影响猪肉价格的因素分析

影响猪肉价格的因素分析1研究问题的背景在当前通货膨胀日益严重的情况下，各种物价飞涨，给人们的日常生活带来了极大地影响，生活中一些必需的物质也在涨，蔬菜肉的价格节节攀升，因此我觉得有必要研究一下究竟是什么因素在影响着这些必需品的价格，从而控制这些因素的上涨，使必需品的价格维持在一个比较稳定的水平上。2研究的主要内容这里我选取了对人们生活影响较大的猪肉的价格，从城镇居民收入，猪的供给量，饲料价格，替代品鸡蛋的价格，猪肉供给量五个方面来研究，看看它们相不相关，是正相关还是负相关，有多大的影响程度，从而调节这些变量使猪肉的价格比较稳定，对人们的生活产生较小的影响。关键词：猪肉价格3选取数据年份猪肉价格城镇居民收入饲料价格鸡蛋价格猪肉供给量199610.54838.91.57.83158199712.25160.31.566.23596.3199810.15425.11.495.53883.719997.558541.25.24005.6200010.10262801.47255.093966200110.656859.61.3945.34051.7200210.237702.81.5225.394123.1200310.748472.21.65.254238.6200413.769421.61.696.394341200513.19104931.85256.574555.3200612.1311759.51.8686.224650.5200718.8113785.52.137.764287.8200823.4915780.82.627.844620.54建立模型将以上数据导入eviews,就可以建立以下equation其中y代表猪肉价格，x1表示城镇居民收入，x2代表饲料价格，x3代表鸡蛋价格，x4表示猪肉的供给量.DependentVariable:YMethod:LeastSquaresDate:12/18/10Time:14:25Sample:19962008Includedobservations:13VariableCoefficientStd.Errort-StatisticProb.C9.15743513.677330.6695340.5220X10.0004980.0005410.9196120.3847X210.258233.1922473.2134820.0124X3-0.4803610.861324-0.5577010.5923X4-0.0036890.002947-1.2515900.2461R-squared0.946997Meandependentvar12.56938AdjustedR-squared0.920496S.D.dependentvar4.232883S.E.ofregression1.193522Akaikeinfocriterion3.475417Sumsquaredresid11.39596Schwarzcriterion3.692706Loglikelihood-17.59021F-statistic35.73401Durbin-Watsonstat2.510756Prob(F-statistic)0.000038表中除x2外，概率均大于0.05,说明其对y的影响不显著，必须对其进行修正，使其对y的影响显著。经修正的结果如下：DependentVariable:YMethod:LeastSquaresDate:12/18/10Time:13:42Sample(adjusted):19972008Includedobservations:12afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C26.351905.2969214.9749460.0016D(X1)0.0039440.0016512.3881830.0483LOG(X2)14.449493.6619583.9458360.0056D(X3)1.4456990.5840702.4752130.0425X4(-1)-0.0060370.001353-4.4634710.0029R-squared0.973597Meandependentvar12.74183AdjustedR-squared0.958510S.D.dependentvar4.373145S.E.ofregression0.890771Akaikeinfocriterion2.900878Sumsquaredresid5.554310Schwarzcriterion3.102922Loglikelihood-12.40527F-statistic64.53091Durbin-Watsonstat1.689968Prob(F-statistic)0.000013从表中可以看出，t检验的概率均小于0.05,此时的变量对y的影响是显著的，此模型才是可以用的。还可以看出F检验的值也较大，所以拒绝原假设，总体的显著性成立。可以得到下列模型：EstimationCommand:=====================LSYCD(X1)LOG(X2)D(X3)X4(-1)EstimationEquation:=====================Y=C(1)+C(2)*D(X1)+C(3)*LOG(X2)+C(4)*D(X3)+C(5)*X4(-1)SubstitutedCoefficients:=====================Y=26.35189582+0.003943975368*D(X1)+14.44948848*LOG(X2)+1.44569861*D(X3)-0.006037331562*X4(-1)5异方差检验（怀特检验）原假设HO:残差项不存在异方差备择假设H1：残差项存在异方差WhiteHeteroskedasticityTest:F-statistic0.975116Probability0.568784Obs*R-squared8.666952Probability0.371165TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:12/18/10Time:14:44Sample:19972008Includedobservations:12VariableCoefficientStd.Errort-StatisticProb.C9.59299477.996970.1229920.9099D(X1)0.0023160.0021471.0788210.3597(D(X1))^22.08E-078.06E-070.2584460.8128LOG(X2)1.6597335.6856470.2919160.7894(LOG(X2))^2-4.5993656.490699-0.7086090.5297D(X3)0.2440161.1780230.2071400.8492(D(X3))^2-0.6373930.997492-0.6389950.5683X4(-1)-0.0023800.037570-0.0633530.9535X4(-1)^2-5.31E-084.56E-06-0.0116440.9914R-squared0.722246Meandependentvar0.462859AdjustedR-squared-0.018431S.D.dependentvar0.471484S.E.ofregression0.475809Akaikeinfocriterion1.466105Sumsquaredresid0.679182Schwarzcriterion1.829785Loglikelihood0.203370F-statistic0.975116Durbin-Watsonstat2.734176Prob(F-statistic)0.568784从表中可以看出怀特检验的概率均大于0.05,所以接受原假设，说明残差项不存在异方差。6自相关检验（LM检验）Breusch-GodfreySerialCorrelationLMTest:F-statistic0.263214Probability0.778599Obs*R-squared1.143077Probability0.564656TestEquation:DependentVariable:RESIDMethod:LeastSquaresDate:12/18/10Time:14:47Presamplemissingvaluelaggedresidualssettozero.VariableCoefficientStd.Errort-StatisticProb.C-0.4681376.037956-0.0775320.9412D(X1)0.0002890.0019010.1522180.8850LOG(X2)-0.7251874.286532-0.1691780.8723D(X3)-0.2234790.730846-0.3057810.7721X4(-1)0.0001390.0015390.0906470.9313RESID(-1)0.1161500.5420630.2142740.8388RESID(-2)-0.4161950.575909-0.7226750.5023R-squared0.095256Meandependentvar-2.09E-15AdjustedR-squared-0.990436S.D.dependentvar0.710589S.E.ofregression1.002519Akaikeinfocriterion3.134108Sumsquaredresid5.025226Schwarzcriterion3.416970Loglikelihood-11.80465F-statistic0.087738Durbin-Watsonstat1.851760Prob(F-statistic)0.994924从表中可以看出，检验之后的概率均大于0.05,接受原假设，说明残差之间不存在二阶自相关，通过了LM检验。7正态分布检验0123456-1.5-1.0-0.50.00.51.01.5Series:ResidualsSample19972008Observations12Mean-2.09e-15Median-0.286222Maximum1.122528Minimum-1.185263Std.Dev.0.710589Skewness0.214591Kurtosis1.951146Jarque-Bera0.642146Probability0.725370从表中可以看出JB统计量的概率为0.725370,说明残差有百分之72.5370的概率是正态分布，大于0.05,通过了正态分布检验。8白噪声检验Date:12/18/10Time:15:00Sample:19972008Includedobservations:12AutocorrelationPartialCorrelationACPACQ-StatProb.|*.|.|*.|10.1450.1450.32050.571.**|.|.**|.|2-0.213-0.2391.08060.583.**|.|.*|.|3-0.243-0.1842.18230.535.|.|.|.|4-0.040-0.0272.21610.696.|*.|.|.|50.1200.0442.56210.767.|.|.*|.|60.000-0.0892.56210.861.|.|.|.|70.0000.0342.56210.922.|.|.|.|80.0000.0122.56210.959.|.|.|.|90.000-0.0102.56210.979.|.|.|.|100.0000.0002.5621