我國(guó)糧食生產(chǎn)與相關(guān)投入計(jì)量經(jīng)濟(jì)學(xué)模型分析

1、精選優(yōu)質(zhì)文檔-傾情為你奉上我國(guó)糧食生產(chǎn)與相關(guān)投入計(jì)量經(jīng)濟(jì)學(xué)模型分析一 理論分析二 建立模型以19802003年各年糧食產(chǎn)量作為被解釋變量，解釋變量中，包括農(nóng)業(yè)化肥施用量，糧食播種面積，成災(zāi)面積，農(nóng)業(yè)機(jī)械總動(dòng)力，農(nóng)業(yè)勞動(dòng)力。模型設(shè)定為其中 Y：糧食產(chǎn)量（萬(wàn)噸） X1：農(nóng)業(yè)化肥試用量（萬(wàn)噸） X2：糧食播種面積（千公頃） X3：成災(zāi)面積（千公頃） X4：農(nóng)業(yè)機(jī)械總動(dòng)力（萬(wàn)千瓦） X5：農(nóng)業(yè)勞動(dòng)力（萬(wàn)人）顯著性水平0.05三 估計(jì)參數(shù)假定模型中隨機(jī)項(xiàng)滿足基本假定，用OLS法估計(jì)參數(shù)，估計(jì)結(jié)果如下：Dependent Variable: YMethod: Least SquaresDate: 12/1

2、5/06 Time: 00:16Sample: 1980 2003Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C-5410.50021545.50-0.0.8046X18.1.5.0.0001X20.0.1.0.2949X3-0.0.-2.0.0381X4-0.0.-1.0.0718X50.0.1.0.1542R-squared0. Mean dependent var42847.33Adjusted R-squared0. S.D. dependent var5325.186S.E. of re

3、gression1676.383 Akaike info criterion17.89898Sum squared resid Schwarz criterion18.19350Log likelihood-208.7878 F-statistic42.81740Durbin-Watson stat0. Prob(F-statistic)0.估計(jì)方程為t: (-0.25) (5.07) (1.08) (-2.24) (-1.91) (1.49) =0.9224 F=42.8174由于，未通過(guò)t檢驗(yàn)，而且前的符號(hào)經(jīng)濟(jì)意義也不合理，因此解釋變量鍵可能存在多重共線性。四 多重共線性分析1. 檢驗(yàn)簡(jiǎn)單

4、相關(guān)系數(shù)，的相關(guān)系數(shù)表如下：X1X2X3X4X5X1 1.-0. 0. 0. 0.X2-0. 1.-0.-0.-0.X3 0.-0. 1. 0.-0.X4 0.-0. 0. 1. 0.X5 0.-0.-0. 0. 1.2. 用Y分別關(guān)于，作一元線性回歸得：變量參數(shù)估計(jì)值4.255-0.3480.4690.2813.235t統(tǒng)計(jì)量8.29-1.192.5285.1184.5220.75760.06060.22510.54350.4817 由上表知，解釋變量的重要程度依次為，3. 將各解釋變量按以上順序分別引入基本回歸模型中，并用OLS法估計(jì)。先把引入模型，用Y關(guān)于，做回歸并用OLS法估計(jì)得：De

5、pendent Variable: YMethod: Least SquaresDate: 12/15/06 Time: 18:13Sample: 1980 2003Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C29444.911146.28725.687210.0000X110.230871.7.0.0000X4-0.0.-4.0.0001R-squared0. Mean dependent var42847.33Adjusted R-squared0. S.D. dependent var53

6、25.186S.E. of regression1904.447 Akaike info criterion18.05824Sum squared resid Schwarz criterion18.20550Log likelihood-213.6989 F-statistic79.41445Durbin-Watson stat0. Prob(F-statistic)0. =0.9224 t (25.69)(7.82) (-4.75)可見(jiàn)，引入后，擬合優(yōu)度有所提高，但回歸參數(shù)的符號(hào)不對(duì)，所以應(yīng)該把從模型中刪除。按照上面的方法依次引入，經(jīng)過(guò)檢驗(yàn)均可保留。刪去不符合條件的解釋變量，得到Y(jié)關(guān)于，的

7、方程： (-1.95) (8.51) (2.37) (-2.39) (2.34) =0.9067 F=46.1480 DW=0.38Dependent Variable: YMethod: Least SquaresDate: 12/15/06 Time: 12:41Sample: 1980 2003Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C-33196.4016990.08-1.0.0656X15.0.8.0.0000X20.0.2.0.0286X3-0.0.-2.0.0273X50.0.2

8、.0.0303R-squared0. Mean dependent var42847.33Adjusted R-squared0. S.D. dependent var5325.186S.E. of regression1789.857 Akaike info criterion18.00071Sum squared resid Schwarz criterion18.24614Log likelihood-211.0085 F-statistic46.14801Durbin-Watson stat0. Prob(F-statistic)0.五 序列相關(guān)性分析對(duì)上一步得到的回歸方程做序列相關(guān)性

9、分析，采用LM檢驗(yàn)法：1. 階滯后： Breusch-Godfrey Serial Correlation LM Test:F-statistic24.93890 Probability0.Obs*R-squared17.89932 Probability0.Test Equation:Dependent Variable: RESIDMethod: Least SquaresDate: 12/15/06 Time: 13:05Presample missing value lagged residuals set to zero.VariableCoefficientStd. Errort-

10、StatisticProb. C5709.0289294.2960.0.5472X10.0.0.0.8155X2-0.0.-1.0.2322X3-0.0.-1.0.1067X50.0.0.0.3489RESID(-1)1.0.6.0.0000RESID(-2)-0.0.-2.0.0166R-squared0. Mean dependent var1.33E-11Adjusted R-squared0. S.D. dependent var1626.789S.E. of regression954.0127 Akaike info criterion16.79772Sum squared res

11、id Schwarz criterion17.14132Log likelihood-194.5727 F-statistic8.Durbin-Watson stat2. Prob(F-statistic)0.得估計(jì)結(jié)果為：t(0.61) (0.24) (-1.24) (-1.70) (0.96) (6.45) (-2.66)=0.7458 N=24 P=2 K=5（包含常數(shù)項(xiàng)）LM=(N-P)*=(24-2)*0.7458=16.4076=5.99 由于LM>,而且，的回歸系數(shù)顯著不為零，表明此模型存在一階，二階自相關(guān)2. 階滯后： Breusch-Godfrey Serial Cor

12、relation LM Test:F-statistic17.48614 Probability0.Obs*R-squared18.39076 Probability0.Test Equation:Dependent Variable: RESIDMethod: Least SquaresDate: 12/15/06 Time: 13:27Presample missing value lagged residuals set to zero.VariableCoefficientStd. Errort-StatisticProb. C2300.2259626.9830.0.8142X1-0.

13、0.-0.0.9734X2-0.0.-1.0.2421X3-0.0.-1.0.2466X50.0.1.0.2144RESID(-1)1.0.4.0.0003RESID(-2)-0.0.-0.0.5494RESID(-3)-0.0.-1.0.2537R-squared0. Mean dependent var1.33E-11Adjusted R-squared0. S.D. dependent var1626.789S.E. of regression942.9348 Akaike info criterion16.79707Sum squared resid Schwarz criterion

14、17.18976Log likelihood-193.5649 F-statistic7.Durbin-Watson stat2. Prob(F-statistic)0.得估計(jì)結(jié)果為：t (0.24) (-0.03) (-1.21) (-1.20) (1.29) (4.67) (-0.61) (-1.18)=0.7663 N=24 P=3 K=5（包含常數(shù)項(xiàng)）LM=(24-3)*0.7663=16.0923>=7.81，表明存在自相關(guān)；但由于的回歸系數(shù)不顯著，故不存在三階序列相關(guān)性。3. 運(yùn)用廣義差分法進(jìn)行自相關(guān)的處理Dependent Variable: YMethod: Least

15、SquaresDate: 12/15/06 Time: 13:43Sample(adjusted): 1982 2003Included observations: 22 after adjusting endpointsConvergence achieved after 22 iterationsVariableCoefficientStd. Errort-StatisticProb. C-28788.979833.202-2.0.0104X14.0.9.0.0000X20.0.6.0.0000X3-0.0.-5.0.0001X50.0.0.0.9296AR(1)0.0.3.0.0028A

16、R(2)-0.0.-1.0.3190R-squared0. Mean dependent var43808.09Adjusted R-squared0. S.D. dependent var4410.156S.E. of regression617.7612 Akaike info criterion15.94345Sum squared resid. Schwarz criterion16.29060Log likelihood-168.3780 F-statistic175.8753Durbin-Watson stat2. Prob(F-statistic)0.Inverted AR Ro

17、ots .40 -.18i .40+.18i結(jié)果表明，調(diào)整后的模型的DW=2.5047>=1.78,廣義差分后的模型已不存在序列相關(guān)性，得到的回歸方程為：六 異方差性檢驗(yàn)采用懷特檢驗(yàn)法，輔助回歸模型的估計(jì)結(jié)果如下：White Heteroskedasticity Test:F-statistic2. Probability0.Obs*R-squared19.69010 Probability0.Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/15/06 Time: 14:08Sample: 198

18、0 2003Included observations: 24VariableCoefficientStd. Errort-StatisticProb. C4.25E+081.49E+090.0.7814X133760.9382018.780.0.6902X120.3.0.0.8234X1*X2-0.0.-1.0.3320X1*X30.0.0.0.7927X1*X51.2.0.0.7057X223241.7824004.530.0.3582X22-0.0.-1.0.1315X2*X3-0.0.-0.0.4391X2*X50.0.1.0.2654X38993.28513257.930.0.514

19、6X32-0.0.-0.0.3730X3*X50.0.0.0.8238X5-.965324.90-1.0.1103X520.0.0.0.5314R-squared0. Mean dependent var.Adjusted R-squared0. S.D. dependent var.S.E. of regression. Akaike info criterion32.31506Sum squared resid4.36E+13 Schwarz criterion33.05134Log likelihood-372.7807 F-statistic2.Durbin-Watson stat2.

20、 Prob(F-statistic)0.在同方差的條件下：n，h=4,為解釋變量的個(gè)數(shù)從上圖可知n19.6901，在顯著性水平0.05的情況下，9.49，由于n>9.49,故存在異方差性。克服異方差，采用加權(quán)最小二乘法（WLS）,以為權(quán)數(shù)進(jìn)行WLS估計(jì)，得估計(jì)結(jié)果如下：Dependent Variable: YMethod: Least SquaresDate: 12/15/06 Time: 14:22Sample: 1980 2003Included observations: 24Weighting series: 1/ABS(RESID)VariableCoefficientStd

21、. Errort-StatisticProb. C-38848.226162.635-6.0.0000X15.0.97.960400.0000X20.0.11.042280.0000X3-0.0.-13.158680.0000X50.0.9.0.0000Weighted StatisticsR-squared1. Mean dependent var41264.31Adjusted R-squared1. S.D. dependent var.7S.E. of regression37.90557 Akaike info criterion10.29112Sum squared resid27

22、299.81 Schwarz criterion10.53655Log likelihood-118.4935 F-statistic6319.212Durbin-Watson stat0. Prob(F-statistic)0.Unweighted StatisticsR-squared0. Mean dependent var42847.33Adjusted R-squared0. S.D. dependent var5325.186S.E. of regression1815.075 Sum squared residDurbin-Watson stat0.最終擬合的回歸方程為t (-6

23、.30) (97.96) (11.04) (-13.16) (9.93) =1.0000和初始方程比較，無(wú)論是擬合優(yōu)度還是個(gè)參數(shù)的t值都有顯著的改善。擬合結(jié)果可以由下圖形象的看出：七 模型的經(jīng)濟(jì)含義經(jīng)過(guò)以上分析，得出模型的回歸方程為1.0000表明，糧食總產(chǎn)量的變化可以完全由化肥施用量，糧食播種面積，成災(zāi)面積和農(nóng)業(yè)勞動(dòng)力的數(shù)值來(lái)解釋；的回歸參數(shù)5.63表示：在其他條件不變的情況下，化肥施用量每增加萬(wàn)噸，糧食產(chǎn)量增加5.63萬(wàn)噸；的回歸參數(shù)0.40表示：在其他條件不變的情況下，糧食播種面積每增加1000公頃，糧食產(chǎn)量增加4000噸；的回歸參數(shù)-0.27表示：在其他條件不變的情況下，成災(zāi)面積每減少

24、1000公頃，糧食產(chǎn)量增加2700噸；的回歸參數(shù)0.87表示：在其他條件不變的情況下，農(nóng)業(yè)勞動(dòng)力每增加萬(wàn)人，糧食產(chǎn)量增加8700噸；八 模型預(yù)測(cè)以此模型預(yù)測(cè)2004年的糧食產(chǎn)量，由統(tǒng)計(jì)年鑒的數(shù)據(jù)知，2004年各解釋變量的數(shù)值如下：=4636.6 =16297 =30596代入模型中得Y=49979.33而2004年實(shí)際糧食總產(chǎn)量為50146.03，誤差率為0.059%，Eviews模型如下：附：中國(guó)糧食生產(chǎn)與相關(guān)投入資料年份糧食產(chǎn)量（萬(wàn)噸）Y化肥施用量（萬(wàn)噸）糧食播種面積千公頃）成災(zāi)面積千公頃農(nóng)業(yè)機(jī)械總動(dòng)力萬(wàn)千瓦農(nóng)業(yè)勞動(dòng)力（萬(wàn)人）1980 32056.00 1269.400 .0 22317.

25、30 14746.00 29808.401981 32502.00 1334.900 .0 18743.30 15680.00 30677.601982 35450.00 1513.400 .0 16120.30 16614.00 31152.701983 38728.00 1659.800 .0 16209.30 18022.00 31645.101984 40731.00 1739.800 .0 15264.00 19497.00 31685.001985 37911.00 1775.800 .0 22705.30 20913.00 30351.501986 39151.00 1930.600 .0 23656.00 22950.00 30467.001987 40208.00 1999.300 .0 20392.70 24836.00 30870.001988 39408.00 2141.500 .0 23944.70 26575.00 31455.701989 40755.00 2