影響人身保險(xiǎn)保費(fèi)收入的重要因素及其實(shí)證分析-2019年精選文檔

1、影響人身保險(xiǎn)保費(fèi)收入的重要因素及其實(shí)證分析一、摘要中國保險(xiǎn)業(yè)自 1979年恢復(fù)經(jīng)營以來，取得了迅猛的發(fā)展。其中， 1982 年中國恢 復(fù)了人身保險(xiǎn)業(yè)務(wù)，當(dāng)期人身保費(fèi)收入為 159萬元，而 2019年已增長為 10500.8832 億元。人身保險(xiǎn)收入在 2019 年市場份額超過財(cái)產(chǎn)險(xiǎn)以后，一直占據(jù)保險(xiǎn)市場的大 壁江山，并一直保持高速發(fā)展。人身保險(xiǎn)對于穩(wěn)定社會，提高人們的福利水平以 及促進(jìn)地區(qū)的經(jīng)濟(jì)發(fā)展，都起著重要作用。針對這一現(xiàn)象，根據(jù)影響人身保險(xiǎn)保 費(fèi)收入因素的觀點(diǎn)，收集了 19822009 年相關(guān)數(shù)據(jù)并加以實(shí)證分析。本文主要通 過我國國內(nèi)生產(chǎn)總值，居民可支配收入、物價(jià)指數(shù)、人口總數(shù)對我國人身

2、保險(xiǎn)的 保費(fèi)收入的影響進(jìn)行實(shí)證分析。通過建立理論模型，利用 Eviews 軟件對計(jì)量模型 進(jìn)行參數(shù)估計(jì)和檢驗(yàn)并加以修正，最后對所得結(jié)果做出經(jīng)濟(jì)意義的分析，以揭示 人身保險(xiǎn)保費(fèi)收入迅猛發(fā)展的重要因素，并從中總結(jié)經(jīng)驗(yàn)繼續(xù)開創(chuàng)輝煌業(yè)績。二、關(guān)鍵詞人身保費(fèi)收入 國內(nèi)生產(chǎn)總值 居民可支配收入 物價(jià)指數(shù) 人口總量 OLS 法三、模型設(shè)定研究影響人身保險(xiǎn)保費(fèi)收入的重要因素，需要考慮以下方面：（一）影響因素分析1. 國內(nèi)生產(chǎn)總值：我國保險(xiǎn)業(yè)的發(fā)展離不開國民經(jīng)濟(jì)的發(fā)展，經(jīng)濟(jì)發(fā)展帶來保 險(xiǎn)需求的增加，最近十幾年保險(xiǎn)業(yè)的高速發(fā)展主要得益于改革開放以來國民 經(jīng)濟(jì)的發(fā)展。 一般來說，人身保費(fèi)收入會隨著經(jīng)濟(jì)的增長而同步增

3、長。2. 居民可支配收入：可支配收入反映了人均消費(fèi)水平的高低。可支配收入越大， 用于購買消費(fèi)品的支出越多，而保險(xiǎn)是一種商品，收入增加會刺激保險(xiǎn)的需 求。3. 物價(jià)指數(shù)：物價(jià)指數(shù)在一定程度上反映我國商品價(jià)格的基本水平。而保險(xiǎn)商 品的價(jià)格是保險(xiǎn)費(fèi)率，保險(xiǎn)費(fèi)率與保險(xiǎn)需求一般成反比關(guān)系。物價(jià)指數(shù)偏高， 導(dǎo)致保險(xiǎn)經(jīng)營成本上升，一定程度上又影響了保險(xiǎn)費(fèi)率。因此，物價(jià)指數(shù)是 人身保險(xiǎn)商品價(jià)格的影響因素。4. 人口總量。 人身保險(xiǎn)是以人的身體和壽命為保險(xiǎn)標(biāo)的的， 而生命表是我國計(jì)算 人身保險(xiǎn)費(fèi)率的重要依據(jù)，中國是世界上人口最多的國家，人身保險(xiǎn)市場存 在著巨大的發(fā)展?jié)摿?，所以人口總量對人身保險(xiǎn)保費(fèi)收入也有影響。

4、因此，準(zhǔn)備將國內(nèi)生產(chǎn)總值，居民可支配收入，物價(jià)指數(shù)，人口總量作為模型的 解釋變量。（二）、模型形式設(shè)計(jì)經(jīng)分析，將模型設(shè)定為如下形式：其中，丫為人身保費(fèi)收入，X2為國內(nèi)生產(chǎn)總值，X3為居民可支配收入，X4為物價(jià) 指數(shù)，X5為人口總量。三、數(shù)據(jù)的收集 本文收集了 1982-2009年數(shù)據(jù)，如表1所示表1年份人身險(xiǎn)保費(fèi)收 入Y/億元國內(nèi)生產(chǎn)總值X2/ 億元居民可支配收入X3/元物價(jià)指數(shù)X4/人口總量X5/ 萬人19820.01595323.4526.6101.910165419830.10445962.7564101.510300819840.7257208.1651.2102.8104357198

5、54.419016.0739.1109.3105851198611.33610275.2899.6106.5107507198724.99312058.61002.2107.3109300198837.4815042.81181.4118.8111026198945.9516992.31373.9118112704199059.7718667.81510.2103.1114333199182.721781.51700.6103.41158231992141.9426923.52026.6106.41171711993193.3735333.92577.4114.71185171994147.3

6、948197.93496.2124.11198502019166.7660793.74283.0117.11211212019220.4371176.64838.9108.31223892019601.9678973.05160.3102.81236262019753.7884402.35425.199.21247612019885.0889677.15854.098.61257862000989.5699214.66280.0100.412674320191424.04109655.26859.6100.712762720192274.64120332.77702.899.212845320

7、193010.99135822.88472.2101.212922720193193.585921159878.39421.6103.912998820193646.227293184937.410493.0101.813075620194061.090122216314.411759.5101.513144820194948.968118265810.313785.8104.813212920197337.566735314045.415780.8105.913280220098144.182989340506.917174.799.3133474利用Eviews軟件，生成丫、X2、X3、X

8、4、X5等數(shù)據(jù)，采用這些數(shù)據(jù)對模型進(jìn) 行OLS回歸，結(jié)果如表2所示Depe ndent Variable: 丫Method: Least SquaresDate: 12/16/11Time: 10:11Sample: 1982 2009In eluded observati ons: 28VariableCoefficie ntStd. Errort-StatisticProb.C6054.5923165.2151.9128530.0683X20.0319750.0153632.0812780.0487X3-0.0803010.356451-0.2252800.8238X4-3.7061871

9、1.96429-0.3097710.7595X5-0.0553240.031920-1.7331920.0965R-squared0.977264Mean depe ndent var1514.609Adjusted R-squared0.973310S.D. dependent var2273.394S.E. of regressi on371.4090Akaike info criteri on14.83292Sum squared resid3172727.Schwarz criteri on15.07081Log likelihood-202.6608F-statistic247.14

10、97Durbi n-Watson stat0.827275Prob(F-statistic)0.000000由此可見，該模型 R2=0.977264, F檢驗(yàn)值247.1497,明顯顯著。但是當(dāng)a =0.05 時(shí)，10.025(28-5)=2.069,不僅X3、X4、X5的系數(shù)t檢驗(yàn)不顯著，而且 X3、X5系 數(shù)的符號與預(yù)期相反，這表明很可能存在嚴(yán)重的多重共線性。(一)多重共線性的檢驗(yàn)計(jì)算各解釋變量的相關(guān)系數(shù)，選擇 X2、X3、X4、X5數(shù)據(jù)，得相關(guān)系數(shù)矩陣如表3所示：表3X2X3X4X510.994744955-0.3428075920.8369897213875264720.9947449

11、551-0.3556809440.883156759387746248-0.34280759-0.3556809441-0.28843825425267463920.8369897210.883156759-0.2884382541472248392由相關(guān)系數(shù)矩陣可以看出，各解釋變量之間的相關(guān)系數(shù)較高，證實(shí)確實(shí)存在嚴(yán)重 多重共線性。采用逐步回歸的辦法，檢驗(yàn)和解決多重共線性問題。分別作 丫對X2、X3、X4、 X5的一元回歸，結(jié)果如表 4,5,6,7所示表4Depe ndent Variable: YMethod: Least SquaresDate: 12/16/11Time: 10:13Sa

12、mple: 1982 2009In cluded observati ons: 28VariableCoefficie ntStd. Errort-StatisticProb.C-608.3686129.7551-4.6885910.0001X20.0231810.00098823.459590.0000R-squared0.954889Mean depe ndent var1514.609Adjusted R-squared0.953154S.D. dependent var2273.394S.E. of regressi on492.0537Akaike info criteri on15

13、.30380Sum squared resid6295038.Schwarz criteri on15.39896Log likelihood-212.2532F-statistic550.3524Durbi n-Watson stat0.450283Prob(F-statistic)0.000000Depe ndent Variable: YMethod: Least SquaresDate: 12/13/11Time: 20:40Sample: 1982 2009In cluded observati ons: 28VariableCoefficie ntStd. Errort-Stati

14、sticProb.C-915.1152189.8201-4.8209590.0001X30.4489380.02632617.053080.0000R-squared0.917931Mean depe ndent var1514.609Adjusted R-squared0.914775S.D.dependent var2273.394S.E. of regressi on663.6801Akaike info criteri on15.90223Sum squared resid11452253Schwarz criteri on15.99738Log likelihood-220.6312

15、F-statistic290.8076Durb in-Watson stat0.283940Prob(F-statistic)0.000000Depe ndent Variable: YMethod: Least SquaresDate: 12/13/11Time: 20:43Sample: 1982 2009In cluded observati ons: 28VariableCoefficie ntStd. Errort-StatisticProb.C13635.596547.4292.0825860.0473X4-114.561161.76058-1.8549240.0750R-squa

16、red0.116870Mean depe ndent var1514.609Adjusted R-squared0.082904S.D. dependent var2273.394S.E. of regressi on2177.119Akaike info criteri on18.27814Sum squared resid1.23E+08Schwarz criteri on18.37330Log likelihood-253.8940F-statistic3.440742Durbi n-Watson stat0.156575Prob(F-statistic)0.074981Depe nde

17、nt Variable: YMethod: Least SquaresDate: 12/13/11Time: 20:43Sample: 1982 2009In cluded observati ons: 28VariableCoefficie ntStd. Errort-StatisticProb.C-18542.793627.690-5.1114580.0000X50.1670740.0301175.5475040.0000R-squared0.542050Mean depe ndent var1514.609Adjusted R-squared0.524437S.D. dependent

18、var2273.394S.E. of regressi on1567.757Akaike info criteri on17.62143Sum squared resid63904414Schwarz criteri on17.71659Log likelihood-244.7000F-statistic30.77480Durbi n-Watson stat0.128815Prob(F-statistic)0.000008其中，加入X2方程R2最大，以X2為基礎(chǔ)，順次加入其他變量逐步回歸，結(jié)果如 表8， 9,10所示表8Depe ndent Variable: YMethod: Least S

19、quaresDate: 12/13/11Time: 21:00Sample: 1982 2009In cluded observati ons: 28VariableCoefficie ntStd. Errort-StatisticProb.C-109.0568156.5631-0.6965680.4925X20.0546100.0075467.2372220.0000X3-0.6241000.149050-4.1871890.0003R-squared0.973484Mean depe ndent var1514.609Adjusted R-squared0.971363S.D. depen

20、dent var2273.394S.E. of regressi on384.7144Akaike info criteri on14.84384Sum squared resid3700129.Schwarz criteri on14.98657Log likelihood-204.8137F-statistic458.9181Durbi n-Watson stat0.795735Prob(F-statistic)0.000000Depe ndent Variable: YMethod: Least SquaresDate: 12/13/11Time: 21:00Sample: 1982 2

21、009In cluded observati ons: 28VariableCoefficie ntStd. Errort-StatisticProb.C-326.28541641.307-0.1987960.8440X20.0231180.00107221.563840.0000X4-2.61125215.14423-0.1724260.8645R-squared0.954942Mean depe ndent var1514.609Adjusted R-squared0.951338S.D. dependent var2273.394S.E. of regressi on501.5002Ak

22、aike info criteri on15.37404Sum squared resid6287561.Schwarz criteri on15.51678Log likelihood-212.2366F-statistic264.9220Durbi n-Watson stat0.448721Prob(F-statistic)0.000000表10Depe ndent Variable: YMethod: Least SquaresDate: 12/13/11Time: 21:00Sample: 1982 2009In cluded observati ons: 28VariableCoef

23、ficie ntStd. Errort-StatisticProb.C6324.3061407.6944.4926700.0001X20.0285950.00131021.820000.0000X5-0.0618780.012536-4.9359010.0000R-squared0.977153Mean depe ndent var1514.609Adjusted R-squared0.975326S.D. dependent var2273.394S.E. of regressi on357.1066Akaike info criteri on14.69490Sum squared resi

24、d3188128.Schwarz criteri on14.83764Log likelihood-202.7286F-statistic534.6260Durbi n-Watson stat0.823758Prob(F-statistic)0.000000經(jīng)比較，當(dāng)加入X3的方程可決系數(shù)增大，t檢驗(yàn)也顯著，但是參數(shù)為負(fù)值不合理。 當(dāng)加入X4的方程可決系數(shù)增大，但是t檢驗(yàn)不顯著。當(dāng)加入X5的方程可決系數(shù)增 大，t檢驗(yàn)也顯著，但是參數(shù)為負(fù)值不合理。所以最后的修正嚴(yán)重多重共線性的回 歸結(jié)果為（二）異方差的檢驗(yàn)由表4估計(jì)結(jié)果，進(jìn)入 Goldfeld-Quanadt檢驗(yàn)，經(jīng)Goldfeld-Quana

25、dt檢驗(yàn)結(jié)果如表 11,12所示：表11Depe ndent Variable: YMethod: Least SquaresDate: 12/14/11Time: 20:01Sample: 1982 1991In cluded observati ons: 10VariableCoefficie ntStd. Errort-StatisticProb.C-34.623864.439514-7.7990210.0001X20.0050170.00033215.102860.0000R-squared0.966116Mean depe ndent var26.74843Adjusted R-sq

26、uared0.961880S.D. dependent var28.95639S.E. of regressi on5.653548Akaike info criteri on6.479300Sum squared resid255.7008Schwarz criteri on6.539817Log likelihood-30.39650F-statistic228.0962Durbi n-Watson stat0.717899Prob(F-statistic)0.000000表12Depe ndent Variable: YMethod: Least SquaresDate: 12/14/1

27、1Time: 20:03Sample: 2000 2009In cluded observati ons: 10VariableCoefficie ntStd. Errort-StatisticProb.C-1261.981395.6476-3.1896580.0128X20.0265350.00187214.172810.0000R-squared0.961698Mean depe ndent var3903.085Adjusted R-squared0.956911S.D. dependent var2346.457S.E. of regressi on487.0765Akaike inf

28、o criteri on15.39158Sum squared resid1897948.Schwarz criteri on15.45209Log likelihood-74.95788F-statistic200.8687Durbi n-Watson stat1.212255Prob(F-statistic)0.000001由表11,12可得，F(xiàn)統(tǒng)計(jì)量為F=228.0962/200.8687=1.135548 ,在 a =0.05 下，查 F 分布表得 Fo.o5（8,8）=3.44, F=1.135548 F.05（8,8）=3.44,所以不能拒絕原假設(shè)，表明模型不存 在異方差。（三）自

29、相關(guān)的檢驗(yàn)由表 4可得，D-W=0.450283，在a =0.05, n=28下，查 DW 統(tǒng)計(jì)表可知，dL=1.328,du=1.476模型中DWdl，顯然模型中存在自相關(guān)。從殘差圖中也可以看 出，殘差項(xiàng)存在一階自相關(guān)。表13采用廣義差分法，補(bǔ)救自相關(guān)。利用EViews，得et滯后一期的自回歸，如表14所示，回歸方程為表14Depe ndent Variable: EMethod: Least SquaresDate: 12/14/11Time: 21:03Sample(adjusted): 1983 2009In cluded observati ons: 27 after adjusti

30、 ng en dpo intsVariableCoefficie ntStd. Errort-StatisticProb.E(-1)0.7902180.1338935.9018650.0000R-squared0.571978Mean depe ndent var-17.96234Adjusted R-squared0.571978S.D. dependent var482.4265S.E. of regressi on315.6197Akaike info criteri on14.38329Sum squared resid2590011.Schwarz criteri on14.4312

31、8Log likelihood-193.1744Durb in-Watson stat1.496516所以廣義差分方程為Yt-0.790218Yt-i = B i(1- 0.790218)+ B 2(Xt-0.790218X )+Vt對廣義差分方程進(jìn)行回歸，結(jié)果如表15所示：Depe ndent Variable: Y-0.790218*Y(-1)Method: Least SquaresDate: 12/14/11Time: 21:06Sample(adjusted): 1983 2009In cluded observati ons: 27 after adjusti ng en dpo

32、intsVariableCoefficie ntStd. Errort-StatisticProb.C-207.485184.16058-2.4653480.0209X2-0.790218*X2(-1)0.0261130.00201913.013710.0000R-squared0.871370Mean depe ndent var567.8636Adjusted R-squared0.866225S.D. dependent var844.4715S.E. of regressi on308.8674Akaike info criteri on14.37489Sum squared resid2384977.Schwarz criteri on14.47088Log likelihood-192.0610F-statistic169.3566Durbi n-Watson stat1.601198Prob(F-statistic)0.000000由于使用了廣義差分法數(shù)據(jù)，樣本容量減少了一個(gè)，為 27個(gè)。查5%顯著水平的 DV統(tǒng)計(jì)表可知，dL =1.316,