云南省关于影响固定资本形成总额因素的计量报告

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1、编号：时间：2021年x月x日书山有路勤为径，学海无涯苦作舟页码：第60页 共60页计量经济学分析报告关于云南省固定资本形成总额影响因素的计量分析指导老师：沈鸣班级：市调0901姓名：陈妍琦 学号：2009161676 一、前言在科技统计分析中，经常需要对与科技进步相关的经济发展质量进行反映，因而常常要面临有关资本指标的选择，其中有两个十分相似的指标，即资本形成和固定资本形成。其实这个问题对大多数人来说并不是很困难，把握住核心的一点，即资本形成总额包括固定资本形成总额与存货增加就可以了。本文旨在研究云南省的固定资本形成总额，固定资本形成总额可分为有形固定资本形成总额和无形固定资本形成总额。有形

2、固定资本形成总额包括一定时期内完成的建筑工程、安装工程和设备工器 具购置（减处置）价值，以及土地改良、新增役、种、奶、毛、娱乐用牲畜和新增经济林木 价值。无形固定资本形成总额包括矿藏的勘探、计算机软件、娱乐和文学艺术品原件等获得减处置。固定资本形成都是生产总值统计中的主要指标。它指的是以货币形式表现的、在一定时期内建造和购置固定资产的工作量以及与此有关费用的总称。固定资产投资统计中的指标，有分月和分季度的数据；固定资产投资额存在着重复统计，比如上一期留存下来的固定资产被其他生产单位在计算期购买或租赁而产生的费用，固定资本形成中则扣除了由于出售、易货交易和实物资本转移而转出的价值；在统计口径上，

3、固定资产投资自1997年起，统计起点已由5万元提高到了50万元；固定资本形成中还包含部分无形资产的净增加额。本文通过选择固定资本形成总额来观察云南省经济发展，希望可以起到一个概括性的作用。一、 理论背景云南省是一个内地省市，没有与其相连接的海域，且处于横断山脉的地理位置，造就了它的交通个方面的限制，与中国南部和中原地区的省市相比较，农作物的发展不如中原地区的鱼米之乡，相对偏远的地理位置对外商投资也有一定影响。 中华人民共和国成立以来，特别是改革开发以来，云南省的国民经济和各项社会事业进入了快速发展的时期。云南全省面貌发生了深刻变化，经济实力明显增强。云南经济发展迅速，提前二年实现人均1000美

4、元的阶段性目标,以能源、通讯、交通为重点的基础设施不断改善，工业化进程明显加快。10多年来，云南经济保持了较快的增长速度，国内生产总值在全国的排序，由1980年的第22位，上升至1999年的第18位，以能源、通讯、交通为重点的基础设施不断改善，工业化进程明显加快。产业结构逐步得到调整，一批新的支柱产业正在兴起。农业的基础地位不断增强，农业经济持续发展，粮食生产连续获得丰收，1999年粮食产量达1300万吨。云南省气候和地理位置的缘故，适合种植很多经济林木，期间可以饲养多种家禽，随着经济的发展，大片的土地得到了开发与利用。固定资本形成总额也呈上升的发展趋势，一切发展都呈现较好的趋势。影响固定资本

5、形成总额的因素很多很多，在这里我们只几个主要的因素来讨论。固定资本形成总额与GDP，进口额，固定资产投资额，社会消费品零售总额，外商直接投资额等都有关系，在下文中主要就是对这几个因素进行了分析。二、 模型的选择与建立选取了以下五个解释变量，来考察对固定资本形成总额的影响：Y:云南省固定资本形成总额，单位，亿元。X1：云南省GDP，单位：亿元。X2：云南省进口额，单位：亿元。X3：云南省固定资产投资额，单位：亿元。X4：云南省社会消费零售总额，单位：亿元。X5：云南省外商直接投资额：单位：亿元。Y = 0 + 1X1 + 2X2 + 3X3 + 4X4 + 5X5 + 一共有5个解释变量；0为常

6、数项；为随机误差项，描述变量外的因素对模型的干扰。四、数据来源与分析1.原始数据时间序列的变量及原始数据：由于解释变量和被解释变量中都是以“亿元”作单位，数据的口径均同质可比，没有实物量指标，故可用现价数据，而不用调整为可比价YX1X2X3X4X5年份固定资本形成总额（亿元）GDP （亿元）进口额 （亿元）固定资产投资额 （亿元）社会消费品零售总额（亿元）外商直接投资额 (亿元）198126.0994.130.53588218.6842.040.0090365198229.01110.120.50851524.5547.540.01305825198329.63120.070.56742124

7、.555.190.01797887198441.97139.580.91637332.9965.580.0276913198550.07164.962.3645546.2884.450.04581096198655.66182.283.3298849.9291.910.1222291198759.75229.032.9743354.38102.550.1786608198877.51301.093.79356467.77135.570.1153851198979.9363.056.52341267.8142.150.2786174199089.25451.675.449575.74145.59

8、0.12436321991150.37517.417.96046398.32163.750.15756971992208.41618.6911.25144140.69204.61.2755271993288.95783.2718.27534251.4261.95.5902921994337.09983.7837.39654321.73304.9717.495961995399.911222.1556.83774380.57369.5518.789751996472.821517.6968.66615448.02414.1814.965561997560.291676.1763.39542540

9、.5467.0713.678171998703.151831.3360.39852672.54500.0912.060991999738.211899.8251.75924717.28538.9512.736162000722.272011.1952.78887697.94583.1710.606292001770.22138.3161.65868734.81640.85.3444592002860.672312.8265.94286828.65711.259.24209820031068.512556.0282.032521021.18782.4613.8656320041352.78308

10、1.91124.89281330.6915.3111.7133320051755.33472.89171.75051755.31034.414.2142420062156.844006.72226.42382220.451188.8824.1019420072616.774772.52307.47762798.891394.5430.0000620082616.775700.1320.33583526.61764.753.9550920093502.46169.75379.01394526.42051.162.1689320105213.17220.1451.82625528.72500.18

11、9.966662，在解释变量之间一一做散点图。由散点图可看出，被解释变量Y与解释变量X1、X2、X3、X4、X5之间基本存在着线性关系，所以初步估计我所要建立的模型是直线模型。 固定资本形成总额与GDP的关系 固定资本形成总额与进口额的关系 固定资本形成总额与固定资产投资额的关系 固定资本形成总额与社会消费 品零售额的关系固定资本形成总额与外商直接投资额 基本统计量YX1X2X3X4X5 Mean 901.1217 1888.287 88.23493 966.7727 590.1447 14.09538 Median 436.3650 1369.920 52.27406 414.2950 39

12、1.8650 9.924194 Maximum 5213.100 7220.100 451.8262 5528.700 2500.100 89.96666 Minimum 26.09000 94.13000 0.508515 18.68000 42.04000 0.009036 Std. Dev. 1222.936 1973.996 123.9789 1409.945 633.8903 20.81079 Skewness 1.976121 1.233406 1.675712 1.943477 1.528074 2.267134 Kurtosis 6.665648 3.622869 4.6660

13、26 5.954296 4.657863 7.899764 Jarque-Bera 36.32149 8.091412 17.50960 29.79535 15.11069 55.70910 Probability 0.000000 0.017497 0.000158 0.000000 0.000523 0.000000 Observations303030303030协方差YX1X2X3X4X5Y1445720.88422269118.38134144130.2058741652002.16849738970.63606923357.2514624X12269118.381343766772

14、.63223231449.1166062618338.641631203693.7433536486.8693819X2144130.205874231449.11660614858.4064615167359.1922774721.22471592351.11292648X31652002.168492618338.64163167359.192271921678.87467853880.66340827240.5179288X4738970.6360691203693.7433574721.2247159853880.663408388423.00817812022.5690476X523

15、357.251462436486.86938192351.1129264827240.517928812022.5690476418.652493742简单相关系数YX1X2X3X4X5Y10.9723670267630.9833919805370.991123965980.9861261612210.949407520978X10.97236702676310.9783285039510.9731968711560.9951277251960.918808612769X20.9833919805370.97832850395110.9904283108040.9835702645230.94

16、2671833345X30.991123965980.9731968711560.99042831080410.9883352773030.96039106613X40.9861261612210.9951277251960.9835702645230.98833527730310.94279650752X50.9494075209780.9188086127690.9426718333450.960391066130.942796507521五、模型估计模型的初步估计与检验Y=0+1X1+2X2+3X3+4X4+5X5+ 模型的回归分析CX1X2X3X4X5R2F合格五元-99.4036-0

17、.674874.571649-0.0568943.348834*-3.5329780.986175342.3919*不四元-16.45140.2062920.609772*0.534287-0.3864210.984197389.2506*不-46.5515-0.2275230.518969*1.540989-2.4000110.984802404.9883*不-92.0768-0.632825*4.217241*3.161763*-3.5367720.986164445.4792*不16.72390.095267-0.2555590.754738*-0.1847040.98349372.30

18、15*不-95.8836-0.602354.31684-0.058283.066812*0.985936438.1531*不三元-16.71370.62624*0.541395-0.5035530.98419539.4967*不-102.1553.479084*0.999634*7.5465110.979724418.7597*不67.27590.8084430.808142*-1.3321520.982526487.3162*不-17.10380.2322090.60094*0.5367570.984194539.6513*不-183.357-0.539528*3.5286*1.481166

19、0.980739441.3008*不18.00290.0911590.736834*-0.0970040.98348515.941*不-46.1544-0.1946090.49581*1.4156160.98469557.4081*不-38.12820.1590125.280184*12.28007*0.974379329.6006*不-88.3741-0.559199*3.953507*2.874867*0.985925607.0893*合16.36820.095702-0.246940.750776*0.983489516.2355*不二元-31.15940.1486427.384845*

20、0.969528429.5277*不17.79010.0914650.735042*0.98348803.6675*不-187.682-0.570682*3.670991*0.980693685.74*合-147.7850.397836*21.1189*0.965619379.1565*合66.35080.9085210.780543*0.982488757.4147*不-118.5254.076322*1.118324*0.97801600.4235*合43.6437.830709*11.81498*0.971562461.2129*合-17.64220.617078*0.5459510.9

21、84184840.069*不70.74770.886047*-1.8610260.982405753.7457*不-178.1941.580211*10.41210.975934547.4518*不一元-236.390.602404*0.945498485.7392*合45.22119.700247*0.96706822.0249*合70.020.859666*0.9823271556.312*合-221.6221.902489*0.972445988.1424*合114.71955.7915*0.901375255.9026*合每个变量下的数值即为该变量的系数值，符号*表示该项系数值通过显著

22、性水平为0.05的T检验；R2表示该模型的可决系数；F表示对应的模型整体的F检验值，若出现*号则表明通过了显著性水平为0.05的F检验；若是偏回归系数和方程整体都通过了各自的假设检验，则“合格”一栏中注上“合”，若有任一项没有通过检验，则该栏注上“不”。六、五元模型的检验回归方程：Dependent Variable: YMethod: Least SquaresDate: 12/03/11 Time: 14:50Sample: 1981 2010Included observations: 30VariableCoefficientStd. Errort-StatisticProb. C-9

23、9.4036275.63059-1.3143310.2012X1-0.6748700.364239-1.8528210.0762X24.5716492.9614261.5437320.1357X3-0.0568940.421120-0.1351020.8937X43.3488341.5511152.1589860.0411X5-3.5329785.489547-0.6435830.5259R-squared0.986175 Mean dependent var901.1217Adjusted R-squared0.983295 S.D. dependent var1222.936S.E. of

24、 regression158.0640 Akaike info criterion13.14073Sum squared resid599621.8 Schwarz criterion13.42097Log likelihood-191.1110 F-statistic342.3919Durbin-Watson stat1.560280 Prob(F-statistic)0.000000 a:自相关检验DW检验值是1.071.5602801.83，所以存在自相关。 b: 检验异方差将Xi按升序排列，i=1，2，3，4，5；删掉8个。将其余样本点划分为样本容量各为11个的两个子样本。分别用两个子

25、样本做回归。求出大和小，并做F检验。结果如下X1X2X3X4X5大175966.9141539.2206228.3175966.9175966.9小270.8036270.8036270.8036270.8036270.8036F-statistic649.795276522.6636574761.5419 649.795276649.7953F临界值5.055.055.055.055.05是否存在异方差是是是是是（一）处理自相关对模型做广义一阶差分回归，得AR（1）= 0.372682，经差分后得到新序列，设为C1、Y1、X11、X21、X31、X41、X51、生成新数据：C1=1- 0.3

26、72682 补第一个数c1= (1-0.3726822)0.5Y1=y-y (-1)* 0.372682 y1=y*(1-0.3726822)0.5x11=x1-x1 (-1)* 0.372682 x11=x1*(1-0.3726822)0.5x21=x2-x2(-1)* 0.372682 x21=x2*(1-0.3726822)0.5 x31=x3-x3(-1)* 0.372682 x31=x3*(1-0.3726822)0.5x41=x4-x4(-1)* 0.372682 x41=x4*(1-0.3726822)0.5x51=x5-x5(-1)* 0.372682 x51=x5*(1-0.

27、3726822)0.5obsY1C1X11X21X31X41X51198124.210450.92798187.348790.49727717.3342839.011400.008386198219.286730.62731875.039440.30880117.5883031.872450.009691198318.818500.62731879.030260.37790715.3506637.472700.013112198430.927430.62731894.832070.70490523.8592945.011680.020991198534.428540.627318112.941

28、02.02303433.9852260.009510.035491198636.999810.627318120.80242.44865532.6722860.437010.105156198739.006520.627318161.09751.73334435.7757168.296800.133108198855.242250.627318215.73462.68508547.5035597.351460.048801198951.013420.627318250.83925.10961942.5433491.625500.235615199059.472710.627318316.367

29、83.01834250.4721692.613250.0205281991117.10810.627318349.08075.92953270.09307109.49120.1112221992152.36980.627318425.86068.284719104.0479143.57331.2168041993211.27930.627318552.695414.08213198.9674185.64935.1149261994229.40350.627318691.869430.58565228.0377207.364615.412561995274.28260.627318855.512

30、942.90072260.6670255.893212.269321996323.78070.6273181062.21747.48375306.1884276.45547.9629581997384.07850.6273181110.55437.80478373.5310312.71268.1007751998494.34000.6273181206.65236.77219471.1054326.02146.9633821999476.15870.6273181217.31629.24980466.6364352.57558.2412462000447.15240.6273181303.16

31、133.49913430.6227382.31305.8597522001501.02300.6273181388.77641.98522474.7003423.46301.3916862002573.63030.6273181515.91042.96378554.7995472.43547.2503142003747.75380.6273181694.07457.45680712.3571517.389910.421272004954.56560.6273182129.32794.32076950.0246623.70126.54585920051251.1430.6273182324.31

32、8125.20521259.409693.28049.84889320061502.6710.6273182712.436162.41551566.281803.377718.8045520071812.9550.6273183279.288223.09351971.368951.465821.0177020081641.5470.6273183921.468205.74442483.5041244.98042.7746120092527.1770.6273184045.425259.63053212.1001393.42842.0608420103907.8190.6273184920.74

33、5310.57453841.7921735.69266.79742Dependent Variable: Y1Method: Least SquaresDate: 12/05/11 Time: 14:20Sample: 1981 2010Included observations: 30VariableCoefficientStd. Errort-StatisticProb. C1-128.3329107.8559-1.1898550.2457X11-1.0441830.423228-2.4671900.0211X217.7412863.3668892.2992400.0305X31-0.41

34、15360.499486-0.8239190.4181X414.6701271.8369242.5423620.0179X51-2.6893176.659050-0.4038590.6899R-squared0.974779 Mean dependent var629.9881Adjusted R-squared0.969524 S.D. dependent var885.8026S.E. of regression154.6371 Akaike info criterion13.09690Sum squared resid573903.1 Schwarz criterion13.37714L

35、og likelihood-190.4534 Durbin-Watson stat1.402876检验自相关：DW检验值是1.071.4028761.83，所以存在自相关。 对新序列进行异方差检验将Xi按升序排列，i=1，2，3，4，5；删掉8个。将其余样本点划分为样本容量各为11个的两个子样本。分别用两个子样本做回归。求出大和小，并做F检验。结果如下：X11X21X31X41X51大137822.4204801.4179159.8137822.4365754.3小359.0328359.0328359.0328359.0328359.0328F-statistic383.8713343570

36、.4253205499.006776383.87133431018.721F临界值5.055.055.055.055.05是否存在异方差是是是是是（二）处理异方差建立新序列：、，名称分别为Y2、X12、X22、X32、X42、X52。Genr e=abs(resid)Ls y1/e c1/e x11/e x21/e x31/e x41/e x51/eGenr y2=y1/e Genr c2=c1/eGenr x12=x11/e Genr x22=x21/eGenr x32=x31/e Genr x42=x41/eGenr x52=x51/eobsY2C2X12X22X32X42X521981

37、1.442061 0.059564 5.202805 0.029620 1.032491 2.323658 0.0004991982 3.634458 0.188443 19.25645 0.088923 4.293006 6.006155 0.0018261983 1.340730 0.071245 7.938157 0.037514 1.619762 2.669754 0.0009341984 2.094413 0.067720 8.771434 0.057586 2.073145 3.048202 0.0014221985 0.864720 0.025116 3.844719 0.055

38、111 1.078647 1.507222 0.0008911986 1.057399 0.028578 4.834006 0.088307 1.323862 1.727199 0.0030051987 1.065691 0.027321 5.806513 0.075407 1.378676 1.865927 0.0036371988 0.869556 0.015741 4.397961 0.055412 0.989903 1.532388 0.0007681989 1.269657 0.024889 8.384886 0.150663 1.565887 2.280438 0.00586419

39、90 5.004184 0.084143 35.26677 0.425501 5.913842 7.792713 0.0017271991 5.604456 0.047857 22.97789 0.353520 4.366336 5.239933 0.0053231992 37.26768 0.244587 140.4221 2.553703 31.93196 35.11618 0.2976141993 85.06427 0.402615 292.6378 6.827858 93.92566 74.74521 2.0593471994 6.171348 0.026902 24.55878 0.

40、933556 8.031568 5.578463 0.4146241995 234.7286 0.855791 970.5571 45.13707 302.2255 218.9911 10.499971996 8.486728 0.026211 36.91486 1.670170 10.89721 7.246266 0.2087201997 9.340201 0.024318 37.82537 1.430616 12.19722 7.604691 0.1969981998 4.278003 0.008654 14.70655 0.485032 5.400852 2.821379 0.06026

41、11999 7.936581 0.016668 29.38482 0.800569 11.09429 5.876705 0.1373652000 394.5556 0.882374 1646.776 43.22389 571.4781 337.3430 5.1704932001 14.38088 0.028703 56.95441 1.642294 19.57184 12.15467 0.0399462002 27.79175 0.048449 103.9811 2.964697 37.25493 22.88897 0.3512702003 11.87562 0.015882 37.66958

42、 1.208962 15.04974 8.217046 0.1655082004 24.35420 0.025513 72.96540 2.956885 31.50247 15.91273 0.1670072005 9.844371 0.007868 25.35717 1.254030 12.81625 5.454939 0.0774942006 11.05777 0.007359 27.36034 1.546160 15.16259 5.911850 0.1383782007 18.43666 0.010169 45.03725 2.901600 26.41252 9.675835 0.21

43、37372008 2.862845 0.001744 9.224778 0.518417 5.707286 2.171236 0.0745992009 9.126966 0.003612 20.67699 1.270208 15.16955 5.032402 0.1519042010 9.633295 0.002465 16.51629 1.033572 12.64714 4.278713 0.164665Dependent Variable: Y2Method: Least SquaresDate: 12/06/11 Time: 19:03Sample: 1981 2010Included

44、observations: 30VariableCoefficientStd. Errort-StatisticProb. C2-25.450456.360935-4.0010550.0005X12-0.3303010.031788-10.390650.0000X221.2624060.3434343.6758370.0012X320.3721160.0501777.4160490.0000X422.1480210.16620612.923820.0000X52-5.9647181.216551-4.9029760.0001R-squared0.999876 Mean dependent va

45、r31.71469Adjusted R-squared0.999850 S.D. dependent var81.37620S.E. of regression0.995524 Akaike info criterion3.005761Sum squared resid23.78563 Schwarz criterion3.286001Log likelihood-39.08642 Durbin-Watson stat1.063735检验子相关：对新序列作回归分析，DW=1.063735，1.0637351.07,存在自相关异方差的检验X12X22X32X42X52大3.4694242.205

46、6144.8788436.2194133.814167小1.409991.9957810.9129051.409991.995781F-statistic2.4606021.1051385.3443054.4109621.911115F临界值5.055.055.055.055.05是否存在异方差否否是否否（三）处理自相关对模型做广义一阶差分回归，得AR（1）=0.689860，经差分后得到新序列生成新数据：C3=c2-c2(-1)* 0.689860 补第一个数：c3=c2*(1-0.6898602)0.5Y3=y2-y2(-1)* 0.689860 y3=y2*(1-0.6898602)0.

47、5x13=x12-x12(-1)* 0.689860 x13=x12*(1-0.6898602)0.5x23=x22-x22(-1)* 0.689860 x23=x22*(1-0.6898602)0.5 x33=x32-x32(-1)* 0.689860 x33=x32*(1-0.6898602)0.5x43=x42-x42(-1)* 0.689860 x43=x42*(1-0.6898602)0.5x53=x52-x52(-1)* 0.689860 x53=x52*(1-0.6898602)0.5obsY3C3X13X23X33X43X531981 1.043970-0.080276 3.7

48、66533 0.021443 0.747465 1.682196 0.0003621982 2.639638-0.062542 15.66724 0.068490 3.580731 4.403156 0.0014821983-1.166537-0.062542-5.346096-0.023831-1.341811-1.473652-0.0003261984 1.169498-0.062542 3.295217 0.031707 0.955736 1.206446 0.0007771985-0.580132-0.062542-2.206343 0.015384-0.351533-0.595611

49、-8.92E-051986 0.460863-0.062542 2.181689 0.050289 0.579747 0.687427 0.0023901987 0.336234-0.062542 2.471726 0.014487 0.465397 0.674401 0.0015631988 0.134378-0.062542 0.392279 0.003391 0.038809 0.245160-0.0017411989 0.669785-0.062542 5.350909 0.112436 0.882993 1.223305 0.0053341990 4.128299-0.062542

50、29.48237 0.321565 4.833599 6.219530-0.0023181991 2.152269-0.062542-1.351246 0.059983 0.286613-0.135948 0.0041311992 33.40139-0.062542 124.5706 2.309824 28.91980 31.50136 0.2939421993 59.35479-0.062542 195.7663 5.066160 71.89708 50.51997 1.8540351994-52.51109-0.062542-177.3204-3.776710-56.76399-45.98

51、527-1.0060371995 230.4712-0.062542 953.6149 44.49305 296.6848 215.1427 10.213941996-153.4431-0.062542-632.6336-29.46809-197.5961-143.8269-7.0347921997 3.485547-0.062542 12.35928 0.278432 4.679666 2.605782 0.0530111998-2.165428-0.062542-11.38766-0.501892-3.013520-2.424793-0.0756401999 4.985358-0.0625

52、42 19.23936 0.465964 7.368454 3.930348 0.0957932000 389.0805-0.062542 1626.505 42.67161 563.8246 333.2889 5.0757302001-257.8073-0.062542-1079.091-28.17614-374.6680-220.5648-3.5269702002 17.87095-0.062542 64.69054 1.831745 23.75310 14.50394 0.3237132003-7.296793-0.062542-34.06282-0.836264-10.65095-7.573136-0.0768192004 16.16169-0.062542 46.97866 2.122870 21.12025 10.24412 0.0528302005-6.956621-0.062542-24.97874-0.785807-8.916037-5.522620-0.0377172006 4.266535-0.062542 9.86