我國公路客運(yùn)量的研究

1、我國公路客運(yùn)量的研究報(bào)告白一佳 陳華 師群昌 王一竹 張斯蕊 莊云摘要：本文通過建立模型對(duì)影響我國公路客運(yùn)量的因素進(jìn)行了研究，通過Evies對(duì)七個(gè)變量進(jìn)行回歸擬合，通過建立模型對(duì)樣本數(shù)據(jù)進(jìn)行回歸，分析得到最終模型，并在此基礎(chǔ)上細(xì)分變量優(yōu)化模型，引入虛擬變量對(duì)城市農(nóng)村的影響情況進(jìn)行對(duì)比分析，由此提出了最終模型的改進(jìn)模型，通過樣本回歸分析得出一定的結(jié)論，提出進(jìn)一步探討的問題。關(guān)鍵詞：公路客運(yùn)量 OLS回歸一背景綜述改革開放后，我國國民經(jīng)濟(jì)持續(xù)高速發(fā)展，公路運(yùn)輸需求強(qiáng)勁增長，國家加大了公路基礎(chǔ)設(shè)施的建設(shè)力度。隨著道路環(huán)境的改善和城鄉(xiāng)交流的日益頻繁，公路客運(yùn)量逐年提高。伴隨著中國城市化的進(jìn)程，城鄉(xiāng)之間

2、、城際之間的交流日益頻繁，這直接支持了公路客運(yùn)行業(yè)的發(fā)展。公路客運(yùn)在我國綜合運(yùn)輸體系客運(yùn)市場(chǎng)中發(fā)揮著舉足輕重的作用，承擔(dān)著90%以上的份額，因此對(duì)我國公路客運(yùn)的研究就顯得很有現(xiàn)實(shí)意義，通過研究我國從改革開放至今的公路客運(yùn)量發(fā)展變化，可以從我國國民經(jīng)濟(jì)發(fā)展的一個(gè)側(cè)面了解到我國二十多年來的交通運(yùn)輸、公共事業(yè)建設(shè)、人民生活水平、社會(huì)生產(chǎn)、流通、分配、消費(fèi)各環(huán)節(jié)協(xié)調(diào)發(fā)展等諸多現(xiàn)實(shí)經(jīng)濟(jì)問題，對(duì)于提升個(gè)人對(duì)國家經(jīng)濟(jì)發(fā)展認(rèn)識(shí)、研究分析的能力大有好處。因此，本文以1978年為課題研究的時(shí)間起點(diǎn)，縱觀中國公路、人口、人均收入、客運(yùn)汽車產(chǎn)量、鐵路、民航、水路運(yùn)輸客運(yùn)量等眾多因素對(duì)我國公路客運(yùn)量的推動(dòng)作用和影響，通

3、過建立多元線性回歸方程，進(jìn)行實(shí)證分析，得出對(duì)我國公路客運(yùn)量的顯著影響因素。二模型變量選擇及預(yù)測(cè)在模型建立之初，我們選擇了七個(gè)對(duì)公路客運(yùn)量可能造成影響的因素：客運(yùn)汽車總量、年底總?cè)丝?、鐵路客運(yùn)量、水運(yùn)客運(yùn)量、民用航空客運(yùn)量、公路長度及全國總?cè)司杖?。從?jīng)濟(jì)常識(shí)的角度，初步認(rèn)為，人口、人均收入作為國民經(jīng)濟(jì)衡量的基本要素對(duì)公路客運(yùn)量應(yīng)該有一定的影響；鐵路客運(yùn)、水運(yùn)客運(yùn)、民航客運(yùn)與公路客運(yùn)存在替代的經(jīng)濟(jì)關(guān)系，其三者的客運(yùn)量要么與公路客運(yùn)量有負(fù)相關(guān)的關(guān)系，要么與公路客運(yùn)量的相關(guān)關(guān)系不大；客運(yùn)汽車作為公路客運(yùn)的硬件條件我們也將其引入模型，去考察客運(yùn)汽車總量與客運(yùn)量規(guī)模間的解釋關(guān)系；而客運(yùn)路線的豐富程度勢(shì)必

4、也將對(duì)公路客運(yùn)量造成影響，在此我們用公路的長度去衡量客運(yùn)路線的豐富程度。在以上分析的基礎(chǔ)上，進(jìn)行主觀的預(yù)測(cè)，對(duì)公路客運(yùn)量可能造成影響的因素有：年底總?cè)丝?、全國總?cè)司杖搿㈣F路客運(yùn)量、客運(yùn)汽車總量。三模型分析根據(jù)對(duì)經(jīng)濟(jì)現(xiàn)象的分析，建立如下模型描述：其中：（一）、對(duì)所選擇的樣本作散點(diǎn)圖得個(gè)解釋變量與被解釋變量的關(guān)系如下系列圖所示：從圖形看出所選擇的解釋變量x3與x4樣本數(shù)據(jù)與所選擇的被解釋變量的樣本數(shù)據(jù)間沒有明顯的相關(guān)性，其余解釋變量與被解釋變量間有明顯的線性相關(guān)性。所以推測(cè)所建模型中x3和x4對(duì)y的解釋可能不顯著。（二）、樣本模型的估計(jì)1、模型估計(jì)對(duì)所選擇的樣本數(shù)據(jù)運(yùn)用OLS法回歸得：Depen

5、dent Variable: YMethod: Least SquaresDate: 12/16/05 Time: 15:08Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-1810996.156801.2-11.549640.0000X1-18.56917178.2442-0.1041780.9191X216.031731.7781879.0157720.0000X33.7978611.1424343.3243600.0077X4-2.6284404.549093-0.5

6、777940.5762X510.8877217.879220.6089590.5561X61357.762726.40071.8691640.0911X7349.150853.140406.5703460.0001R-squared0.998779 Mean dependent var941880.1Adjusted R-squared0.997924 S.D. dependent var413515.1S.E. of regression18842.03 Akaike info criterion22.82667Sum squared resid3.55E+09 Schwarz criter

7、ion23.22239Log likelihood-197.4400 F-statistic1168.282Durbin-Watson stat2.666635 Prob(F-statistic)0.000000即：從回歸的樣本模型的統(tǒng)計(jì)量R=0.998779可以看出，模型的擬合優(yōu)度非常好，從F=1168.282可知解釋變量對(duì)模型的整體解釋顯著，然而通過樣本數(shù)據(jù)所得的解釋變量x1、x4、x5參數(shù)估計(jì)值的t值明顯不顯著，據(jù)此推測(cè)模型解釋變量間可能存在多重共線性。2、多重共線性的檢驗(yàn)運(yùn)用相關(guān)系數(shù)矩陣檢驗(yàn)，相關(guān)系數(shù)矩陣為：X1X2X3X4X5X6X7X1 1.000000 0.882892 0.40

8、7131-0.702549 0.973972 0.960579 0.907679X2 0.882892 1.000000 0.504735-0.504676 0.920224 0.819337 0.924883X3 0.407131 0.504735 1.000000 0.276174 0.330393 0.359901 0.295472X4-0.702549-0.504676 0.276174 1.000000-0.751790-0.739402-0.722706X5 0.973972 0.920224 0.330393-0.751790 1.000000 0.933892 0.974145

9、X6 0.960579 0.819337 0.359901-0.739402 0.933892 1.000000 0.863272X7 0.907679 0.924883 0.295472-0.722706 0.974145 0.863272 1.000000從相關(guān)系數(shù)矩陣中可以看出，解釋變量x1與x2、x5、x6、x7，x2與x5、x6、x7，x5與x6、x7，x6與x7高度相關(guān)，說明模型存在多重共線性。3、多重共線性的消除運(yùn)用逐步回歸法消除多重共線性：第一步：Dependent Variable: YMethod: Least SquaresDate: 12/16/05 Time: 15:

10、25Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C224417.043625.735.1441430.0001X7759.698140.5134618.751750.0000R-squared0.956478 Mean dependent var941880.1Adjusted R-squared0.953758 S.D. dependent var413515.1S.E. of regression88922.47 Akaike info criterion25.7333

11、6Sum squared resid1.27E+11 Schwarz criterion25.83229Log likelihood-229.6002 F-statistic351.6280Durbin-Watson stat0.528434 Prob(F-statistic)0.000000第二步：X2 x7Dependent Variable: YMethod: Least SquaresDate: 12/16/05 Time: 15:27Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-Statisti

12、cProb. C-1905953.296654.0-6.4248360.0000X7406.146652.954207.6697710.0000X220.835102.8937677.1999910.0000R-squared0.990233 Mean dependent var941880.1Adjusted R-squared0.988931 S.D. dependent var413515.1S.E. of regression43506.46 Akaike info criterion24.35022Sum squared resid2.84E+10 Schwarz criterion

13、24.49861Log likelihood-216.1520 F-statistic760.3815Durbin-Watson stat0.787593 Prob(F-statistic)0.000000第三步： x2 x6 x7Dependent Variable: YMethod: Least SquaresDate: 12/16/05 Time: 15:29Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-1956629.196460.9-9.9593800.0000

14、X7328.316939.038748.4100300.0000X62111.153468.41224.5070420.0005X219.710071.92948810.215180.0000R-squared0.996015 Mean dependent var941880.1Adjusted R-squared0.995161 S.D. dependent var413515.1S.E. of regression28765.19 Akaike info criterion23.56485Sum squared resid1.16E+10 Schwarz criterion23.76271

15、Log likelihood-208.0836 F-statistic1166.384Durbin-Watson stat1.807779 Prob(F-statistic)0.000000第四步：通過加入剩余變量后剔除不顯著的變量后得：x2 x3 x6 x7Dependent Variable: YMethod: Least SquaresDate: 12/16/05 Time: 15:31Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-1877325.121383.9-

16、15.466010.0000X7393.156427.2833414.410130.0000X215.968811.40413211.372720.0000X61957.836288.53886.7853460.0000X33.2002030.6488084.9324360.0003R-squared0.998612 Mean dependent var941880.1Adjusted R-squared0.998185 S.D. dependent var413515.1S.E. of regression17616.05 Akaike info criterion22.62114Sum s

17、quared resid4.03E+09 Schwarz criterion22.86847Log likelihood-198.5903 F-statistic2338.575Durbin-Watson stat2.590139 Prob(F-statistic)0.000000但從回歸后所得的統(tǒng)計(jì)量看，加入x3后模型的整體擬合優(yōu)度改善并不明顯，說明x3對(duì)y的解釋能力不大；同時(shí)從經(jīng)濟(jì)意義上看，從我們先前的預(yù)測(cè)得鐵路的客運(yùn)量與公路客運(yùn)量間應(yīng)該存在負(fù)相關(guān)性，然而所估計(jì)的系數(shù)為正，與經(jīng)濟(jì)意義相違背。所以剔除x3，故最后的模型為：4、異方差檢驗(yàn)運(yùn)用arch檢驗(yàn)得：ARCH Test:F-statis

18、tic0.000226 Probability0.988210Obs*R-squared0.000256 Probability0.987238Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/20/05 Time: 20:07Sample(adjusted): 2 18Included observations: 17 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C6.00E+082.28E+082.631

19、7560.0189RESID2(-1)0.0038870.2586790.0150260.9882R-squared0.000015 Mean dependent var6.02E+08Adjusted R-squared-0.066651 S.D. dependent var6.62E+08S.E. of regression6.84E+08 Akaike info criterion43.63541Sum squared resid7.02E+18 Schwarz criterion43.73343Log likelihood-368.9009 F-statistic0.000226Dur

20、bin-Watson stat1.997109 Prob(F-statistic)0.988210根據(jù)F-statistic與Obs*R-squared的P值可得模型不存在異方差。5、自相關(guān)檢驗(yàn)由DW=1.807779，給定顯著性水平查表，n=18，k=3得下臨界值和上臨界值為，因?yàn)?-1.696>1.807779>1.696,所以模型不存在自相關(guān)性。6模型結(jié)論從所取樣本的估計(jì)模型得出：全國人均總收入每增加一元RMB，其他因素不變時(shí)，公路客運(yùn)總量平均提高萬人；全國總?cè)丝诿吭黾右蝗f人，其他因素不變時(shí)，公路客運(yùn)總量平均提高萬人；公路總長度每增加一萬公里，其他因素不變時(shí)，公路客運(yùn)總量平均

21、提高萬人。四模型改進(jìn)（一）、對(duì)所選擇的樣本作散點(diǎn)圖得分類后的解釋變量與被解釋變量的關(guān)系如下系列圖所示：考慮到全國人均收入與全國總?cè)丝诖嬖趨^(qū)域差異，即可把人口范圍細(xì)分為城鎮(zhèn)和農(nóng)村。因此，在上述模型的基礎(chǔ)上，我們進(jìn)一步考慮各細(xì)化因素的影響程度，以及農(nóng)村人口由于政策因素而呈現(xiàn)的二次型，建立如下模型： 其中： （二）、樣本模型的估計(jì)（1）對(duì)模型的估計(jì)模型估計(jì)選擇的樣本數(shù)據(jù)運(yùn)用OLS法回歸得：Dependent Variable: YMethod: Least SquaresDate: 12/24/05 Time: 23:23Sample: 1 10Included observations: 10Va

22、riableCoefficientStd. Errort-StatisticProb. C453987.7709790.90.6396080.5461X6-11946.309828.607-1.2154620.2698X2141.153768.5584004.8085810.0030X71121.882734.826153.4997470.0128R-squared0.995571 Mean dependent var641103.1Adjusted R-squared0.993356 S.D. dependent var290572.5S.E. of regression23684.97 A

23、kaike info criterion23.27224Sum squared resid3.37E+09 Schwarz criterion23.39328Log likelihood-112.3612 F-statistic449.5276Durbin-Watson stat2.133751 Prob(F-statistic)0.000000即：上述結(jié)果，雖然方程有相當(dāng)高的擬合優(yōu)度和F值，但解釋變量的t值并不顯著，且x6違背經(jīng)濟(jì)意義，由此推測(cè)模型的解釋變量間可能存在多重共線性。多重共線性的檢驗(yàn)：X21X71X6X2110.9681937631010.938423544908X710.968

24、19376310110.9467378499X60.9384235449080.94673784991從相關(guān)矩陣可以看出解釋變量間存在高度的相關(guān)。多重共線性的消除：運(yùn)用逐步回歸得到消除后的結(jié)果為：Dependent Variable: YMethod: Least SquaresDate: 12/24/05 Time: 23:25Sample: 1 10Included observations: 10VariableCoefficientStd. Errort-StatisticProb. C-406302.355062.37-7.3789480.0002X2131.185342.52828

25、612.334580.0000X7182.0646012.214326.7187190.0003R-squared0.994480 Mean dependent var641103.1Adjusted R-squared0.992903 S.D. dependent var290572.5S.E. of regression24479.23 Akaike info criterion23.29236Sum squared resid4.19E+09 Schwarz criterion23.38314Log likelihood-113.4618 F-statistic630.5537Durbi

26、n-Watson stat1.603479 Prob(F-statistic)0.000000由此得到方程：異方差檢驗(yàn)：Arch x21 x71ARCH Test:F-statistic0.090948 Probability0.771738Obs*R-squared0.115433 Probability0.734042Test Equation:Dependent Variable: RESID2Method: Least SquaresDate: 12/24/05 Time: 23:29Sample(adjusted): 2 10Included observations: 9 afte

27、r adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C5.08E+082.58E+081.9700260.0895RESID2(-1)-0.1155490.383151-0.3015750.7717R-squared0.012826 Mean dependent var4.54E+08Adjusted R-squared-0.128199 S.D. dependent var5.25E+08S.E. of regression5.58E+08 Akaike info criterion43.31014Sum sq

28、uared resid2.18E+18 Schwarz criterion43.35397Log likelihood-192.8956 F-statistic0.090948Durbin-Watson stat2.056760 Prob(F-statistic)0.771738可判斷模型不存在異方差。自相關(guān)檢驗(yàn)：在置信度為0.1的水平下，模型不存在自相關(guān)。5模型結(jié)論：從所取樣本的估計(jì)模型得出：城市人均總收入每增加一元RMB，其他因素不變時(shí)，公路客運(yùn)總量平均提高萬人；城市總?cè)丝诿吭黾右蝗f人，其他因素不變時(shí)，公路客運(yùn)總量平均提高萬人。（）.對(duì)模型的估計(jì)模型估計(jì)選擇的樣本數(shù)據(jù)運(yùn)用OLS法回歸得：D

29、ependent Variable: YMethod: Least SquaresDate: 12/25/05 Time: 22:09Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-5456506.1075174.-5.0749980.0003D05950910.3298356.1.8042050.0963D0*X22-68.0856238.50329-1.7683060.1024X2269.9251914.624444.7813940.0004X7277.7181728.

30、688032.7090800.0190X61245.9103307.8190.3766560.7130R-squared0.988681 Mean dependent var941880.1Adjusted R-squared0.983965 S.D. dependent var413515.1S.E. of regression52363.89 Akaike info criterion24.83102Sum squared resid3.29E+10 Schwarz criterion25.12781Log likelihood-217.4792 F-statistic209.6303Du

31、rbin-Watson stat1.527338 Prob(F-statistic)0.000000即：上述結(jié)果，雖然方程有較高的擬合優(yōu)度和F值，但個(gè)別解釋變量的t值并不顯著，由此推測(cè)模型的解釋變量間可能存在多重共線性。多重共線性的檢驗(yàn)D0D0*X22X22X72X6D010.998996948031-0.3716625096950.878283144760.790165566279D0*X220.9989969480311-0.3421454499550.8634536867410.764711913402X22-0.371662509695-0.3421454499551-0.322713

32、375095-0.502528541506X720.878283144760.863453686741-0.32271337509510.9467378499X60.7901655662790.764711913402-0.5025285415060.94673784991從相關(guān)矩陣看，個(gè)別變量間存在很高的相關(guān)性。多重共線性的消除：通過逐步回歸得到如下結(jié)果：第一步：Dependent Variable: YMethod: Least SquaresDate: 12/25/05 Time: 21:46Sample: 1 18Included observations: 18VariableCoe

33、fficientStd. Errort-StatisticProb. C352643.347262.157.4614320.0000X72153.445010.2779014.929600.0000R-squared0.933024 Mean dependent var941880.1Adjusted R-squared0.928838 S.D. dependent var413515.1S.E. of regression110309.8 Akaike info criterion26.16441Sum squared resid1.95E+11 Schwarz criterion26.26

34、334Log likelihood-233.4797 F-statistic222.8931Durbin-Watson stat0.434010 Prob(F-statistic)0.000000留x72第二步 ：Dependent Variable: YMethod: Least SquaresDate: 12/25/05 Time: 22:01Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-2375663.484871.8-4.8995700.0002X72164.99

35、756.35163825.977160.0000X2232.572785.7793835.6360300.0000R-squared0.978517 Mean dependent var941880.1Adjusted R-squared0.975653 S.D. dependent var413515.1S.E. of regression64522.96 Akaike info criterion25.13844Sum squared resid6.24E+10 Schwarz criterion25.28684Log likelihood-223.2460 F-statistic341.

36、6186Durbin-Watson stat0.952903 Prob(F-statistic)0.000000留x72 x22第三步 Dependent Variable: YMethod: Least SquaresDate: 12/25/05 Time: 22:03Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-2414088.505274.8-4.7777730.0003D030585.2067056.750.4561090.6553X72159.912012.91

37、94112.377650.0000X2233.111146.0544445.4688990.0001R-squared0.978832 Mean dependent var941880.1Adjusted R-squared0.974296 S.D. dependent var413515.1S.E. of regression66296.85 Akaike info criterion25.23480Sum squared resid6.15E+10 Schwarz criterion25.43266Log likelihood-223.1132 F-statistic215.7906Dur

38、bin-Watson stat0.944512 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 12/25/05 Time: 22:03Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-2396782.502043.1-4.7740550.0003X72160.913512.2894313.093650.0000X2232.886126.0029145.4783600.0001D

39、0*X220.3041500.7749330.3924850.7006R-squared0.978751 Mean dependent var941880.1Adjusted R-squared0.974198 S.D. dependent var413515.1S.E. of regression66423.18 Akaike info criterion25.23861Sum squared resid6.18E+10 Schwarz criterion25.43647Log likelihood-223.1475 F-statistic214.9529Durbin-Watson stat

40、0.943698 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 12/25/05 Time: 22:04Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-3333059.719412.6-4.6330290.0004X72130.323721.019186.2002290.0000X2240.494927.1232655.6848820.0001X63592.7752088.1

41、331.7205680.1073R-squared0.982267 Mean dependent var941880.1Adjusted R-squared0.978467 S.D. dependent var413515.1S.E. of regression60679.57 Akaike info criterion25.05773Sum squared resid5.15E+10 Schwarz criterion25.25559Log likelihood-221.5196 F-statistic258.4966Durbin-Watson stat1.077225 Prob(F-sta

42、tistic)0.000000第三步的回歸中雖然各個(gè)引入的變量t值均不顯著擔(dān)任然暫留x6，繼續(xù)回歸。第四步：Dependent Variable: YMethod: Least SquaresDate: 12/25/05 Time: 22:05Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-4054005.783040.4-5.1772620.0002X7289.7871730.057432.9871870.0105X2247.321317.6637196.1747180.

43、0000X65735.0912287.4492.5072000.0262D0119447.167233.411.7766040.0990R-squared0.985731 Mean dependent var941880.1Adjusted R-squared0.981341 S.D. dependent var413515.1S.E. of regression56485.28 Akaike info criterion24.95148Sum squared resid4.15E+10 Schwarz criterion25.19881Log likelihood-219.5633 F-st

44、atistic224.5224Durbin-Watson stat1.451476 Prob(F-statistic)0.000000Dependent Variable: YMethod: Least SquaresDate: 12/25/05 Time: 22:06Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-4012963.778026.2-5.1578770.0002X2246.745107.5680056.1766740.0000X7290.7511330.07

45、5563.0174380.0099X65787.2962324.8222.4893510.0271D0*X221.3698020.7881681.7379580.1058R-squared0.985610 Mean dependent var941880.1Adjusted R-squared0.981183 S.D. dependent var413515.1S.E. of regression56724.22 Akaike info criterion24.95992Sum squared resid4.18E+10 Schwarz criterion25.20725Log likelih

46、ood-219.6393 F-statistic222.6075Durbin-Watson stat1.441605 Prob(F-statistic)0.000000引入變量后發(fā)現(xiàn)他們都不顯著，但不能確定是新引入變量的影響還是后引入變量的影響，于是在進(jìn)一步回歸有：Dependent Variable: YMethod: Least SquaresDate: 12/25/05 Time: 22:14Sample: 1 18Included observations: 18VariableCoefficientStd. Errort-StatisticProb. C-5539369.101709

47、8.-5.4462480.0001D06896284.2068140.3.3345340.0054D0*X22-79.2160223.85477-3.3207620.0055X2272.3516912.688325.7022270.0001X7282.0059925.448743.2223990.0067R-squared0.988547 Mean dependent var941880.1Adjusted R-squared0.985023 S.D. dependent var413515.1S.E. of regression50606.11 Akaike info criterion24

48、.73167Sum squared resid3.33E+10 Schwarz criterion24.97899Log likelihood-217.5850 F-statistic280.5194Durbin-Watson stat1.524092 Prob(F-statistic)0.000000由此得到估計(jì)方程為：則：異方差檢驗(yàn)ARCH Test:F-statistic0.107898 Probability0.747090Obs*R-squared0.121411 Probability0.727509Test Equation:Dependent Variable: RESID2M

49、ethod: Least SquaresDate: 12/25/05 Time: 22:52Sample(adjusted): 2 18Included observations: 17 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C1.62E+098.72E+081.8608590.0825RESID2(-1)0.0848970.2584540.3284790.7471R-squared0.007142 Mean dependent var1.77E+09Adjusted R-squared-0.059049 S.D. dependent var2.99E+09S.E. of regression3.08E+09 Akaike info criterion46.64214Sum squared resid1.42E