计量经济学数据

1、例 1.3序列 T 和 H 分别表示某地区1997 年 1 月至 2000 年 12 月的气温和绝对湿度的月平均值序列，数据见表1.2。要求绘制序列H 的经验累计分布函数图和它与序列T 的 QQ图。表 1.2（ 0102）某地区气温和绝对湿度月平均值月1997199819992000HTHTHTHT15.425.42.86.73.65.3226.244.71.86.22.67.1639.18.78.58.88.36.87.16413.313.611.813.713.714.211.813.1519.119.317.918.71819.618.118.7625.324.322.322.621.2

2、22.422.522.77312829.126.730.126.73027.1831.924.929.427.5312830.628.1922.72423.623.726.724.92724.61015.118.415.117.414.91817.218.51112.112.512.313.39.210.310.811.8124.4110.69.564.986.9例 2.1 表 2.1 是1950 1987 年间美国机动车汽油消费量和影响消费量的变量数值。其中各变量表示：qmg机动车汽油消费量（单位：千加仑）； car汽车保有量；pmg机动汽油零售价格；pop人口数；rgnp按1982 年美圆

3、计算的gnp（单位：十亿美圆）； pgnp gnp 指数（以1982 年为100） 。以汽油量为因变量，其他变量为自变量，建立一个回归模型。表2.1（ 0201 ）1950 1987 年间美国机动车汽油消费量数据年份QMGCARPMGPOPRGNPPGNP195040617285491952120.2721522711090.426.143896887519487960.2761548781179.227.946428148533013290.2871575531226.128.349374047563132810.291601841282.128.551107135586225470.291

4、1630261252.12954333255626887920.2991659311356.729.356022406651538100.311689031383.530.357415622671249040.3041719841410.231.459154330682965940.3051748821384.732.161596548713544200.311177830148132.6196062811854738686820.3081806711517.233.263978489759582150.3061836911547.933.662531373791733290.30418653

5、81647.93464779104827137170.3041892421711.634.567663848863012070.3121918891806.93570337126903607210.3211943031918.535.773638812939620300.3321965602048.936.676139326969309490.3371987122100.337.8807726571010391130.3482007062195.439.4854160841035620180.3572026772260.741.21970886840501068076290.364205052

6、2250.743.4921946201112974590.361207661233245.6953489041170516380.3882098962465.547.5998046001238117410.5242119092602.850.21002122101279512540.5722138542564.255.11023277501309189180.5952159732530.960.41069727401363339340.6312180352680.563.51100234101415231970.6572202392822.467.31136259601464843360.67

7、82225853115.272.21078312201494222050.8572250553192.478.619801008560701533578761.1912277573187.185.71009940401559074731.3112301383248.8941002428701569936941.22223252031661001015152601610179261.1572347993279.1103.91026036901634329441.1292370013489.9107.91047192301687438171.1152392793585.2111.519861078

8、312201732558500.8572416133676.5114.519871104679801779220000.8972439153847117.719881789220000.8772459153947119.719891799220000.8572479154047121.719901809220000.8372499154147123.7ls car c pmg pop rgnp pgnpls qmg c car pmg pop rgnp pgnpls car c pmg pop rgnp pgnpscalar vifcar=1/(1-eqcar.r2)eq01.testdrop

9、 carLs qmg-qmg(-1) car-car(-1) pmg-pmg(-1) pop-pop(-1) rgnp-rgnp(-1)pgnp-pgnp(-1)Ls qmg-qmg(-1) car-car(-1)Ls qmg c qmg(-1) car car(-1) pmg pmg(-1) pop pop(-1) rgnp rgnp(-1)pgnp pgnp(-1)Ls qmg c qmg(-1) carpmg pmg(-1) pop pop(-1) rgnp rgnp(-1)pgnpLs qmg c qmg(-1) carpmg pmg(-1) pop pop(-1)rgnp(-1) p

10、gnpLs qmg c qmg(-1) carpmg pmg(-1) pop pop(-1)rgnp(-1)Eq01.testdrop pgnpLs qmg c qmg(-1) carpmg pmg(-1) pop pop(-1)rgnp(-1)pgnp(-2)Ls cScalar beta0=eq04.Dependent Variable: QMGMethod: Least SquaresDate: 10/16/12 Time: 19:02Sample: 1950 1987Included observations: 38VariableCoefficientStd. Errort-Stat

11、isticProb.C68497350134161555.1055870.0000CAR1.5876770.13774211.526460.0000PMG-103754103346338.-3.1005260.0040POP-462.2931108.0825-4.2772240.0002RGNP-12666.475248.346-2.4134210.0217PGNP-579453.059259.84-9.7781730.0000R-squared0.991878Mean dependent var80901846Adjusted R-squared0.990608S.D. dependent

12、var22972717S.E. of regression2226295. Akaike info criterion32.21351Sum squared resid1.59E+14Schwarz criterion32.47208Log likelihood-606.0568Hannan-Quinn criter.32.30551F-statistic781.5361Durbin-Watson stat0.869418Prob(F-statistic)0.000000例 2.2为研究采取某项保险革新措施的速度y与保险公司的规模x1 和保险公司类型的关系，选取下列数据：y一个公司提出该项革新

13、直至革新被采纳间隔的月数，x1 公司的资产总额（单位：百万元）， x2定性变量，表示公司类型：其中1 表示股份制公司，0 表示互助公司。数据资料见表2.5。表 2.5 （ 0205）保险公司革新数据yX1X2yX1X2171510281641269201527212117501129513031038681221040318510277021224112210020166119120013305142900301241162380142461要建立的模型：yi01 xi 12 xi 2i得到模型为y=33.87407-0.101742*x1+8.055469*x2差分回归方程：yt0.65*

14、xt即ytyt10.65xt0.65xt1即ytyt10.65xt0.65xt1消除自相关的模型：qmg=75541509.38+1.4390*car-10354749*pmg-503.50*pop-5290.80*rgnp-565089.4*pgnp某市楼盘销售价格及相关情况的抽样调查表，其中建筑类别分别用1 、 2、 3、 4 表示多层、多层别墅、小高层、高层；交通状况综合分、物业管理综合分、周边配套等级是通过对居民用户的问卷调查平均而得。楼盘名均价Y建筑类别X1交通状况X2物业管理综合分X3绿化率X4（ %）周边配套等级X512150277336226003883073240037740

15、6418001663065300039843863000399458723003773368210026630793000388401010375041095010112800398408122450356346132100256307求：1. Y 关于 X1 、 X2、 X3、 X4 和 X5 的回归方程；2. 对回归方程和解释变量做显著性检验；3. 当 X1=4， X2=8， X3=7， X4=36% ， X5=8 时，对楼盘的均价进行预测。例 3.1 表 3.3 是某企业在16 个月度的产品产量和单位成本资料，研究二者关系。表 3.3 （ 0301）某企业某产品产量和单位成本资料月度序号

16、obs产量（台）x单机位成本（元/台） y14300346.2324004343.3434300327.4645016313.2755511310.7565648307.6175876314.5686651305.7296024310.82106194306.83117558305.11127381300.71136950306.84146471303.44156354298.03168000296.21为了明确产量和单机成本是何种关系，先绘制散点图。双曲线模型：y=a+b/x对数曲线模型：y=a+blnx双对数曲线模型：lny=a+lnx在自变量个数K=1 ，样本量n=16，在显著性水平=0

17、.01 下，dL=0.84， du=1.00，此时有D W=1.151568D W=1.115981D W=1.156127均有du=1.0 D W=1.151568 4- du=3说明三种模型来描述x 与 y 的关系都比较好。例 3.2 根据例 3.1 中数据，用非线性最小二乘法建立成本函数模型例 3.3 粮食产量通常由粮食生产劳动力（L） 、化肥施用量（K）等因素决定。表3.8 是我国粮食生产的有关数据（由于粮食生产劳动力不易统计，假定它在农业劳动力中的比例是一定的，故用农业劳动力的数据代替）， 研究其间关系，建立Cobb Douglas 生产函数模型。生产的产出量与投入要素之间并不简单地

18、满足线性关系，通常讨论的生产函数，都是以非线性的形式出现。Cobb Douglas 生产函数模型为Y=aL bK1-b（ 3.2.4）表 3.8（ 0308）我国 1975 2000 年粮食产量、农业劳动力、播种面积和化肥使用量年份粮食产量（ Y）（万吨）农业劳动力（ L）（万人）粮食播种面积（ M）（万公顷）化肥使用量（ K）（万公斤）1975284522756112106.25500001976286312796512074.35970001977282732812412040.06790001978304772837312058.78840001979332122869211926.31

19、0800001980320562918111723.412690001981325022983611495.813350001982354503091711346.315130001983387283120911404.716600001984407313092711288.417400001985379113035210884.517760001986391513131111093.319310001987402983087011126.8199900019883940831455.711012.3214150019894075532440.511220.523574001990446243

20、3336.411346.6259030019914352934186.311231.428051001992442663403711056.0293020019934564933258.211050.9315190019944451032690.310954.4331790019954666232334.511006.0359370019965045432260.411254.8382790019974941732433.911291.2398070019985123032626.411378.7408370019995038932911.811316.14124300200046218327

21、97.510846.34146400例 4.1 我国轿车保有量资料见表4.1表 4.1（ 0401） 1971 1992 年我国轿车保有量年份obs时间（T）保有量（辆，Y）1971147994259519371993486221510545661174117142014816284491939451980102009471124322712285719133193351436373315484806166101531787621518106063719115626519902013401532115005571992221844184根据表绘制时间序列曲线趋势图。例 4.6我国民航客运量数据

22、的季节调整。有关数据见表4.6表 4.6（ 0406） 我国 1993 年 10 月至 1998 年 3月民航客运量数据年、月民航客运量年、月民航客运量1993 年 10328.001996 年 1 月369.00263.00403.00251.00436.001994 年 1 月241.00447.00249.00483.00316.00439.00344.00514.00360.00550.00321.00489.00344.00534.00384.00498.00368.00402.00401.001997 年 1 月397.00363.00416.00336.00451.001995

23、年 1 月366.00486.20331.00507.00390.00458.99401.00493.00439.00562.00397.31474.00463.00528.00509.00436.13474.00398.00508.001998 年 1 月442.00458.94404.551995 年 12 月412.001998年 3月428.00例 5.4序列Pt是某国1960 年至 1993 年 GNP 平减指数的季度时间序列。表 5.4（ 0504）某国1960 年至 1993年 GNP 平减指数的季度时间序列年 /季GNP 指 数年 /季GNP 指 数年 /季GNP 指 数年 /

24、季GNP 指 数1960.156.041969.167.041978.181.151987.1136.8056.2167.5582.14139.0156.4167.8182.84141.0356.6768.0083.99143.241961.156.771970.168.441979.184.971988.1145.1257.0168.5686.10148.8956.9968.8687.49152.0257.5868.9688.62155.381962.157.581971.168.881980.189.891989.1158.6057.5769.2291.07161.8557.9269.54

25、91.79165.1258.5869.6593.03168.051963.158.761972.170.231981.194.401990.1171.9458.8070.4895.70176.4659.0070.6296.52180.2458.7471.0897.39185.131964.159.381973.171.411982.198.721991.1190.0159.5871.4699.42193.0359.4571.66100.25197.7059.7772.17101.54201.691965.160.271974.172.361983.1102.951992.1203.9860.6

26、572.57104.75206.7761.0372.97106.53208.5361.4073.16108.74210.271966.161.911975.173.771984.1110.721993.1212.8762.4374.13113.48214.2563.1374.56116.42215.8963.6974.96119.79218.211967.164.401976.175.711985.1122.8864.6576.58124.4465.2876.99126.6865.3777.75128.991968.165.631977.178.271986.1130.1265.7978.53

27、131.3066.1779.28132.8966.4780.13134.99例 5.6表 5.4是我国 1990 年 1 月至 1997年 12 月工业总产值的月度资料（ 1990 年不变价格），记作IPt，共有96 个观测值，对序列IPt建立 ARMA 模型。表 5.41990 年 1 月至 1997年 12 月我国工业总产值单位： 亿元月 /年1990199119921993199419951996199711421.41757.81984.22179.12903.32996.73476.63843.8421367.41485.71812.42408.72513.82740.32970.3

28、3181.2631719.71893.92274.72869.434093580.93942.64404.4941759.61969.82328.92916.73499.53746.34067.64520.1851795.72033.72373.13022.13642.63817.94746.8994638.9961748.121032515.83274.53871.44046.64417.2994969.9371637.31836.322882862.933733483.93806.84146.89981670.91914.723212864.23463.43510.63746.34198.

29、791760.12022.22441.129083663.743703.14011.14536.839101789.52045.12502.62911.83753.383810.74129.64718.91111888.62069.22608.83101.33973.1740914372.8995034.939121981.421362823.83664.34469.024650.7994991.55545.74月 /年工业总产值1990.11421.421367.431719.741759.651795.761748.171637.381670.991760.1101789.5111888.

30、6121981.41991.11757.821485.731893.941969.852033.76210371836.381914.792022.2102045.1112069.21221361992.11984.221812.432274.742328.952373.162515.8722888232192441.1102502.6112608.8122823.81993.12179.122408.732869.442916.753022.163274.572862.982864.292908102911.8113101.3123664.31994.12903.322513.8334094

31、3499.553642.663871.47337383463.493663.74103753.38113973.17124469.021995.12996.722740.333580.943746.353817.964046.673483.983510.693703.1103810.7114091124650.7991996.13476.622970.333942.644067.654746.89964417.29973806.883746.394011.1104129.6114372.899124991.51997.13843.8423181.2634404.4944520.1854638.

32、9964969.9374146.89984198.794536.839104718.91115034.939125545.74例 5.6 表 5.4是我国 1990年 1 月至 1997 年 12月工业总产值2资料（ 1990 年不变价格），记作ipt，共有96 个观测值，对序列ipt 建立 ARMA 模型。月 /年1990199119921993199419951996199711421.4001757.8001984.2002179.1002903.3002996.7003476.6003843.84021367.4001485.7001812.4002408.7002513.800274

33、0.3002970.3003181.26031719.7001893.9002274.7002869.4003409.0003580.9003942.6004404.49041759.6001969.8002328.9002916.7003499.5003746.3004067.6004520.18051795.7002033.7002373.1003022.1003642.6003817.9004746.8994638.99061748.1002103.0002515.8003274.5003871.4004046.6004417.2994969.93071637.3001836.30022

34、88.0002862.9003373.0003483.9003806.8004146.89981670.9001914.7002321.0002864.2003463.4003510.6003746.3004198.70091760.1002022.2002441.1002908.0003663.7403703.1004011.1004536.839101789.5002045.1002502.6002911.8003753.3803810.7004129.6004718.910111888.6002069.2002608.8003101.3003973.1704091.0004372.899

35、5034.939121981.4002136.0002823.8003664.3004469.0204650.7994991.5005545.740例 6.1 表 6.1 是某水库1998年至 2000年各旬的流量、降水量数据。试对其建立多项式分布滞后模型。表 6.1某水库各旬的流量与降水量数据序号流量 (VOL)降水量(RA)序号流量 (VOL)降水量(RA)1534234043082404241373193345184222926430164352795316674434636108827452491763844621128477047160193992481680104121649199

36、711370050133712346051143113208152202414202153220201521245438151621115534251720625628615182091257255819309758481152024245972541215373609322322377176113703923422462635524574516392647256768645143026546676557828271287136626481286962767578913729790406817823530584014469229341312100537012303132218064719375

37、73311801672134018348648734838933557026745296673673541751014137105039765521538561477430103934324782419例 6.2表 6.6 中， 序列 St和 Zt 分别表示1992 年 1 月至 1998年 12月经居民消费价格指数调整的中国城镇居民月人均可支配收入和人均生活费支出时间序列，现以人均生活费支出Zt为因变量，建立自回归分布滞后模型。表 6.6 城镇居民月人均人均生活费支出和可支配收入调整时间序列单位：元序列月份19921993199419951996199719981151.83265.9327

38、3.98370.00438.37521.01643.402159.86196.96318.81385.21561.29721.01778.623126.00200.19236.45308.62396.82482.38537.164124.88199.48248.00320.33405.27492.96545.795127.75200.75261.16327.94410.06499.90567.99St6134.48208.50273.45338.53415.38508.81555.797145.05218.82278.10361.09434.70516.24570.238138.31209.0

39、7277.15356.30418.21509.98564.389144.24223.17292.71371.32442.30538.46576.3610143.86226.51289.36378.72440.81537.09599.4011149.12226.62296.50383.58449.03534.12577.4012139.93210.32277.60427.78449.17511.23606.141139.47221.74234.28307.10373.58419.39485.702168.07186.49272.09353.55471.77528.09596.823110.471

40、85.92202.88263.37350.36390.04417.274113.22185.26227.89281.22352.15405.63455.605115.82187.62235.70299.73369.57426.81466.20Zt6118.20192.11237.89308.18370.42422.00455.197118.03186.75239.71315.87376.90428.70458.578124.45187.07252.52331.88387.44459.29475.409147.70219.23286.75385.99454.93517.06591.4210135

41、.14212.80270.00355.92403.77463.98494.5711135.20205.22274.37355.11410.10442.96496.6912128.03192.64250.01386.08400.48460.92516.16年月STZT1992.1151.83139.472159.86168.073126.00110.474124.88113.225127.75115.826134.48118.207145.05118.038138.31124.459144.24147.7010143.86135.1411149.12135.2012139.93128.031993.1265.93221.742196.96186.493200.19185.924199.48185.265200.75187.626208.50192.117218.82186.758209.07187.079223.17219.2310226.51212