob(F-statistic)0.Dep

14、endent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 19:11Sample: 1965Q1 1970Q4Included observations: 24VariableCoefficientStd. Errort-StatisticProb.C6965.8521753.6423.0.0008X0.0.2.0.0078SEAS(1)*X0.0.0.0.7452SEAS(2)*X0.0.2.0.0237SEAS(4)*X0.0.0.0.5774R-squared0.Mean dependent var12838.54Adjust

15、ed R-squared0.S.D. dependent var1433.284S.E. of regression1082.323Akaike info criterion16.99466Sum squared residSchwarz criterion17.24009Log likelihood-198.9359F-statistic5.Durbin-Watson stat0.Prob(F-statistic)0.Dependent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 19:11Sample: 1965Q1 1970Q

16、4Included observations: 24VariableCoefficientStd. Errort-StatisticProb.C6965.8521753.6423.0.0008X0.0.2.0.0082SEAS(2)*X0.0.2.0.0565SEAS(3)*X-0.0.-0.0.7452SEAS(4)*X0.0.0.0.8362R-squared0.Mean dependent var12838.54Adjusted R-squared0.S.D. dependent var1433.284S.E. of regression1082.323Akaike info crite

17、rion16.99466Sum squared residSchwarz criterion17.24009Log likelihood-198.9359F-statistic5.Durbin-Watson stat0.Prob(F-statistic)0.习题二表2给出了某地区某行业的库存和销售的统计资料。假设库存额依赖于本年销售额与前三年的销售额，试用Almon变换估计以下有限分布滞后模型：表2库存Y（万元）销售额X（万元）库存Y（万元）销售额X（万元）198011267 8827 199017053 13668 198112661 9247 199119491 14956 1982129

18、68 9579 199221164 15483 198312518 9093 199322719 16761 198413177 10073 199424269 17852 198513454 10265 199525411 17620 198613735 10299 199625611 18639 198714553 11038 199726930 20672 198815011 11677 199830218 23799 198915846 12445 199936784 27359 Y=+0Xt-i+1Xt-i+2Xt-i+t3，i=0笔记11,26）在最上面输入genr z0=x+x(

19、-1)+x(-1)+x(-3)genr z1=x(-1)+2*x(-2)+3*x(-3)genr z2=x(-1)+4*x(-2)+9*x(-3)y c z0 z1 z2Dependent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 19:38Sample (adjusted): 1983 1999Included observations: 17 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.C-1928.495503.5272-3.0.0021Z00.

20、0.3.0.0024Z10.0.2.0.0372Z2-0.0.-2.0.0206R-squared0.Mean dependent var20467.29Adjusted R-squared0.S.D. dependent var6997.995S.E. of regression455.0907Akaike info criterion15.28119Sum squared resid.Schwarz criterion15.47724Log likelihood-125.8902F-statistic1256.768Durbin-Watson stat1.Prob(F-statistic)

21、0.Y c PDL(x,3,2)重新回归Dependent Variable: YMethod: Least SquaresDate: 11/26/14 Time: 19:46Sample (adjusted): 1983 1999Included observations: 17 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.C-1784.821498.4654-3.0.0034PDL010.0.6.0.0000PDL020.0.0.0.8215PDL03-0.0.-2.0.0223R-squared0.Mean

22、dependent var20467.29Adjusted R-squared0.S.D. dependent var6997.995S.E. of regression439.5669Akaike info criterion15.21178Sum squared resid.Schwarz criterion15.40783Log likelihood-125.3001F-statistic1347.416Durbin-Watson stat1.Prob(F-statistic)0.Lag Distribution of XiCoefficientStd. Errort-Statistic

23、. * |00.496210.124163.99656. *|10.848260.135056.28108. * |20.552340.123264.48121* . |3-0.391530.17618-2.22234Sum of Lags1.505280.0475031.6921习题三表3给出了印度19491965年实际货币存量、实际总国民收入和长期利率数据。假设有如下的长期货币需求关系式：其中，为长期货币需求（现金余额）；为长期利率；为实际总国民收入。请在如下存量调整假说下估计该货币需求模型，其中为实际现金存量： 表3年份实际货币M实际净收入Y长期利率R年份实际货币M实际净收入Y长期利率R

24、(千万卢比)(10亿卢比)(%)(千万卢比)(10亿卢比)(%)19491898.69 86.50 3.03 19582307.26 108.90 4.18 19501840.71 88.20 3.07 19592335.66 117.34 4.13 19511838.31 88.50 3.15 19602491.27 118.60 4.05 19521646.39 91.00 3.41 19612579.92 127.17 4.06 19531699.94 94.60 3.66 19622687.80 130.60 4.16 19541716.88 100.30 3.64 19632860.

25、83 133.10 4.49 19552054.59 102.80 3.70 19643045.80 139.70 4.66 19562328.00 104.80 3.74 19653068.64 150.50 4.80 19572277.66 110.00 3.99 LnM*t=ln0+1lnRt+2lnYt+tLnMt-LnMt-1= lnM*t- lnMt-1LnMt= ln0+1 lnRt+2 lnYt+（1- ）lnMt-1+ t求回归Quick-Equation Estimationlog(m) c log(r) log(y) log(m(-)Dependent Variable:

26、 LOG(M)Method: Least SquaresDate: 11/26/14 Time: 20:13Sample (adjusted): 1950 1965Included observations: 16 after adjustmentsVariableCoefficientStd. Errort-StatisticProb.C0.0.0.0.5649LOG(R)-0.0.-0.0.7841LOG(Y)0.0.1.0.1010LOG(M(-1)0.0.2.0.0219R-squared0.Mean dependent var7.Adjusted R-squared0.S.D. de

27、pendent var0.S.E. of regression0.Akaike info criterion-2.Sum squared resid0.Schwarz criterion-2.Log likelihood25.17967F-statistic60.44199Durbin-Watson stat1.Prob(F-statistic)0.求1-=0.非线性模型如何变换，变换后还要回到估计的预原模型中拉格朗日乘数检验VIEW=R=LM testLags to 1Breusch-Godfrey Serial Correlation LM Test:F-statistic0.Probab

28、ility0.Obs*R-squared0.Probability0.Test Equation:Dependent Variable: RESIDMethod: Least SquaresDate: 11/26/14 Time: 20:22Presample missing value lagged residuals set to zero.VariableCoefficientStd. Errort-StatisticProb.C0.1.0.0.9569LOG(R)-0.0.-0.0.9986LOG(Y)0.0.0.0.9659LOG(M(-1)-0.0.-0.0.9432RESID(-1)0.0.0.0.9084R-squared0.Mean dependent var5.55E-16Adjusted R-squared-0.S.D. dependent var0.S.E. of regression0.Akaike info criterion-2.Sum squared resid0.Schwarz criterion-2.Log likelihood25.18975F-statistic0.Durbin-Watson stat1.Prob(F-statistic)0.专心-专注-专业