1、我國農民收入影響因素的回歸分析自改革開放以來 , 雖然中國經濟平均增長速度為9.5 % , 但二元經濟結構給經濟發展帶來的問題仍然很突出。農村人口占了中國總人口的 70 %多 , 農業產業結構不合理 , 經濟不發達 , 以及農民收入增長緩慢等問題勢必成為我國經濟持續穩定增長的障礙。正確有效地解決好“三農”問題是中國經濟走出困境 , 實現長期穩定增長的關鍵。其中 , 農民收入增長是核心 , 也是解決“三農”問題的關鍵。本文力圖應用適當的多元線性回歸模型 , 對有關農民收入的歷史數據和現狀進行分析 , 尋找其根源 , 探討影響農民收入的主要因素 , 并在此基礎上對如何增加農民收入提出相應的政策建議

2、。農民收入水平的度量， 通常采用人均純收入指標。 影響農民收入增長的因素是多方面的， 既有結構性矛盾因素， 又有體制性障礙因素。 但可以歸納為以下幾個方面：一是農產品收購價格水平。 目前農業收入仍是中西部地區農民收入的主要來源。二是農業剩余勞動力轉移水平。 中國的農業目前仍以農戶分散經營為主，農業比較效益低， 盡快地把農業剩余勞動力轉移出去是有效改善農民收入狀況的重要因素。三是城市化、工業化水平。中國多數地區城市化、工業化水平落后于世界平均水平， 這種狀況極大地影響了農民收入的增長。 四是農業產業結構狀況。農林牧漁業對農民收入增長貢獻率是不同的。隨著我國“入世”后農產品市場的開放和人民生活水平

3、的提高、 農產品需求市場的改變， 農業結構狀況直接影響著農民收入的增長。 五是農業投入水平。 農民收入與財政農業支出、 農村集體投入、農戶個人投入以及信貸投入都有顯著的正相關關系。 農業投入是農民收入增長的重要保證。 但考慮到農業投入主體的多元性， 既有國家、 集體和農戶的投入，又有銀行、企業和外資的投入，考慮到復雜性和可行性，所以對農業投入與農民收入，本文暫不作討論。因此，以全國為例，把農民收入與各影響因素關系進行線性回歸分析，并建立數學模型。一、計量經濟模型分析( 一) 、數據搜集根據以上分析，我們在影響農民收入因素中引入7 個解釋變量。即：x2 - 財政用于農業的支出的比重， x3 -

4、第二、三產業從業人數占全社會從業人數的比重，x4 - 非農村人口比重， x5- 鄉村從業人員占農村人口的比重，x6 - 農業總產值占農林牧總產值的比重，x7 - 農作物播種面積， x8 農村用電量。yx2x3x4x5x6x7x8年份78 年可比價比重%比重比重千公頃億千瓦時1986133.6013.4329.5017.9236.0179.99150104.07253.101987137.6312.2031.3019.3938.6275.63146379.53320.801988147.867.6637.6023.7145.9069.25143625.87508.901989196.769.42

5、39.9026.2149.2362.75146553.93790.501990220.539.9839.9026.4149.9364.66148362.27844.501991223.2510.2640.3026.9450.9263.09149585.80963.201992233.1910.0541.5027.4651.5361.51149007.101106.901993265.679.4943.6027.9951.8660.07147740.701244.901994335.169.2045.7028.5152.1258.22148240.601473.901995411.298.434

6、7.8029.0452.4158.43149879.301655.701996460.688.8249.5030.4853.2360.57152380.601812.701997477.968.3050.1031.9154.9358.23153969.201980.101998474.0210.6950.2033.3555.8458.03155705.702042.201999466.808.2349.9034.7857.1657.53156372.812173.452000466.167.7550.0036.2259.3355.68156299.852421.302001469.807.71

7、50.0037.6660.6255.24155707.862610.782002468.957.1750.0039.0962.0254.51154635.512993.402003476.247.1250.9040.5363.7250.08152414.963432.922004499.399.6753.1041.7665.6450.05153552.553933.032005521.207.2255.2042.9967.5949.72155487.734375.70資料來源中國統計年鑒2006 。(二 )、計量經濟學模型建立我們設定模型為下面所示的形式：Yt12 X 23 X 34 X 45

8、 X 56 X 67 X78 X 8ut利用Eviews 軟件進行最小二乘估計，估計結果如下表所示：Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:51Sample: 1986 2004Included observations: 19VariableCCoefficient-1102.373Std. Error375.8283t-Statistic-2.933184Prob.0.0136X2-6.6353933.781349-1.7547690.1071X318.229422.0666178.8208990.0

9、000X42.4300398.3703370.2903160.7770X5-16.237375.894109-2.7548470.0187X6-2.1552082.770834-0.7778190.4531X70.0099620.0023284.2788100.0013X80.0633890.0212762.9793480.0125R-squared0.995823Mean dependent var345.5232Adjusted R-squared0.993165S.D. dependent var139.7117S.E. of regression11.55028Akaike info

10、criterion8.026857Sum squared resid1467.498Schwarz criterion8.424516Log likelihood-68.25514F-statistic374.6600Durbin-Watson stat1.993270Prob(F-statistic)0.000000表 1 最小二乘估計結果回歸分析報告為：?-1102.373-6.6354X2 +18.2294X3 +2.4300X4 -16.2374X5 -2.1552X6 +0.0100X7 +0.0634X8YiSE375.833.78132.066618.370345.89412.7

11、7080.002330.02128t-2.9331.7558.820900.203162.7550.7784.278812.9793R20.995823R20.993165 Df19 DW1.99327 F374.66二、計量經濟學檢驗(一 )、多重共線性的檢驗及修正、檢驗多重共線性(a)、直觀法從“表 1 最小二乘估計結果”中可以看出，雖然模型的整體擬合的很好，但是 x4 x6 的 t 統計量并不顯著，所以可能存在多重共線性。(b)、相關系數矩陣X2X3X4X5X6X7X8X21.000000-0.717662-0.695257-0.7313260.737028-0.332435-0.594

12、699X3-0.7176621.0000000.9222860.935992-0.9457010.7422510.883804X4-0.6952570.9222861.0000000.986050-0.9377510.7539280.974675X5-0.7313260.9359920.9860501.000000-0.9747500.6874390.940436X60.737028-0.945701-0.937751-0.9747501.000000-0.603539-0.887428X7-0.3324350.7422510.7539280.687439-0.6035391.0000000.

13、742781X8-0.5946990.8838040.9746750.940436-0.8874280.7427811.000000表 2 相關系數矩陣從“表 2 相關系數矩陣”中可以看出，個個解釋變量之間的相關程度較高，所以應該存在多重共線性。、多重共線性的修正逐步迭代法A、一元回歸Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:52Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C82

14、0.3133151.87125.4013740.0000X2-51.3783616.18923-3.1736140.0056R-squared0.372041Mean dependent var345.5232Adjusted R-squared0.335102S.D. dependent var139.7117S.E. of regression113.9227Akaike info criterion12.40822Sum squared resid220632.4Schwarz criterion12.50763Log likelihood-115.8781F-statistic10.0

15、7183Durbin-Watson stat0.644400Prob(F-statistic)0.005554表 3y 對 x2的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:52Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-525.889164.11333-8.2024920.0000X319.460311.41604313.742740.0000R-squared0.9

16、17421Mean dependent var345.5232Adjusted R-squared0.912563S.D. dependent var139.7117S.E. of regression41.31236Akaike info criterion10.37950Sum squared resid29014.09Schwarz criterion10.47892Log likelihood-96.60526F-statistic188.8628Durbin-Watson stat0.598139Prob(F-statistic)0.000000表 4y 對 x3的回歸結果Depen

17、dent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:52Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-223.190569.92322-3.1919370.0053X418.650862.2422408.3179560.0000R-squared0.802758Mean dependent var345.5232Adjusted R-squared0.791155S.D. dependent va

18、r139.7117S.E. of regression63.84760Akaike info criterion11.25018Sum squared resid69300.77Schwarz criterion11.34959Log likelihood-104.8767F-statistic69.18839Durbin-Watson stat0.282182Prob(F-statistic)0.000000表 5y 對 x4的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:52Sample: 1986

19、 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-494.1440118.1449-4.1825260.0006X515.779782.1987117.1768320.0000R-squared0.751850Mean dependent var345.5232Adjusted R-squared0.737253S.D. dependent var139.7117S.E. of regression71.61463Akaike info criterion11.47978Sum square

20、d resid87187.14Schwarz criterion11.57919Log likelihood-107.0579F-statistic51.50691Durbin-Watson stat0.318959Prob(F-statistic)0.000002表 6y 對 x5的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:52Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb

21、.C1288.009143.80888.9563950.0000X6-15.523982.351180-6.6026350.0000R-squared0.719448Mean dependent var345.5232Adjusted R-squared0.702945S.D. dependent var139.7117S.E. of regression76.14674Akaike info criterion11.60250Sum squared resid98571.54Schwarz criterion11.70192Log likelihood-108.2238F-statistic

22、43.59479Durbin-Watson stat0.395893Prob(F-statistic)0.000004表 7y 對 x6的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:52Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-4417.766681.1678-6.4855770.0000X70.0315280.0045076.9949430.0000R-square

23、d0.742148Mean dependent var345.5232Adjusted R-squared0.726980S.D. dependent var139.7117S.E. of regression73.00119Akaike info criterion11.51813Sum squared resid90595.96Schwarz criterion11.61754Log likelihood-107.4222F-statistic48.92923Durbin-Watson stat0.572651Prob(F-statistic)0.000002表 8y 對 x7的回歸結果D

24、ependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:52Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C140.162528.966164.8388350.0002X80.1198270.0145438.2395030.0000R-squared0.799739Mean dependent var345.5232Adjusted R-squared0.787959S.D. dependent

25、var139.7117S.E. of regression64.33424Akaike info criterion11.26536Sum squared resid70361.21Schwarz criterion11.36478Log likelihood-105.0209F-statistic67.88941Durbin-Watson stat0.203711Prob(F-statistic)0.000000表 9y 對 x8的回歸結果綜合比較表 39 的回歸結果，發現加入 x3 的回歸結果最好。 以 x3 為基礎順次加入其他解釋變量，進行二元回歸，具體的回歸結果如下表1015 所示：D

26、ependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:53Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-754.4481149.1701-5.0576370.0001X321.788651.93268911.273750.0000X213.450708.0127451.6786630.1126R-squared0.929787Mean dependent var345.5232Adjuste

27、d R-squared0.921010S.D. dependent var139.7117S.E. of regression39.26619Akaike info criterion10.32254Sum squared resid24669.34Schwarz criterion10.47167Log likelihood-95.06417F-statistic105.9385Durbin-Watson stat0.595954Prob(F-statistic)0.000000表 10加入 x2 的回歸結果Dependent Variable: YMethod: Least Squares

28、Date: 06/08/07Time: 21:53Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-508.678175.73220-6.7168020.0000X317.882003.7521214.7658370.0002X41.7533513.8443050.4560900.6545R-squared0.918481Mean dependent var345.5232Adjusted R-squared0.908291S.D. dependent var139.

29、7117S.E. of regression42.30965Akaike info criterion10.47185Sum squared resid28641.71Schwarz criterion10.62097Log likelihood-96.48254F-statistic90.13613Durbin-Watson stat0.596359Prob(F-statistic)0.000000表 11加入 x4 的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:54Sample: 1986 200

30、4Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-498.155067.21844-7.4109860.0000X323.975163.9671836.0433700.0000X5-4.3205663.553466-1.2158740.2417R-squared0.924405Mean dependent var345.5232Adjusted R-squared0.914956S.D. dependent var139.7117S.E. of regression40.74312Akaike in

31、fo criterion10.39639Sum squared resid26560.02Schwarz criterion10.54551Log likelihood-95.76570F-statistic97.82772Durbin-Watson stat0.607882Prob(F-statistic)0.000000表 12加入 x5 的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:54Sample: 1986 2004Included observations: 19VariableCoeff

32、icientStd. Errort-StatisticProb.C-1600.965346.9265-4.6147090.0003X329.937683.5347538.4695280.0000X69.9801353.1841763.1342910.0064R-squared0.948835Mean dependent var345.5232Adjusted R-squared0.942440S.D. dependent var139.7117S.E. of regression33.51927Akaike info criterion10.00606Sum squared resid1797

33、6.66Schwarz criterion10.15518Log likelihood-92.05754F-statistic148.3576Durbin-Watson stat1.125188Prob(F-statistic)0.000000表 13加入 x6 的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:54Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-2153.02

34、8327.1248-6.5816730.0000X314.404971.35835510.604720.0000X70.0122680.0024475.0140150.0001R-squared0.967884Mean dependent var345.5232Adjusted R-squared0.963869S.D. dependent var139.7117S.E. of regression26.55648Akaike info criterion9.540364Sum squared resid11283.94Schwarz criterion9.689485Log likeliho

35、od-87.63345F-statistic241.0961Durbin-Watson stat0.690413Prob(F-statistic)0.000000表 14加入 x7 的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:54Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-400.5635103.0301-3.8878320.0013X315.542712.91635

36、85.3294930.0001X80.0292330.0192331.5199290.1480R-squared0.927840Mean dependent var345.5232Adjusted R-squared0.918820S.D. dependent var139.7117S.E. of regression39.80687Akaike info criterion10.34990Sum squared resid25353.40Schwarz criterion10.49902Log likelihood-95.32401F-statistic102.8643Durbin-Wats

37、on stat0.559772Prob(F-statistic)0.000000表 15加入 x8 的回歸結果綜合表 1015 所示，加入 x7 的模型的 R 最大 ,以 x3、 x7 為基礎順次加入其他解釋變量，進行三元回歸，具體回歸結果如下表1620 所示：Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:55Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-2133.921340.6

38、965-6.2634060.0000X314.960232.0946457.1421340.0000X70.0118430.0027864.2509080.0007X22.1952436.1704030.3557700.7270R-squared0.968153Mean dependent var345.5232Adjusted R-squared0.961783S.D. dependent var139.7117S.E. of regression27.31242Akaike info criterion9.637224Sum squared resid11189.52Schwarz cri

39、terion9.836053Log likelihood-87.55363F-statistic151.9988Durbin-Watson stat0.712258Prob(F-statistic)0.000000表 16加入 x2 的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:55Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-2226.420353.4425-6.299

40、2430.0000X315.667292.4431136.4128390.0000X70.0127030.0025894.9063730.0002X4-1.6013622.553294-0.6271750.5400R-squared0.968705Mean dependent var345.5232Adjusted R-squared0.962445S.D. dependent var139.7117S.E. of regression27.07472Akaike info criterion9.619741Sum squared resid10995.60Schwarz criterion9

41、.818571Log likelihood-87.38754F-statistic154.7677Durbin-Watson stat0.704178Prob(F-statistic)0.000000表 17加入 x4 的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:55Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-2110.381306.2690-6.8906130.00

42、00X318.601562.6173817.1069370.0000X70.0121390.0022855.3116650.0001X5-3.9648782.163262-1.8328230.0868R-squared0.973760Mean dependent var345.5232Adjusted R-squared0.968512S.D. dependent var139.7117S.E. of regression24.79152Akaike info criterion9.443544Sum squared resid9219.289Schwarz criterion9.642373

43、Log likelihood-85.71367F-statistic185.5507Durbin-Watson stat0.733972Prob(F-statistic)0.000000表 18加入 x5 的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:55Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-2418.859323.7240-7.4719790.0000X320.

44、998873.3971206.1813740.0000X70.0099200.0024953.9766600.0012X65.3591842.5719502.0837050.0547R-squared0.975093Mean dependent var345.5232Adjusted R-squared0.970112S.D. dependent var139.7117S.E. of regression24.15359Akaike info criterion9.391407Sum squared resid8750.940Schwarz criterion9.590236Log likel

45、ihood-85.21837F-statistic195.7489Durbin-Watson stat1.084023Prob(F-statistic)0.000000表 19加入 x6 的回歸結果Dependent Variable: YMethod: Least SquaresDate: 06/08/07Time: 21:56Sample: 1986 2004Included observations: 19VariableCoefficientStd. Errort-StatisticProb.C-2013.355361.8657-5.5638180.0001X313.015782.03

46、24206.4040780.0000X70.0116150.0025584.5403220.0004X80.0123750.0134160.9224010.3709R-squared0.969608Mean dependent var345.5232Adjusted R-squared0.963529S.D. dependent var139.7117S.E. of regression26.68115Akaike info criterion9.590455Sum squared resid10678.26Schwarz criterion9.789285Log likelihood-87.10933F-statistic159.5158Durbin-Watson stat0.672264Prob(F-statis