关于影响我国南方几省市农业总产值因素的实证分析

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　　关于影响我国南方几省市农业总产值因素的实证分析关于影响我国南方几省市农业总产值因素的实证分析问题简述：本文通过对我国南方几省市（包括上海、江苏、浙江、安徽、福建、江西、山东、湖北、湖南、广东、广西、海南、重庆、四川、贵州、云南）在2004年度农业总产值、农业劳动力、有效灌溉面积、农用化肥施用量、农村居民家庭生产性固定资产以及农业机械拥有量的统计数据的收集和整理，建立我国南方地区产出线性计量经济模型，并对模型中是否存在违反古典假设的情况（包括“多重共线性”、“异方差性”和“自相关性”）进行了多种方式的检验分析。然后对症下药，针对模型中所存在的问题选用适当的方法进行修正。最后应用产业经济学和区域经济学的相关知识对修正后的模型进行分析，解释其实际的经济含义，并对其反映出来的现实问题提出几点看法和建议。模型假设（古典假设）符号约定：―― 分别对应上海、江苏、浙江、安徽、福建、江西、山东、湖北、湖南、广东、广西、海南、重庆、四川、贵州、云南这16个省市在2004年度的农业总产值（单位：亿元）――分别对应16个省市在2004年度的农业劳动力（单位：万人）――分别对应16个省市在2004年度的有效灌溉面积（单位：万公顷）――分别对应16个省市在2004年度的农用化肥施用量（单位：万吨）――分别对应16个省市在2004年度的农村居民家庭生产性固定资产（单位：元）――分别对应16个省市在2004年度的农业机械拥有量（单位：万千瓦）――随机扰动项序列――残差序列解释变量是一组固定的值，即是非随机的。解释变量无测量误差。模型自身不存在设定误差。零均值假定，即在给定的的条件下，的条件期望值为零，即同方差假定，即对于每一个给定的，的条件方差都等于一个常数，即无自相关性假定，即不存在自相关，或中个项预测值互不影响，即 随机扰动项与解释变量不相关，即正态性假定，即假定服从均值为0，方差为的正态分布，表示为统计数据收集整理的统计数据如下地区农业总产值（亿元）农业劳动力（万人）有效灌溉面积(万公顷）农用化肥施用量单位：(万吨)农村居民家庭生产性固定资产（元）农业机械总动力（单位：万千瓦）上海98.271.74257.3115.871687.35112.61江苏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山东1599.32264.624760.79432.654733.428336.70湖北733.41110.712043.69270.322431.091661.75湖南671.71997.672675.34188.332191.082664.45广东851.71543.411315.93199.612166.261788.80广西500.81541.021516.67183.692305.271696.30海南152.7187.25177.2733.924289.36221.62重庆270.1813.19649.6971.602036.17695.67四川804.72413.992503.15208.393092.471891.06 贵州275.51322.10682.7174.922714.07761.99云南433.91690.221457.00129.224056.211542.91拟合使用的方法：最小距离法（用Eviee:19:07Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C242.6481186.09921.3038640.2215X10.0581570.0965900.6021040.5605X20.0797500.0793021.0056490.3383X30.0072420.3007750.0240760.9813X4-0.0417030.058281-0.7155370.4906X50.1219870.0538902.2636470.0471R-squared0.860155Meandependentvar585.6875AdjustedR-squared0.790233S.D.dependentvar368.9720S.E.ofregression168.9905Akaikeinfocriterion13.37756Sumsquaredresid285578.0Sche:19:45Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C2.6849722.2942341.1703130.2690 LNX1-0.0895820.196497-0.4558950.6582LNX20.0380250.2620960.1450790.8875LNX30.0596810.1351520.4415830.6682LNX4-0.1558380.284183-0.5483710.5955LNX50.6626690.3028392.1881910.0535R-squared0.911189Meandependentvar6.170439AdjustedR-squared0.866784S.D.dependentvar0.704874S.E.ofregression0.257270Akaikeinfocriterion0.402616Sumsquaredresid0.661879Sche:19:48Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C95.40032150.91780.6321340.5375X10.3803090.1043523.6444790.0027R-squared0.486845Meandependentvar585.6875AdjustedR-squared0.450192S.D.dependentvar368.9720S.E.ofregression273.5893Akaikeinfocriterion14.17760Sumsquaredresid1047916.Sche:19:52 Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C126.302979.749531.5837440.1356X20.2501510.0358196.9838280.0000R-squared0.776977Meandependentvar585.6875AdjustedR-squared0.761047S.D.dependentvar368.9720S.E.ofregression180.3639Akaikeinfocriterion13.34430Sumsquaredresid455436.0Sche:19:49Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C387.9315140.39822.7630800.0152X30.9434050.5287381.7842580.0961R-squared0.185269Meandependentvar585.6875AdjustedR-squared0.127073S.D.dependentvar368.9720S.E.ofregression344.7325Akaikeinfocriterion14.63988Sumsquaredresid1663767.Sche:19:50 Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C104.7661310.11510.3378300.7405X40.1603170.0991631.6166990.1282R-squared0.157323Meandependentvar585.6875AdjustedR-squared0.097132S.D.dependentvar368.9720S.E.ofregression350.5949Akaikeinfocriterion14.67361Sumsquaredresid1720835.Sche:19:50Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C239.787360.274603.9782480.0014X50.1720910.0219357.8456600.0000R-squared0.814703Meandependentvar585.6875AdjustedR-squared0.801467S.D.dependentvar368.9720S.E.ofregression164.4028Akaikeinfocriterion13.15898Sumsquaredresid378396.0Sche:19:58 Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C151.790389.060001.7043600.1121X50.1481240.0280985.2716270.0002X10.1056250.0803271.3149300.2113R-squared0.836455Meandependentvar585.6875AdjustedR-squared0.811294S.D.dependentvar368.9720S.E.ofregression160.2825Akaikeinfocriterion13.15911Sumsquaredresid333976.1Sche:19:58Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C170.628071.203472.3963430.0323X50.1078290.0447122.4116460.0314X20.1079950.0665521.6227070.1286R-squared0.845914Meandependentvar585.6875AdjustedR-squared0.822208S.D.dependentvar368.9720S.E.ofregression155.5785Akaikeinfo criterion13.09954Sumsquaredresid314660.8Sche:19:59Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C224.615473.256423.0661530.0090X50.1678640.0250706.6958590.0000X30.1129100.2881980.3917810.7016R-squared0.816865Meandependentvar585.6875AdjustedR-squared0.788691S.D.dependentvar368.9720S.E.ofregression169.6105Akaikeinfocriterion13.27225Sumsquaredresid373980.3Sche:20:00Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C382.9909150.12742.5511060.0241X50.1870790.0261847.1447680.0000X4-0.0577800.055509-1.0409160.3169R-squared0.828959Meandependentvar585.6875AdjustedR-squared0.802645S.D.dependent var368.9720S.E.ofregression163.9147Akaikeinfocriterion13.20393Sumsquaredresid349284.3Sche:20:16Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C130.305289.822581.4506960.1725X50.1062440.0455092.3345630.0378X20.0856560.0738001.1606410.2684X10.0655720.0864610.7583960.4628R-squared0.852961Meandependentvar585.6875AdjustedR-squared0.816201S.D.dependentvar368.9720S.E.ofregression158.1847Akaikeinfocriterion13.17772Sumsquaredresid300268.8Sche:20:16Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C162.225480.792572.0079240.0677X50.1062280.0468252.2686400.0425 X20.1062190.0694231.5300350.1519X30.0709950.2757570.2574550.8012R-squared0.846760Meandependentvar585.6875AdjustedR-squared0.808450S.D.dependentvar368.9720S.E.ofregression161.4859Akaikeinfocriterion13.21903Sumsquaredresid312932.3Sche:20:17Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C289.1764157.90951.8312800.0920X50.1249910.0495832.5208680.0269X20.0989920.0681441.4526790.1720X4-0.0455060.053946-0.8435440.4154R-squared0.854539Meandependentvar585.6875AdjustedR-squared0.818174S.D.dependentvar368.9720S.E.ofregression157.3337Akaikeinfocriterion13.16693Sumsquaredresid297046.7Sche:20:20Sample:116 Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C243.2397175.89071.3829030.1941X50.1221530.0509592.3971090.0354X20.0797030.0755911.0544010.3143X4-0.0417520.055536-0.7517930.4680X10.0587980.0885310.6641480.5203R-squared0.860147Meandependentvar585.6875AdjustedR-squared0.809291S.D.dependentvar368.9720S.E.ofregression161.1308Akaikeinfocriterion13.25262Sumsquaredresid285594.6Sche:20:21Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C280.5572169.97501.6505790.1271X50.1234330.0522532.3622400.0377X20.0977030.0713221.3698960.1980X4-0.0447940.056348-0.7949520.4435X30.0571480.2806260.2036430.8424 R-squared0.855085Meandependentvar585.6875AdjustedR-squared0.802389S.D.dependentvar368.9720S.E.ofregression164.0208Akaikeinfocriterion13.28817Sumsquaredresid295931.1Sche:20:59Sample:16Includedobservations:6VariableCoefficientStd.Errort-StatisticProb.C78.9739150.633621.5597130.1938X50.2959290.0660864.4779440.0110R-squared0.833694Meandependentvar274.5000AdjustedR-squared0.792117S.D.dependentvar137.7252S.E.ofregression62.79473Akaikeinfocriterion11.37882Sumsquaredresid15772.71Sche:21:01Sample:1116Includedobservations:6VariableCoefficientStd.Errort-StatisticProb.C344.0503150.85182.2807180.0847X50.1460040.0358774.0695510.0152 R-squared0.805459Meandependentvar867.3667AdjustedR-squared0.756824S.D.dependentvar391.7530S.E.ofregression193.1847Akaikeinfocriterion13.62637Sumsquaredresid149281.3Sche:21:21Sample:116Includedobservations:16eandependentvar428.0649AdjustedR-squared-0.171033S.D.dependentvar117.3273S.E.ofregression126.9649Akaikeinfocriterion12.64217Sumsquaredresid225681.2Schsquaredresid619830.6Durbin-ethod:LeastSquaresDate:05/17/05Time:21:27Sample:116Includedobservations:16VariableCoefficientStd.Errort-StatisticProb.C1.5197620.4068293.7356280.0022LNX50.6457900.05594911.542380.0000R-squared0.904908Meandependentvar6.170439 AdjustedR-squared0.898116S.D.dependentvar0.704874S.E.ofregression0.224990Akaikeinfocriterion-0.029051Sumsquaredresid0.708689SchberofRuns11Z.776Asymp.Sig.(2-tailed).438aMedian由于0.438>0.05，所以在显著性水平下，接受原假设，拒绝备择假设。即认为残差序列的排列是随即的。五、模型的解释与应用 2004年我国粮食生产实现恢复性增长，增产量创历史新高。但必须清醒地看到，我国农业综合生产能力特别是耕地的综合产出能力并没有实质性提高，农业持续稳定增产的基础并不牢固。所谓农业综合生产能力，是在一定地区、一定时期和一定经济技术条件下，由农业生产诸要素综合投入所形成的、可以相对稳定实现的农业综合产出水平。农业综合生产能力的大小，既取决于土地、生产资料、机械和人力投入的多少，也取决于农业科技水平的高低和农业抗灾能力的强弱。通过以上分析，我们最终得到的我国南方地区农业产出线性计量经济模型的最终形式为，从中不难发现：对我国南方地区农业产出起决定性影响的因素为（16个省市在2004年度的有效灌溉面积）和（16个省市在2004年度的农业机械拥有量），且二者之间存在着共线性。即对于我国南方各省来说有效耕地面积和农业机械投入量的多少对其农业产出起了决定性的作用。针对于此，我们提出以下两点建议：坚定不移地贯彻“十分珍惜、合理利用土地和严格保护耕地”的基本国策，实行最严格的土地管理制度，切实保护好耕地特别是保护好基本农田。实行严格的耕地占补平衡制度，不仅要做到数量上的占补平衡，而且要做到生产能力上的占补平衡。对耕地质量下降的趋势要给予高度重视，采取有效措施尽快加以遏制。增加和有效使用现代投入品，提高物质装备对农业发展的支撑能力。农业的现代物质装备水平，是农业现代化程度的重要标志之一，也是农业综合生产能力的决定性因素之一。化肥是地力的重要补充，农药是作物健康的重要保障，农机是人力和畜力的重要延伸。要促进农资工业加快发展，扩大生产，稳定价格，鼓励农民多投入。同时，要引导农民科学施肥、合理用药，提高使用效率，降低成本，减少污染。但是，从模型分析中来看，化肥施用量对产出的影响并不十分明显，可见我国农业在化肥生产与施用方面还有很大的发展空间，应当加强这方面的科技创新，使之切实转化为生产力。