關(guān)于影響我國南方幾省市農(nóng)業(yè)總產(chǎn)值因素的實(shí)證分析

1、關(guān)于影響我國南方幾省市農(nóng)業(yè)總產(chǎn)值因素的實(shí)證分析問題簡述：本文通過對(duì)我國南方幾省市（包括上海、江蘇、浙江、安徽、福建、江西、山東、湖北、湖南、廣東、廣西、海南、重慶、四川、貴州、云南）在2004年度農(nóng)業(yè)總產(chǎn)值、農(nóng)業(yè)勞動(dòng)力、有效灌溉面積、農(nóng)用化肥施用量、農(nóng)村居民家庭生產(chǎn)性固定資產(chǎn)以及農(nóng)業(yè)機(jī)械擁有量的統(tǒng)計(jì)數(shù)據(jù)的收集和整理，建立我國南方地區(qū)產(chǎn)出線性計(jì)量經(jīng)濟(jì)模型，并對(duì)模型中是否存在違反古典假設(shè)的情況（包括“多重共線性”、“異方差性”和“自相關(guān)性”）進(jìn)行了多種方式的檢驗(yàn)分析。然后對(duì)癥下藥，針對(duì)模型中所存在的問題選用適當(dāng)?shù)姆椒ㄟM(jìn)行修正。最后應(yīng)用產(chǎn)業(yè)經(jīng)濟(jì)學(xué)和區(qū)域經(jīng)濟(jì)學(xué)的相關(guān)知識(shí)對(duì)修正后的模型進(jìn)行分析，解釋其實(shí)

2、際的經(jīng)濟(jì)含義，并對(duì)其反映出來的現(xiàn)實(shí)問題提出幾點(diǎn)看法和建議。一、 模型假設(shè)（古典假設(shè)）1. 符號(hào)約定：分別對(duì)應(yīng)上海、江蘇、浙江、安徽、福建、江西、山東、湖北、湖南、廣東、廣西、海南、重慶、四川、貴州、云南這16個(gè)省市在2004年度的農(nóng)業(yè)總產(chǎn)值（單位：億元）分別對(duì)應(yīng)16個(gè)省市在2004年度的農(nóng)業(yè)勞動(dòng)力（單位：萬人）分別對(duì)應(yīng)16個(gè)省市在2004年度的有效灌溉面積（單位：萬公頃）分別對(duì)應(yīng)16個(gè)省市在2004年度的農(nóng)用化肥施用量（單位：萬噸）分別對(duì)應(yīng)16個(gè)省市在2004年度的農(nóng)村居民家庭生產(chǎn)性固定資產(chǎn)（單位：元）分別對(duì)應(yīng)16個(gè)省市在2004年度的農(nóng)業(yè)機(jī)械擁有量（單位：萬千瓦）隨機(jī)擾動(dòng)項(xiàng)序列殘差序列2.

3、解釋變量是一組固定的值，即是非隨機(jī)的。3. 解釋變量無測(cè)量誤差。4. 模型自身不存在設(shè)定誤差。5. 零均值假定，即在給定的的條件下，的條件期望值為零，即6. 同方差假定，即對(duì)于每一個(gè)給定的，的條件方差都等于一個(gè)常數(shù)，即7. 無自相關(guān)性假定，即不存在自相關(guān)，或中個(gè)項(xiàng)預(yù)測(cè)值互不影響，即8. 隨機(jī)擾動(dòng)項(xiàng)與解釋變量不相關(guān)，即9. 正態(tài)性假定，即假定服從均值為0，方差為的正態(tài)分布，表示為二、 統(tǒng)計(jì)數(shù)據(jù)收集整理的統(tǒng)計(jì)數(shù)據(jù)如下地區(qū)農(nóng)業(yè)總產(chǎn)值（億元）農(nóng)業(yè)勞動(dòng)力（萬人）有效灌溉面積(萬公頃）農(nóng)用化肥施用量單位：(萬噸)農(nóng)村居民家庭生產(chǎn)性固定資產(chǎn)（元）農(nóng)業(yè)機(jī)械總動(dòng)力（單位：萬千瓦） 上 海 98.271.7425

4、7.3115.871687.35112.61 江 蘇 981.21230.293840.98334.673290.253029.10 浙 江 529.4872.961403.8090.383738.942039.66 安 徽 617.91860.573285.38281.283908.283544.66 福 建 466.8735.92939.95120.292958.61951.91 江 西 383.7971.261873.16110.982398.181220.52 山 東 1599.32264.624760.79432.654733.428336.70 湖 北 733.41110.7120

5、43.69270.322431.091661.75 湖 南 671.71997.672675.34188.332191.082664.45 廣 東 851.71543.411315.93199.612166.261788.80 廣 西 500.81541.021516.67183.692305.271696.30 海 南 152.7187.25177.2733.924289.36221.62 重 慶 270.1813.19649.6971.602036.17695.67 四 川 804.72413.992503.15208.393092.471891.06 貴 州 275.51322.1068

6、2.7174.922714.07761.99 云 南 433.91690.221457.00129.224056.211542.91擬合使用的方法：最小距離法（用eviews軟件實(shí)現(xiàn)）三、 模型建立初始狀態(tài)下的農(nóng)業(yè)產(chǎn)出線性模型：用eviews軟件得到的分析報(bào)告如下dependent variable: ymethod: least squaresdate: 05/17/05 time: 19:07sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c242.6481186.0992

7、1.3038640.2215x10.0581570.0965900.6021040.5605x20.0797500.0793021.0056490.3383x30.0072420.3007750.0240760.9813x4-0.0417030.058281-0.7155370.4906x50.1219870.0538902.2636470.0471r-squared0.860155 mean dependent var585.6875adjusted r-squared0.790233 s.d. dependent var368.9720s.e. of regression168.9905

8、akaike info criterion13.37756sum squared resid285578.0 schwarz criterion13.66728log likelihood-101.0205 f-statistic12.30156durbin-watson stat3.062212 prob(f-statistic)0.000521四、 模型檢驗(yàn)與修正1. 對(duì)解釋變量之間多重共線性的檢驗(yàn)：（1）簡單相關(guān)系數(shù)矩陣法：用eviews得到的協(xié)方差矩陣如下 x1x2x3x4x5x1 1.000000 0.715554 0.481862 0.258392 0.648671x2 0.715

9、554 1.000000 0.422811 0.426337 0.885707x3 0.481862 0.422811 1.000000 0.178946 0.430369x4 0.258392 0.426337 0.178946 1.000000 0.549919x5 0.648671 0.885707 0.430369 0.549919 1.000000大致上可以判斷出，之間可能存在共線形。（2）變量顯著性和方程顯著性的綜合判斷：在顯著性水平，樣本容量的條件下，t統(tǒng)計(jì)量的臨界值為。由此可見，除了以外其余變量均是不顯著的，所以變量之間存在多重共線性。（3）對(duì)多重共線性進(jìn)行修正：變換模型形式：

10、回歸分析報(bào)告如下：dependent variable: lnymethod: least squaresdate: 05/17/05 time: 19:45sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c2.6849722.2942341.1703130.2690lnx1-0.0895820.196497-0.4558950.6582lnx20.0380250.2620960.1450790.8875lnx30.0596810.1351520.4415830.6682lnx

11、4-0.1558380.284183-0.5483710.5955lnx50.6626690.3028392.1881910.0535r-squared0.911189 mean dependent var6.170439adjusted r-squared0.866784 s.d. dependent var0.704874s.e. of regression0.257270 akaike info criterion0.402616sum squared resid0.661879 schwarz criterion0.692336log likelihood2.779075 f-stat

12、istic20.51983durbin-watson stat3.049254 prob(f-statistic)0.000058可見效果并不明顯，因此將采用逐步回歸法進(jìn)行修正：dependent variable: ymethod: least squaresdate: 05/17/05 time: 19:48sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c95.40032150.91780.6321340.5375x10.3803090.1043523.6444790.0

13、027r-squared0.486845 mean dependent var585.6875adjusted r-squared0.450192 s.d. dependent var368.9720s.e. of regression273.5893 akaike info criterion14.17760sum squared resid1047916. schwarz criterion14.27418log likelihood-111.4208 f-statistic13.28223durbin-watson stat1.581933 prob(f-statistic)0.0026

14、54dependent variable: ymethod: least squaresdate: 05/17/05 time: 19:52sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c126.302979.749531.5837440.1356x20.2501510.0358196.9838280.0000r-squared0.776977 mean dependent var585.6875adjusted r-squared0.761047 s.d. dependen

15、t var368.9720s.e. of regression180.3639 akaike info criterion13.34430sum squared resid455436.0 schwarz criterion13.44087log likelihood-104.7544 f-statistic48.77385durbin-watson stat2.702078 prob(f-statistic)0.000006dependent variable: ymethod: least squaresdate: 05/17/05 time: 19:49sample: 1 16inclu

16、ded observations: 16variablecoefficientstd. errort-statisticprob. c387.9315140.39822.7630800.0152x30.9434050.5287381.7842580.0961r-squared0.185269 mean dependent var585.6875adjusted r-squared0.127073 s.d. dependent var368.9720s.e. of regression344.7325 akaike info criterion14.63988sum squared resid1

17、663767. schwarz criterion14.73646log likelihood-115.1191 f-statistic3.183575durbin-watson stat2.112477 prob(f-statistic)0.096062dependent variable: ymethod: least squaresdate: 05/17/05 time: 19:50sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c104.7661310.11510.33

18、78300.7405x40.1603170.0991631.6166990.1282r-squared0.157323 mean dependent var585.6875adjusted r-squared0.097132 s.d. dependent var368.9720s.e. of regression350.5949 akaike info criterion14.67361sum squared resid1720835. schwarz criterion14.77018log likelihood-115.3889 f-statistic2.613717durbin-wats

19、on stat1.642909 prob(f-statistic)0.128246dependent variable: ymethod: least squaresdate: 05/17/05 time: 19:50sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c239.787360.274603.9782480.0014x50.1720910.0219357.8456600.0000r-squared0.814703 mean dependent var585.6875a

20、djusted r-squared0.801467 s.d. dependent var368.9720s.e. of regression164.4028 akaike info criterion13.15898sum squared resid378396.0 schwarz criterion13.25556log likelihood-103.2719 f-statistic61.55438durbin-watson stat2.497236 prob(f-statistic)0.000002綜合比較檢驗(yàn)和t檢驗(yàn)發(fā)現(xiàn)的擬合效果較好，從而得到基本方程：逐一引入其他變量：dependen

21、t variable: ymethod: least squaresdate: 05/17/05 time: 19:58sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c151.790389.060001.7043600.1121x50.1481240.0280985.2716270.0002x10.1056250.0803271.3149300.2113r-squared0.836455 mean dependent var585.6875adjusted r-squared

22、0.811294 s.d. dependent var368.9720s.e. of regression160.2825 akaike info criterion13.15911sum squared resid333976.1 schwarz criterion13.30397log likelihood-102.2729 f-statistic33.24441durbin-watson stat2.614918 prob(f-statistic)0.000008dependent variable: ymethod: least squaresdate: 05/17/05 time:

23、19:58sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c170.628071.203472.3963430.0323x50.1078290.0447122.4116460.0314x20.1079950.0665521.6227070.1286r-squared0.845914 mean dependent var585.6875adjusted r-squared0.822208 s.d. dependent var368.9720s.e. of regression15

24、5.5785 akaike info criterion13.09954sum squared resid314660.8 schwarz criterion13.24440log likelihood-101.7963 f-statistic35.68411durbin-watson stat2.549746 prob(f-statistic)0.000005dependent variable: ymethod: least squaresdate: 05/17/05 time: 19:59sample: 1 16included observations: 16variablecoeff

25、icientstd. errort-statisticprob. c224.615473.256423.0661530.0090x50.1678640.0250706.6958590.0000x30.1129100.2881980.3917810.7016r-squared0.816865 mean dependent var585.6875adjusted r-squared0.788691 s.d. dependent var368.9720s.e. of regression169.6105 akaike info criterion13.27225sum squared resid37

26、3980.3 schwarz criterion13.41711log likelihood-103.1780 f-statistic28.99300durbin-watson stat2.538863 prob(f-statistic)0.000016dependent variable: ymethod: least squaresdate: 05/17/05 time: 20:00sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c382.9909150.12742.551

27、1060.0241x50.1870790.0261847.1447680.0000x4-0.0577800.055509-1.0409160.3169r-squared0.828959 mean dependent var585.6875adjusted r-squared0.802645 s.d. dependent var368.9720s.e. of regression163.9147 akaike info criterion13.20393sum squared resid349284.3 schwarz criterion13.34879log likelihood-102.63

28、14 f-statistic31.50252durbin-watson stat2.894780 prob(f-statistic)0.000010保留繼續(xù)引入變量dependent variable: ymethod: least squaresdate: 05/17/05 time: 20:16sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c130.305289.822581.4506960.1725x50.1062440.0455092.3345630.0378x20.

29、0856560.0738001.1606410.2684x10.0655720.0864610.7583960.4628r-squared0.852961 mean dependent var585.6875adjusted r-squared0.816201 s.d. dependent var368.9720s.e. of regression158.1847 akaike info criterion13.17772sum squared resid300268.8 schwarz criterion13.37087log likelihood-101.4218 f-statistic2

30、3.20370durbin-watson stat2.717507 prob(f-statistic)0.000028dependent variable: ymethod: least squaresdate: 05/17/05 time: 20:16sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c162.225480.792572.0079240.0677x50.1062280.0468252.2686400.0425x20.1062190.0694231.5300350

31、.1519x30.0709950.2757570.2574550.8012r-squared0.846760 mean dependent var585.6875adjusted r-squared0.808450 s.d. dependent var368.9720s.e. of regression161.4859 akaike info criterion13.21903sum squared resid312932.3 schwarz criterion13.41218log likelihood-101.7522 f-statistic22.10284durbin-watson st

32、at2.578380 prob(f-statistic)0.000035dependent variable: ymethod: least squaresdate: 05/17/05 time: 20:17sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c289.1764157.90951.8312800.0920x50.1249910.0495832.5208680.0269x20.0989920.0681441.4526790.1720x4-0.0455060.05394

33、6-0.8435440.4154r-squared0.854539 mean dependent var585.6875adjusted r-squared0.818174 s.d. dependent var368.9720s.e. of regression157.3337 akaike info criterion13.16693sum squared resid297046.7 schwarz criterion13.36008log likelihood-101.3355 f-statistic23.49877durbin-watson stat2.898481 prob(f-sta

34、tistic)0.000026保留繼續(xù)引入變量dependent variable: ymethod: least squaresdate: 05/17/05 time: 20:20sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c243.2397175.89071.3829030.1941x50.1221530.0509592.3971090.0354x20.0797030.0755911.0544010.3143x4-0.0417520.055536-0.7517930.4

35、680x10.0587980.0885310.6641480.5203r-squared0.860147 mean dependent var585.6875adjusted r-squared0.809291 s.d. dependent var368.9720s.e. of regression161.1308 akaike info criterion13.25262sum squared resid285594.6 schwarz criterion13.49405log likelihood-101.0209 f-statistic16.91350durbin-watson stat

36、3.062277 prob(f-statistic)0.000115dependent variable: ymethod: least squaresdate: 05/17/05 time: 20:21sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c280.5572169.97501.6505790.1271x50.1234330.0522532.3622400.0377x20.0977030.0713221.3698960.1980x4-0.0447940.056348-

37、0.7949520.4435x30.0571480.2806260.2036430.8424r-squared0.855085 mean dependent var585.6875adjusted r-squared0.802389 s.d. dependent var368.9720s.e. of regression164.0208 akaike info criterion13.28817sum squared resid295931.1 schwarz criterion13.52960log likelihood-101.3054 f-statistic16.22668durbin-

38、watson stat2.928913 prob(f-statistic)0.000139綜合比較后發(fā)現(xiàn)，引入后，使擬合優(yōu)度提高，但是對(duì)的參數(shù)值有明顯的影響，且統(tǒng)計(jì)檢驗(yàn)也不顯著，由此可以斷定之間存在共線性，舍棄，其他變量對(duì)模型的影響均不顯著，均舍棄。最后得到修正后的模型：2對(duì)隨機(jī)誤差項(xiàng)之間異方差性的檢驗(yàn)：（1）圖示法：由圖可見其異方差性是相當(dāng)明顯的。（2）goldfeldquandt檢驗(yàn)：dependent variable: ymethod: least squaresdate: 05/17/05 time: 20:59sample: 1 6included observations: 6v

39、ariablecoefficientstd. errort-statisticprob. c78.9739150.633621.5597130.1938x50.2959290.0660864.4779440.0110r-squared0.833694 mean dependent var274.5000adjusted r-squared0.792117 s.d. dependent var137.7252s.e. of regression62.79473 akaike info criterion11.37882sum squared resid15772.71 schwarz crite

40、rion11.30941log likelihood-32.13646 f-statistic20.05198durbin-watson stat2.910560 prob(f-statistic)0.011007dependent variable: ymethod: least squaresdate: 05/17/05 time: 21:01sample: 11 16included observations: 6variablecoefficientstd. errort-statisticprob. c344.0503150.85182.2807180.0847x50.1460040

41、.0358774.0695510.0152r-squared0.805459 mean dependent var867.3667adjusted r-squared0.756824 s.d. dependent var391.7530s.e. of regression193.1847 akaike info criterion13.62637sum squared resid149281.3 schwarz criterion13.55696log likelihood-38.87912 f-statistic16.56124durbin-watson stat2.868491 prob(

42、f-statistic)0.015229可以得到f統(tǒng)計(jì)量：，查f分布表，在給定在顯著性水平，得臨界值，因?yàn)椋员砻麟S機(jī)誤差存在異方差性。（3）對(duì)異方差性的修正：加權(quán)最小二乘法（wls）：dependent variable: ymethod: least squaresdate: 05/17/05 time: 21:21sample: 1 16included observations: 16weighting series: wvariablecoefficientstd. errort-statisticprob. c108.018833.186353.2549160.0058x50.2

43、376480.0277818.5542500.0000weighted statisticsr-squared-0.092964 mean dependent var428.0649adjusted r-squared-0.171033 s.d. dependent var117.3273s.e. of regression126.9649 akaike info criterion12.64217sum squared resid225681.2 schwarz criterion12.73874log likelihood-99.13734 durbin-watson stat1.4921

44、19unweighted statisticsr-squared0.696475 mean dependent var585.6875adjusted r-squared0.674794 s.d. dependent var368.9720s.e. of regression210.4130 sum squared resid619830.6durbin-watson stat1.123570對(duì)數(shù)變換法：dependent variable: lnymethod: least squaresdate: 05/17/05 time: 21:27sample: 1 16included observations: 16variablecoefficientstd. errort-statisticprob. c1.5197620.4068293.7356280.0022lnx50.6457900.05594911.542380.0000r-squared0.904908 mean dependent var6.170439adjusted r-squared0.898116 s.d. dependent var0.704874s.e. of regression0.224990 akaike info criterio