影响我国旅游收入因素的实证分析

1、影响我国旅游收入因素的实证分析一经济理论背景摘要：影响一国旅游收入的因素有很多，本文针对我国旅游收入影响因素建立了计量经济模型，并利用Eviews软件对收集到的数据进行相关回归以及多重共线性分析，建立了旅游收入影响因素的模型，分析了影响旅游收入主要因素及其影响程度，并提出了相关政策建议。关键字：旅游收入 旅游收入影响因素二指标选取（附数据表）本研究报告的数据来源于“中国统计年鉴”采集数据的区间为1998年2010年年份旅游收入（亿美元）国内旅游人数（亿人次）入境旅游人数（万人次）居民人均旅游花费（元/天）城镇居民家庭人均收入（元）农牧民家庭人均收入（元）1998120.746.446347.8

2、4345435319821999140.997.197279.563944770.520032000162.247.448344.39426.65129.120382001177.927.848901.29449.55535.919732002203.858.789790.83441.8605120862003174.068.79166.21395.77012.922682004257.3911.0210903.82427.58123.126062005292.9612.1212029.23436.19136.829892006339.4913.9412494.21446.9103583342

3、2007419.1916.113187.33482.61237839532008408.4317.1213002.745111443346562009396.7519.0212647.59535.415849.249382010458.1421.0313376.22598.219109.45919三 实验过程1.模型建立以国家旅游收入为被解释变量，国内旅游人数、入境旅游人数、居民人均旅游花费、城镇居民家庭人均收入、农牧民家庭人均收入作为解释变量建立线性回归模型：Yt=0+1X1t+2X2t +3X3t+4X4t+5 X5t+ ui其中，Yt 旅游收入 X1t 国内旅游人数 X2t入境旅游人数

4、X3t居民人均旅游花费X4t城镇居民家庭人均收入 X5t-农牧民家庭人均收入 0、1、2、3、4、5表示待定系数ui 表示随机误差项2、OLS回归模型Dependent Variable: YMethod: Least SquaresDate: 06/22/12 Time: 20:11Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C-226.023872.69947-3.1090160.0171X116.7330311.271381.4845580.1812X20.0275790.0

5、073183.7687440.0070X30.0212340.1554050.1366350.8952X4-0.0342030.012779-2.6764670.0317X50.1014000.0439702.3060890.0545R-squared0.992782Mean dependent var273.2423Adjusted R-squared0.987626S.D. dependent var119.3226S.E. of regression13.27328Akaike info criterion8.313420Sum squared resid1233.259Schwarz

6、criterion8.574166Log likelihood-48.03723Hannan-Quinn criter.8.259825F-statistic192.5546Durbin-Watson stat2.296421Prob(F-statistic)0.000000Yt=-226.0238+16.73303X1+0.027579X2+0.021234X3-0.034203X4+0.101400 X5t=（-3.109016）（1.484558）（3.768744）（0.136635）（-2.676467）（2.306089）R2=0.992782 R2=0.987626 F=192.

7、5546 DW=2.2964213 多重共线性及其修正从回归结果的系数以及t值我们可以看出模型可能存在多重共线性，下面我们计算出解释变量的相关系数。解释变量的相关系数矩阵如下： X1X2X3X4X5X110.911740.914090.13820.98568X20.9117410.8180.04020.7719X30.914090.81810.1480.9934X40.13820.04020.14810.3488X50.985680.77190.99340.34881由各相关系数值可知, 解释变量之间都高度相关，模型存在严重的多重共线性。多重共线性的修正采用逐步回归法，来检验并解决多重共线性问

8、题。分别作y对x1、x2、x3、x4、x5的一元回归Dependent Variable: YMethod: Least SquaresDate: 06/22/12 Time: 20:42Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C-12.1732219.46763-0.6253050.5445X123.672341.50273415.752840.0000R-squared0.957554Mean dependent var273.2423Adjusted R-squared0

9、.953695S.D. dependent var119.3226S.E. of regression25.67651Akaike info criterion9.469668Sum squared resid7252.113Schwarz criterion9.556583Log likelihood-59.55284Hannan-Quinn criter.9.451803F-statistic248.1520Durbin-Watson stat1.022698Prob(F-statistic)0.000000Dependent Variable: YMethod: Least Square

10、sDate: 06/22/12 Time: 20:44Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C-228.575945.38255-5.0366470.0004X20.0474550.00419211.320130.0000R-squared0.920946Mean dependent var273.2423Adjusted R-squared0.913759S.D. dependent var119.3226S.E. of regression35.04118Akaike i

11、nfo criterion10.09156Sum squared resid13506.73Schwarz criterion10.17848Log likelihood-63.59516Hannan-Quinn criter.10.07370F-statistic128.1454Durbin-Watson stat0.419824Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 06/22/12 Time: 20:44Sample: 1998 2010Included observations:

12、13CoefficientStd. Errort-StatisticProb.C-443.8537120.1817-3.6931870.0035X31.5826440.2626806.0249970.0001R-squared0.767445Mean dependent var273.2423Adjusted R-squared0.746303S.D. dependent var119.3226S.E. of regression60.10076Akaike info criterion11.17056Sum squared resid39733.11Schwarz criterion11.2

13、5748Log likelihood-70.60864Hannan-Quinn criter.11.15270F-statistic36.30058Durbin-Watson stat0.452934Prob(F-statistic)0.000086Dependent Variable: YMethod: Least SquaresDate: 06/22/12 Time: 20:45Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C47.3349523.651852.0013220.0

14、706X40.0240250.00226410.612820.0000R-squared0.911026Mean dependent var273.2423Adjusted R-squared0.902938S.D. dependent var119.3226S.E. of regression37.17473Akaike info criterion10.20977Sum squared resid15201.56Schwarz criterion10.29669Log likelihood-64.36353Hannan-Quinn criter.10.19191F-statistic112

15、.6320Durbin-Watson stat0.912764Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 06/22/12 Time: 20:45Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C9.09561330.710920.2961690.7726X50.0842610.0090689.2918420.0000R-squared0.886992Mean dependent va

16、r273.2423Adjusted R-squared0.876719S.D. dependent var119.3226S.E. of regression41.89587Akaike info criterion10.44889Sum squared resid19307.91Schwarz criterion10.53581Log likelihood-65.91778Hannan-Quinn criter.10.43102F-statistic86.33833Durbin-Watson stat0.732076Prob(F-statistic)0.000002比较可知X1的修正可决系数

17、最大，应该以X1为基础，顺次加入其他变量逐步回归Dependent Variable: YMethod: Least SquaresDate: 06/22/12 Time: 20:52Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C-114.934727.52498-4.1756520.0019X114.852112.3167816.4106660.0001X20.0197740.0047364.1755370.0019R-squared0.984529Mean dependent

18、var273.2423Adjusted R-squared0.981434S.D. dependent var119.3226S.E. of regression16.25844Akaike info criterion8.614276Sum squared resid2643.370Schwarz criterion8.744649Log likelihood-52.99279Hannan-Quinn criter.8.587478F-statistic318.1755Durbin-Watson stat1.415696Prob(F-statistic)0.000000Dependent V

19、ariable: YMethod: Least SquaresDate: 06/22/12 Time: 20:53Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C39.7493588.298660.4501690.6622X125.743763.7636976.8400200.0000X3-0.1697140.281070-0.6038150.5594R-squared0.959047Mean dependent var273.2423Adjusted R-squared0.9508

20、56S.D. dependent var119.3226S.E. of regression26.45186Akaike info criterion9.587704Sum squared resid6997.007Schwarz criterion9.718077Log likelihood-59.32008Hannan-Quinn criter.9.560906F-statistic117.0911Durbin-Watson stat1.188241Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate

21、: 06/22/12 Time: 20:53Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C-73.8196225.68246-2.8743200.0165X151.676439.5625805.4040260.0003X4-0.0293520.009950-2.9500110.0145R-squared0.977305Mean dependent var273.2423Adjusted R-squared0.972766S.D. dependent var119.3226S.E.

22、of regression19.69163Akaike info criterion8.997439Sum squared resid3877.603Schwarz criterion9.127812Log likelihood-55.48335Hannan-Quinn criter.8.970641F-statistic215.3094Durbin-Watson stat2.317272Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 06/22/12 Time: 20:54Sample: 199

23、8 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C-17.3852015.98576-1.0875430.3023X141.715247.1387595.8434860.0002X5-0.0677320.026402-2.5654450.0281R-squared0.974402Mean dependent var273.2423Adjusted R-squared0.969282S.D. dependent var119.3226S.E. of regression20.91319Akaike info

24、criterion9.117811Sum squared resid4373.615Schwarz criterion9.248184Log likelihood-56.26577Hannan-Quinn criter.9.091014F-statistic190.3241Durbin-Watson stat2.052792Prob(F-statistic)0.000000比较可得，当加入X2时方程的R2改进最大，而且个参数的t检验显著，因此选择保留X2，再继续加入其他新变量逐步回归。Dependent Variable: YMethod: Least SquaresDate: 06/22/1

25、2 Time: 21:03Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C-83.9768764.45099-1.3029570.2249X116.152133.4155694.7289720.0011X20.0195020.0049403.9475290.0034X3-0.0965660.180221-0.5358190.6051R-squared0.985007Mean dependent var273.2423Adjusted R-squared0.980009S.D. dep

26、endent var119.3226S.E. of regression16.87092Akaike info criterion8.736720Sum squared resid2561.652Schwarz criterion8.910551Log likelihood-52.78868Hannan-Quinn criter.8.700990F-statistic197.0910Durbin-Watson stat1.563059Prob(F-statistic)0.000000在加入x1、x2 基础上加入x 3，但是x 3的系数符号的经济意义不符合实际，所以去除x3Dependent V

27、ariable: YMethod: Least SquaresDate: 06/22/12 Time: 21:10Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C-119.125526.45923-4.5022280.0015X130.0639011.083252.7125520.0239X20.0150330.0056502.6608820.0260X4-0.0137270.009800-1.4007030.1948R-squared0.987298Mean dependent v

28、ar273.2423Adjusted R-squared0.983063S.D. dependent var119.3226S.E. of regression15.52869Akaike info criterion8.570915Sum squared resid2170.261Schwarz criterion8.744745Log likelihood-51.71095Hannan-Quinn criter.8.535185F-statistic233.1760Durbin-Watson stat2.078543Prob(F-statistic)0.000000在加入x1、x2 基础上

29、加入x 4，但是x 4的系数符号的经济意义不符合实际，所以去除x4Dependent Variable: YMethod: Least SquaresDate: 06/22/12 Time: 21:12Sample: 1998 2010Included observations: 13CoefficientStd. Errort-StatisticProb.C-126.976846.33626-2.7403340.0228X110.2663114.023370.7320850.4827X20.0222840.0090422.4644350.0359X50.0130120.0391910.332

30、0230.7475R-squared0.984716Mean dependent var273.2423Adjusted R-squared0.979621S.D. dependent var119.3226S.E. of regression17.03390Akaike info criterion8.755948Sum squared resid2611.383Schwarz criterion8.929778Log likelihood-52.91366Hannan-Quinn criter.8.720218F-statistic193.2804Durbin-Watson stat1.3

31、85382Prob(F-statistic)0.000000因为加入x5后X1、x2的t检验量变的不显著，所以去除x5，所以最优组合是x1、x2最后修正严重多重共线性影响后的回归结果为：Yt=-114.9347+14.85211X1+0.019774X2t=（-4.175652）（6.410666）（4.175537）R2=0.984529 R2=0.981434 F=318.1755 DW=1.4156964.异方差检验及其修正异方差检验1）.图像法从图上看，无法准确判断是否存在异方差，下面我们运用WHITE检验进一步检验模型的异方差是否存在。White检验Heteroskedasticit

32、y Test: WhiteF-statistic0.641701Prob. F(5,7)0.6768Obs*R-squared4.085866Prob. Chi-Square(5)0.5371Scaled explained SS2.965640Prob. Chi-Square(5)0.7053Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 06/22/12 Time: 21:42Sample: 1998 2010Included observations: 13CoefficientStd. Errort-

33、StatisticProb.C-381.06253848.275-0.0990220.9239X1-349.0453894.9700-0.3900080.7081X12-39.7948035.11834-1.1331630.2945X1*X20.1297690.1419920.9139200.3912X20.4864591.2933400.3761260.7180X22-9.83E-050.000138-0.7111940.5000R-squared0.314297Mean dependent var203.3361Adjusted R-squared-0.175490S.D. depende

34、nt var331.4905S.E. of regression359.4021Akaike info criterion14.91080Sum squared resid904189.2Schwarz criterion15.17154Log likelihood-90.92019Hannan-Quinn criter.14.85720F-statistic0.641701Durbin-Watson stat3.332954Prob(F-statistic)0.676836从上表看出，在=0.05的显著水平下，P值大于，所以模型不存在异方差5.自相关的检验及其修正因为散点图大致呈递增趋势，所以需进行进一步检验因为折线图没有连续为正或连续为负的现象，所以需进行SCLM检验：Breusch-Godfrey Serial Correlation LM Test:F-statistic0.630224Prob. F(1,9)0.4477Obs*R-squared0.850750Prob. Chi-Square(1)0.3563Test Equa