影響豬肉價格的因素分析

1、 影響豬肉價格的因素分析1研究問題的背景在當前通貨膨脹日益嚴重的情況下，各種物價飛漲，給人們的日常生活帶來了極大地影響，生活中一些必需的物質(zhì)也在漲，蔬菜肉的價格節(jié)節(jié)攀升，因此我覺得有必要研究一下究竟是什么因素在影響著這些必需品的價格，從而控制這些因素的上漲，使必需品的價格維持在一個比較穩(wěn)定的水平上。2研究的主要內(nèi)容這里我選取了對人們生活影響較大的豬肉的價格，從城鎮(zhèn)居民收入，豬的供給量，飼料價格，替代品雞蛋的價格，豬肉供給量五個方面來研究，看看它們相不相關，是正相關還是負相關，有多大的影響程度，從而調(diào)節(jié)這些變量使豬肉的價格比較穩(wěn)定，對人們的生活產(chǎn)生較小的影響。關鍵詞：豬肉價格 3選取數(shù)據(jù) 年份豬

2、肉價格城鎮(zhèn)居民收入飼料價格雞蛋價格豬肉供給量199610.54838.91.57.83158199712.25160.31.566.23596.3199810.15425.11.495.53883.719997.558541.25.24005.6200010.10262801.47255.093966200110.656859.61.3945.34051.7200210.237702.81.5225.394123.1200310.748472.21.65.254238.6200413.769421.61.696.394341200513.19104931.85256.574555.320061

3、2.1311759.51.8686.224650.5200718.8113785.52.137.764287.8200823.4915780.82.627.844620.54建立模型將以上數(shù)據(jù)導入eviews,就可以建立以下equation其中y代表豬肉價格，x1表示城鎮(zhèn)居民收入，x2代表飼料價格，x3代表雞蛋價格，x4表示豬肉的供給量.dependent variable: ymethod: least squaresdate: 12/18/10 time: 14:25sample: 1996 2008included observations: 13variablecoefficients

4、td. 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-squared0.946997mean dependent var12.56938adjusted r-squared0.920496s.d. dependent var4.232883s.e. of regressio

5、n1.193522akaike info criterion3.475417sum squared resid11.39596schwarz criterion3.692706log likelihood-17.59021f-statistic35.73401durbin-watson stat2.510756prob(f-statistic)0.000038表中除x2外，概率均大于0.05,說明其對y的影響不顯著，必須對其進行修正，使其對y的影響顯著。經(jīng)修正的結果如下：dependent variable: ymethod: least squaresdate: 12/18/10 time:

6、 13:42sample (adjusted): 1997 2008included observations: 12 after adjustmentsvariablecoefficientstd. 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-

7、squared0.973597mean dependent var12.74183adjusted r-squared0.958510s.d. dependent var4.373145s.e. of regression0.890771akaike info criterion2.900878sum squared resid5.554310schwarz criterion3.102922log likelihood-12.40527f-statistic64.53091durbin-watson stat1.689968prob(f-statistic)0.000013從表中可以看出，t

8、檢驗的概率均小于0.05,此時的變量對y的影響是顯著的，此模型才是可以用的。還可以看出f檢驗的值也較大，所以拒絕原假設，總體的顯著性成立?？梢缘玫较铝心Ｐ停篹stimation command:=ls y c d(x1) log(x2) d(x3) x4(-1)estimation equation:=y = c(1) + c(2)*d(x1) + c(3)*log(x2) + c(4)*d(x3) + c(5)*x4(-1)substituted coefficients:=y = 26.35189582 + 0.003943975368*d(x1) + 14.44948848*log(x2

9、) + 1.44569861*d(x3) - 0.006037331562*x4(-1)5異方差檢驗（懷特檢驗）原假設ho:殘差項不存在異方差備擇假設h1：殘差項存在異方差white heteroskedasticity test:f-statistic0.975116probability0.568784obs*r-squared8.666952probability0.371165test equation:dependent variable: resid2method: least squaresdate: 12/18/10 time: 14:44sample: 1997 2008in

10、cluded observations: 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)2-0.637393

11、0.997492-0.6389950.5683x4(-1)-0.0023800.037570-0.0633530.9535x4(-1)2-5.31e-084.56e-06-0.0116440.9914r-squared0.722246mean dependent var0.462859adjusted r-squared-0.018431s.d. dependent var0.471484s.e. of regression0.475809akaike info criterion1.466105sum squared resid0.679182schwarz criterion1.82978

12、5log likelihood0.203370f-statistic0.975116durbin-watson stat2.734176prob(f-statistic)0.568784從表中可以看出懷特檢驗的概率均大于0.05,所以接受原假設，說明殘差項不存在異方差。6自相關檢驗（lm檢驗）breusch-godfrey serial correlation lm test:f-statistic0.263214probability0.778599obs*r-squared1.143077probability0.564656test equation:dependent variable

13、: residmethod: least squaresdate: 12/18/10 time: 14:47presample missing value lagged residuals set to zero.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(-

14、1)0.0001390.0015390.0906470.9313resid(-1)0.1161500.5420630.2142740.8388resid(-2)-0.4161950.575909-0.7226750.5023r-squared0.095256mean dependent var-2.09e-15adjusted r-squared-0.990436s.d. dependent var0.710589s.e. of regression1.002519akaike info criterion3.134108sum squared resid5.025226schwarz cri

15、terion3.416970log likelihood-11.80465f-statistic0.087738durbin-watson stat1.851760prob(f-statistic)0.994924從表中可以看出，檢驗之后的概率均大于0.05,接受原假設，說明殘差之間不存在二階自相關，通過了lm檢驗。7正態(tài)分布檢驗從表中可以看出jb統(tǒng)計量的概率為0.725370,說明殘差有百分之72.5370的概率是正態(tài)分布，大于0.05,通過了正態(tài)分布檢驗。8白噪聲檢驗date: 12/18/10 time: 15:00sample: 1997 2008included observatio

16、ns: 12autocorrelationpartial correlationacpacq-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. | . |. | . |

17、70.0000.0342.56210.922. | . |. | . |80.0000.0122.56210.959. | . |. | . |90.000-0.0102.56210.979. | . |. | . |100.0000.0002.56210.990從圖中可以看出，其自相關系數(shù)和偏自相關系數(shù)均落在二倍的標注差以內(nèi)，說明其波動性較小，且在幾階之后趨近于0，說明從長期來看，其是不相干的，屬于白噪聲。9偽回歸檢驗null hypothesis: e1 has a unit rootexogenous: nonelag length: 0 (automatic based on sic,

18、 maxlag=2)t-statisticprob.*augmented dickey-fuller test statistic-2.7565690.0107test critical values:1% level-2.7921545% level-1.97773810% level-1.602074*mackinnon (1996) one-sided p-values.warning: probabilities and critical values calculated for 20observations and may not be accurate for a sample

19、size of 11augmented dickey-fuller test equationdependent variable: d(e1)method: least squaresdate: 12/18/10 time: 15:11sample (adjusted): 1998 2008included observations: 11 after adjustmentsvariablecoefficientstd. errort-statisticprob.e1(-1)-0.9714940.352429-2.7565690.0202r-squared0.417541mean depen

20、dent var0.144408adjusted r-squared0.417541s.d. dependent var0.956933s.e. of regression0.730322akaike info criterion2.295845sum squared resid5.333700schwarz criterion2.332017log likelihood-11.62715durbin-watson stat1.779706從表中可以看出，其概率為0.0107小于0.05,所以不存在偽回歸，通過了檢驗。9模型平穩(wěn)性和預測性檢驗 從圖中可以看出，模型的穩(wěn)定性一直很好，始終在紅線的

21、范圍內(nèi)。從圖中可以看出，模型的預測能力較強，穩(wěn)定性也較強，符合我們所需要的模型。從圖中可以看出，該模型的一步預測能力較好，因為藍線一直在紅線內(nèi)，處在預測能力之內(nèi)。從圖中可以看出，其n步預測能力較好，藍線一直處在紅線之內(nèi)。圖中的紅線代表預測能力，藍線處在兩條紅線之內(nèi)則代表穩(wěn)定性較強，在預測期內(nèi)結構未發(fā)生改變，說明該模型的預測能力和穩(wěn)定性較好。10參數(shù)約束檢驗（1）約束條件：c(1)=0wald test:equation: equation2test statisticvaluedfprobabilityf-statistic24.75008(1, 7)0.0016chi-square24.75

22、00810.0000null hypothesis summary:normalized restriction (= 0)valuestd. err.c(1)26.351905.296921restrictions are linear in coefficients.從表中可以看出，其概率小于0.05，所以拒絕原假設，說明參數(shù)c(1)=0不成立。（2）約束條件：c(2)=0wald test:equation: equation2test statisticvaluedfprobabilityf-statistic5.703420(1, 7)0.0483chi-square5.703420

23、10.0169null hypothesis summary:normalized restriction (= 0)valuestd. err.c(2)0.0039440.001651restrictions are linear in coefficients.從表中可以看出，其概率小于0.05，所以拒絕原假設，說明參數(shù)c(2)=0不成立。（3）約束條件：c(3)=0wald test:equation: equation2test statisticvaluedfprobability0.f-statistic15.56962(1, 7)0.0056chi-square15.569621

24、0.0001null hypothesis summary:normalized restriction (= 0)valuestd. err.c(3)14.449493.661958restrictions are linear in coefficients.從表中可以看出，其概率小于0.05，所以拒絕原假設，說明參數(shù)c(3)=0不成立。（4）約束條件：c(4)=0wald test:equation: equation2test statisticvaluedfprobabilityf-statistic6.126678(1, 7)0.0425chi-square6.12667810.0

25、133null hypothesis summary:normalized restriction (= 0)valuestd. err.c(4)1.4456990.584070restrictions are linear in coefficients.從表中可以看出，其概率小于0.05,所以拒絕原假設，說明參數(shù)c(4)=0不成立。（5）約束條件：c(5)=0wald test:equation: equation2test statisticvaluedfprobabilityf-statistic19.92258(1, 7)0.0029chi-square19.9225810.0000

26、null hypothesis summary:normalized restriction (= 0)valuestd. err.c(5)-0.0060370.001353restrictions are linear in coefficients.從表中可以看出，其概率小于0.05,所以拒絕原假設，說明參數(shù)c(5)=0不成立。11殘差圖檢驗從圖中可以看出，回歸方程擬合的較好，殘差的波動性不大，模型較穩(wěn)定，預測能力比較強。12預測圖檢驗從圖中可以看出，藍線一直處在兩條紅線之內(nèi)，說明其預測的水平較好，一直處在預測能力之內(nèi)。13經(jīng)濟意義的檢驗estimation command:=ls y c d(x1) log(x2) d(x3) x4(-1)estimation equation:=y = c(1) + c(2)*d(x1) + c(3)*log(x2) + c(4)*d(x3) + c(5)*x4(-1)substituted coefficients:=y = 26.3518