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1、影響人身保險保費收入的重要因素及其實證分析一、摘要中國保險業(yè)自 1979年恢復經(jīng)營以來,取得了迅猛的發(fā)展。其中, 1982 年中國恢 復了人身保險業(yè)務,當期人身保費收入為 159萬元,而 2019年已增長為 10500.8832 億元。人身保險收入在 2019 年市場份額超過財產險以后,一直占據(jù)保險市場的大 壁江山,并一直保持高速發(fā)展。人身保險對于穩(wěn)定社會,提高人們的福利水平以 及促進地區(qū)的經(jīng)濟發(fā)展,都起著重要作用。針對這一現(xiàn)象,根據(jù)影響人身保險保 費收入因素的觀點,收集了 19822009 年相關數(shù)據(jù)并加以實證分析。本文主要通 過我國國內生產總值,居民可支配收入、物價指數(shù)、人口總數(shù)對我國人身

2、保險的 保費收入的影響進行實證分析。通過建立理論模型,利用 Eviews 軟件對計量模型 進行參數(shù)估計和檢驗并加以修正,最后對所得結果做出經(jīng)濟意義的分析,以揭示 人身保險保費收入迅猛發(fā)展的重要因素,并從中總結經(jīng)驗繼續(xù)開創(chuàng)輝煌業(yè)績。二、關鍵詞人身保費收入 國內生產總值 居民可支配收入 物價指數(shù) 人口總量 OLS 法三、模型設定研究影響人身保險保費收入的重要因素,需要考慮以下方面:(一)影響因素分析1. 國內生產總值:我國保險業(yè)的發(fā)展離不開國民經(jīng)濟的發(fā)展,經(jīng)濟發(fā)展帶來保 險需求的增加,最近十幾年保險業(yè)的高速發(fā)展主要得益于改革開放以來國民 經(jīng)濟的發(fā)展。 一般來說,人身保費收入會隨著經(jīng)濟的增長而同步增

3、長。2. 居民可支配收入:可支配收入反映了人均消費水平的高低??芍涫杖朐酱?, 用于購買消費品的支出越多,而保險是一種商品,收入增加會刺激保險的需 求。3. 物價指數(shù):物價指數(shù)在一定程度上反映我國商品價格的基本水平。而保險商 品的價格是保險費率,保險費率與保險需求一般成反比關系。物價指數(shù)偏高, 導致保險經(jīng)營成本上升,一定程度上又影響了保險費率。因此,物價指數(shù)是 人身保險商品價格的影響因素。4. 人口總量。 人身保險是以人的身體和壽命為保險標的的, 而生命表是我國計算 人身保險費率的重要依據(jù),中國是世界上人口最多的國家,人身保險市場存 在著巨大的發(fā)展?jié)摿?,所以人口總量對人身保險保費收入也有影響。

4、因此,準備將國內生產總值,居民可支配收入,物價指數(shù),人口總量作為模型的 解釋變量。(二)、模型形式設計經(jīng)分析,將模型設定為如下形式:其中,丫為人身保費收入,X2為國內生產總值,X3為居民可支配收入,X4為物價 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年份人身險保費收 入Y/億元國內生產總值X2/ 億元居民可支配收入X3/元物價指數(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 行OLS回歸,結果如表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檢驗值247.1497,明顯顯著。但是當a =0.05 時,10.025(28-5)=2.069,不僅X3、X4、X5的系數(shù)t檢驗不顯著,而且 X3、X5系 數(shù)的符號與預期相反,這表明很可能存在嚴重的多重共線性。(一)多重共線性的檢驗計算各解釋變量的相關系數(shù),選擇 X2、X3、X4、X5數(shù)據(jù),得相關系數(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由相關系數(shù)矩陣可以看出,各解釋變量之間的相關系數(shù)較高,證實確實存在嚴重 多重共線性。采用逐步回歸的辦法,檢驗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,順次加入其他變量逐步回歸,結果如 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,當加入X3的方程可決系數(shù)增大,t檢驗也顯著,但是參數(shù)為負值不合理。 當加入X4的方程可決系數(shù)增大,但是t檢驗不顯著。當加入X5的方程可決系數(shù)增 大,t檢驗也顯著,但是參數(shù)為負值不合理。所以最后的修正嚴重多重共線性的回 歸結果為(二)異方差的檢驗由表4估計結果,進入 Goldfeld-Quanadt檢驗,經(jīng)Goldfeld-Quana

25、dt檢驗結果如表 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,表明模型不存 在異方差。(三)自

29、相關的檢驗由表 4可得,D-W=0.450283,在a =0.05, n=28下,查 DW 統(tǒng)計表可知,dL=1.328,du=1.476模型中DWdl,顯然模型中存在自相關。從殘差圖中也可以看 出,殘差項存在一階自相關。表13采用廣義差分法,補救自相關。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對廣義差分方程進行回歸,結果如表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,為 27個。查5%顯著水平的 DV統(tǒng)計表可知,dL =1.316,

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