影响GDP增长的经济因素分析.doc

影响gdp增长的经济因素分析 近年来，我国gdp逐年增长，经济发展速度令人瞩目。为更好的了解我国经济增长的原因，我组对影响我国gdp增长的经济因素进行了分析。下表（表1.1）提供了我国19782002年的gdp及其主要影响因素的数据。其中y=gdp(亿元)；x1能源消费总量（万吨标准煤）；x2就业人员（万人）；x3=居民消费水平（元）；x4农业总产值（亿元）；x5社会消费品零售总额（亿元）；x6进出口贸易总额（亿元）obsx1x2x3x4x5x6y1978571444015218413971558.63553624.1197958588410241971697.61800454.64038.2198060275423612361922.621405704517.8198159447437252492180.622350735.34862.4198262067452952662483.262570771.35294.71983660404643628927502849.4860.15934.5198470904481973273214.133376.412017171198576682498734373619.4943052066.78964.4198680850512824474013.0149502850.410202.2198786632527835084675.758203084.211962.5198892997543346355865.277440382214928.3198996934553297626534.738101.44155.916909.2199098703567408037662.098300.15560.118547.91991103783583608968157.039415.67229.321617.819921091705943210709084.710993.79119.626638.1199311599360220133110995.512462.11127134634.4199412273761470174615750.516264.720381.946759.4199513117662388223620340.92062023499.958478.1199613894868850264122353.724774.124133.867884.6199713817369600283423788.427298.926967.274462.6199813221470637297224541.929152.526849.778345.2199913011971394313824519.131134.729896.282067.5200013029772085339732917.9334152.639273.289468.1200113491473025360937213.4937595.242183.697314.8200214822273740379143499.9140910.551378.2104790.6一：现估计模型为y=a0+a1*x1+a2*x2+a3*x3+a4*x4+a5*x5+a6*x6+u 运用ols估计方法对上式中得参数进行估计，利用eviews软件得回归分析结果如下：（表1.2）dependent variable: ymethod: least squaresdate: 06/03/05 time: 20:44sample: 1978 2002included observations: 25variablecoefficientstd. errort-statisticprob. c5421.2153244.8561.6707100.1121x10.0509330.0302151.6857080.1091x2-0.2244280.113375-1.9795160.0633x321.183872.08494110.160420.0000x4-0.2165350.203877-1.0620840.3022x50.4884900.3118031.5666650.1346x60.3366200.1392082.4181020.0264r-squared0.999725 mean dependent var35976.74adjusted r-squared0.999634 s.d. dependent var34444.88s.e. of regression658.9930 akaike info criterion16.05080sum squared resid7816893. schwarz criterion16.39208log likelihood-193.6350 f-statistic10925.17durbin-watson stat1.748019 prob(f-statistic)0.000000分析回归结果：从经济意义上讲，就业人口x2的系数为负，可初步认为国民经济在向技术密集型、资本密集型发展。农业总产值的系数为负，不符合实际经济意义。其余解释变量的系数为正，符合实际经济现象。从模型检验上讲，拟合较好。可决系数r（2）=0.999725,f统计量为10925.172.66=f0.05(6,18)表明模型在整体上拟合非常好；系数显著性检验：对于a0,t统计量为1.670710，给定0.05 查t分布表，在自由度为n-k=18下，得临界值t0.025(18)=2.101因为tt0.025(18),所以接受h0:a0=0的原假设。对于a1、a2、a3、a4、a5、a6,除去a3、a6的t统计量大于t0.025(18)之外，其余系数的t统计量均小于t0.025(18) ，因此可初步认为模型存在严重的多重共线性。现重新估计模型为y=a1x1+a2x2+a3x3+a4x4+a5x5+a6x6得回归结果如下（表1.3）：dependent variable: ymethod: least squaresdate: 06/03/05 time: 20:57sample: 1978 2002included observations: 25variablecoefficientstd. errort-statisticprob. x10.0106560.0190530.5592690.5825x2-0.0412480.030184-1.3665640.1877x322.689741.96667011.537140.0000x4-0.1400060.207819-0.6736920.5086x50.1460230.2457790.5941240.5594x60.3871080.1421502.7232410.0135r-squared0.999683 mean dependent var35976.74adjusted r-squared0.999599 s.d. dependent var34444.88s.e. of regression689.3576 akaike info criterion16.11496sum squared resid9029064. schwarz criterion16.40749log likelihood-195.4370 durbin-watson stat1.704338从模型检验上看，r(2)=0.999683小于第一次模型的可决系数;t检验也并不优于第一次模型的t检验，故仍采用第一次模型。二、多重共线性检验 1、检验：利用eviews计算线性回归模型中，六个解释变量的如下简单相关系数矩阵(表2.1.1)：x1x2x3x4x5x6x110.9784543274310.920854078840.8881671741190.9111188771040.883472715462x20.97845432743110.9488157613280.9171258679460.9460360865940.909232951166x30.920854078840.94881576132810.9827864539390.9970082872950.982361794167x40.8881671741190.9171258679460.98278645393910.9907093117320.997396156937x50.9111188771040.9460360865940.9970082872950.99070931173210.98844222336x60.8834727154620.9092329511660.9823617941670.9973961569370.988442223361从上表可以看出，各解释变量之间存在高度线性相关。同时由表1.2又可看出，尽管整体上线性回归拟合较好，但x1 x2 x4 x5 变量的参数t值并不显著，表明模型中解释变量确实存在严重的多重共线性。2、修正：运用ols方法逐一求出y对各个解释变量的回归，分别如下：y=-67070.34+1.029232x1 （式2.1.1） (9781.140) (0.093575)t=(-6.856618) (10.99902) r(2)=0.840254 se=14063.12 f=120.9784y=-133299.7+2.962005x2 （式2.1.2） (12588.50) (0.212476)t=(-10.58901) (13.68286)r(2)=0.890591 se=11638.38 f=187.2206y=-2268.943+27.31756x3 （式2.1.3） (348.7497) (0.186822)t=(-6.505936) (146.2225)r(2)=0.998925 se=1153.406 f=21381.06y=617.7713+2.752282x4 （式2.1.4） (1669.79)(0.094620)t=(0.369969) (29.08787)r(2)=0.973536 se=5723.931 f=846.1039y=-1873.193+2.700977x5 （式2.1.5） (712.2024) (0.037971) t=(-2.630142) (71.13173)r(2)=0.995475 se=2366.912 f=5059.723y=5875.266+2.222034x6 （式2.1.6） (1531.230) (0.075790)t=（3.836958）（29.31845）r(2)=0.973940 se=5680.092 f=859.5713综合分析可见，在七个一元回归模型中，gdp(y)对居民消费水平(x3)线性关系强，拟合程度好。（2）将其余解释变量逐一带入对x3的一元线性回归方程中，得以下几个模型：y=-387.7386-0.027368x1+27.93105x3 （式2.2.1） (1367.242) (0.019261) (0.468871)t=(-0.283592) (-1.420886) (59.57089)r(2)=0.998926 se=1128.676 f=11165.14y=6262.980+0.029858x1-0.232206x2+28.56686x3 （式2.2.2） (3643.674) (0.034485) (0.119009) (0.548771)t=(1.718864) (0.865831) (-1.951160) (52.05602)r(2)=0.999048 se=1062.902 f=8394.415y=4027.197+0.032089x1-0.181952x2+24.94421x3+0.327880x4 （式2.2.3） (2612.367) (0.024319) (0.084582) (0.860040) (0.069519)t=(1.541590) (1.319521) (-2.151190) (29.00354) (4.716419)r(2)=0.999606 se=749.4065 f=12670.52y=7124.543+0.061735x1-0.295985x2+22.25771x3+0.173648x4+0.442008x5 （式2.2.4）(3548.587)(0.033478) (0.122613) (2.282200) (0.13910) (0.348654)t=(2.007713)(1.844017) (-2.413968) (9.753743) (1.243811) (1.267755)r(2)=0.999541 se=738.2831 f=10444.48y=5421.215+0.050933x1-0.224428x2+21.18387x3-0.216535x4+0.488490x5+0.336620x6 (式2.2.5) （3244.856）（0.030215）（0.113375）（2.084941）（0.203877）（0.311803）（0.139208）t=（1.670710）（1.685708）（-1.979516）（10.16042）（-1.062084）（1.566665）（2.418102）r(2)=0.999634 se=658.9930 f=10925.17从式（2.2.1）可以看出，解释变量x1与x2之间存在共线性。又因为x2对y的经济意义影响低于x1，故舍去x2。从式（2.2.4）（2.2.5）看出，解释变量x4 x5对y的影响并不显著，故将x4 x5撤去得如下模型（表2.2.1）：dependent variable: ymethod: least squaresdate: 06/03/05 time: 22:06sample: 1978 2002included observations: 25variablecoefficientstd. errort-statisticprob. c-903.4074829.1531-1.0895540.2883x1-0.0052610.012145-0.4332060.6693x323.633610.74070231.907060.0000x60.3188140.0507836.2779440.0000r-squared0.999658 mean dependent var35976.74adjusted r-squared0.999609 s.d. dependent var34444.88s.e. of regression681.1097 akaike info criterion16.03097sum squared resid9742120. schwarz criterion16.22599log likelihood-196.3871 f-statistic20452.98durbin-watson stat1.631881 prob(f-statistic)0.000000表2.2.1中能源消费总量x1的系数为负,不符合实际经济意义,现舍去1978至1982年的数据,重新回归如下:(表2.2.2)dependent variable: ymethod: least squaresdate: 06/03/05 time: 22:10sample: 1983 2002included observations: 20variablecoefficientstd. errort-statisticprob. c-1797.3341438.251-1.2496660.2294x10.0047770.0183430.2604470.7978x323.500090.84188027.913820.0000x60.3173670.0564105.6260570.0000r-squared0.999589 mean dependent var43854.06adjusted r-squared0.999512 s.d. dependent var34234.35s.e. of regression756.2503 akaike info criterion16.27148sum squared resid9150632. schwarz criterion16.47062log likelihood-158.7148 f-statistic12973.20durbin-watson stat1.689291 prob(f-statistic)0.000000经过上述逐步回归分析，表明y对x1 x3 x6的回归模型最优。三、异方差性检验1、检验（1）、goldfeldquandt检验用ols方法求得下列结果：y=-12754.36+0.236669x1+10.96853x3-0.395909x6 （19831989） (6250.887) (0.1134474) (3.689418) (0.767691)r(2)=0.994022 e1(2)=287719.1y=-5129.215+0.035047x1+23.39430x3+0.304576x6 (19942002) （7786.425）（0.065303）（1.268709）(0.079944)r(2)=0.996966 e2(2)=5139285求f统计量： f=e2(2)/e1(2)=17.86216139给定显著性水平=0.05，得临界值f0.05(4,4)=4.28,比较f=17.86216139 f0.05(4,4)=6.39可初步认为不存在异方差。（2）、arch检验利用eviews软件输出结果为（表3.1.1）dependent variable: e2method: least squaresdate: 06/03/05 time: 23:01sample(adjusted): 1986 2002included observations: 17 after adjusting endpointsvariablecoefficientstd. errort-statisticprob. c602213.2337049.71.7867190.0973e2(-1)-0.2996770.304742-0.9833800.3434e2(-2)0.0872030.2895280.3011880.7680e2(-3)-0.0050350.542470-0.0092820.9927r-squared0.115727 mean dependent var495758.6adjusted r-squared-0.088335 s.d. dependent var699258.1s.e. of regression729489.3 akaike info criterion30.04040sum squared resid6.92e+12 schwarz criterion30.23645log likelihood-251.3434 f-statistic0.567116durbin-watson stat1.948353 prob(f-statistic)0.646348丛输出的辅助回归函数重的r(2)计算（n-p）*r(2)=17*0.115727=1.967359,查(2)分布表给定=0.05得临界值(2)0.05（3）=7.81因为（n-p）*r(2)=17*0.115727=1.967359(2)0.05（3）=7.81所以拒绝h0,表明不存在异方差。四、自相关检验1、检验（1）、图示法由表2.2.2的ols估计可直接得到残差resid,生成序列e,输出结果如下图：由此图可以看出，残差et不成线性自回归，可初步认为随机误差ut不存在自相关。（2）dw检验根据表2.2.2估计的结果，dw=1.689291.给定显著性水平=0.05，查dw表n=20,k=3得下限临界值dl=0.998上限临界值du=1.676因为dudw统计量=1.6892914-du,表明不存在自相关。五、运用阿尔蒙法进行滞后性修正1，对y ,x1估计如下有限分布滞后模型y=a0+b0x1+b1x1（-1）+b2x1(-2)+b3x1(-3)应用eviews软件得回归分析结果如下：dependent variable: ymethod: least squaresdate: 06/04/05 time: 23:27sample: 1983 2002included observations: 20variablecoefficientstd. errort-statisticprob. c-78223.1710398.98-7.5221950.0000pdl01-0.9142960.400006-2.2857050.0362pdl02-0.8282240.410601-2.0170990.0608pdl031.0872860.3888242.7963490.0129r-squared0.935158mean dependent var43854.06adjusted r-squared0.923001s.d. dependent var34234.35s.e. of regression9499.609akaike info criterion21.33275sum squared resid1.44e+09schwarz criterion21.53189log likelihood-209.3275f-statistic76.91841durbin-watson stat0.481394prob(f-statistic)0.000000 lag distribution of x1icoefficientstd. errort-statistic . * |0 1.00121 0.44552 2.24728 * . |1-0.91430 0.40001-2.28570 * . |2-0.65523 0.39089-1.67625 . *|3 1.77840 0.45803 3.88274sum of lags 1.21009 0.08629 14.0238即是y-78223.17+1.00121x10.91430x1(-1) -0.65523x1(-2)+ 1.77840x1(-3)2对y ,x3有限分布滞后模型y=a0+b0x3+b1x3(-1)+b2x3(-2)+b3x3(-3)用eviews软件显示回归分如下：dependent variable: ymethod: least squaresdate: 06/04/05 time: 23:31sample: 1983 2002included observations: 20variablecoefficientstd. errort-statisticprob. c-2497.770516.2504-4.8382920.0002pdl012.1069172.1767250.9679300.3475pdl02-15.958292.156581-7.3998110.0000pdl038.5508662.1502223.9767370.0011r-squared0.998874mean dependent var43854.06adjusted r-squared0.998663s.d. dependent var34234.35s.e. of regression1251.806akaike info criterion17.27942sum squared resid25072277schwarz criterion17.47856log likelihood-168.7942f-statistic4731.442durbin-watson stat1.165284prob(f-statistic)0.000000 lag distribution of x3icoefficientstd. errort-statistic . *|0 26.6161 2.22221 11.9773 .* |1 2.10692 2.17672 0.96793 * . |2-5.30051 2.11824-2.50232 . * |3 4.39380 2.45928 1.78662sum of lags 27.8163 0.33326 83.4682即是y-2497.770+26.6161 x3+2.10692 x3 (-1) -5.30051 x3 (-2)+ 4.39380 x3 (-3)3对y , x6有限分布滞后模型y=a0+b0 x6+b1 x6 (-1)+b2 x6 (-2)+b3 x6 (-3)用eviews软件显示回归分如下：dependent variable: ymethod: least squaresdate: 06/04/05 time: 23:37sample: 1983 2002included observations: 20variablecoefficientstd. errort-statisticprob. c8207.6061631.5105.0306800.0001pdl010.7740280.3432472.2550150.0385pdl02-0.0758520.363485-0.2086800.8373pdl03-0.0649750.330846-0.1963910.8468r-squared0.983441mean dependent var43854.06adjusted r-squared0.980336s.d. dependent var34234.35s.e. of regression4800.586akaike info criterion19.96772sum squared resid3.69e+08schwarz criterion20.16687log likelihood-195.6772f-statistic316.7499durbin-watson stat0.342226prob(f-statistic)0.000000 lag distribution of x6icoefficientstd. errort-statistic . *|0 0.78491 0.39271 1.99867 . *|1 0.77403 0.34325 2.25501 . * |2 0.63320 0.34422 1.83951 . * |3 0.36242 0.46095 0.78625sum of lags 2.55456 0.14135 18.0730即是y=8207.606+0.78491 x6+0.77403 x6 (-1)+ 0.63320 x6 (-2)+ 0.36242 x6 (-3)通过以上一系列统计检验可以说明：我国gdp的增长与能源消费总量x1,居民消费水平x3，进出口贸易总额x6有很高的相关性。其中，又以居民消费水平x3的影响程度最为显著。由此可以看出影响我国gdp的主要因素是居民消费水平，进出口贸易总额，能源消费总量。附本（相关表格）（表2.1.2）dependent variable: ymethod: least squaresdate: 06/03/05 time: 21:08sample: 1978 2002included observations: 25variablecoefficientstd. errort-statisticprob. c-67070.349781.840-6.8566180.0000x11.0292320.09357510.999020.0000r-squared0.840254 mean dependent var35976.74adjusted r-squared0.833308 s.d. dependent var34444.88s.e. of regression14063.12 akaike info criterion22.01712sum squared resid4.55e+09 schwarz criterion22.11463log likelihood-273.2140 f-statistic120.9784durbin-watson stat0.094594 prob(f-statistic)0.000000(表2.1.3)dependent variable: ymethod: least squaresdate: 06/03/05 time: 21:12sample: 1978 2002included observations: 25variablecoefficientstd. errort-statisticprob. c-133299.712588.50-10.589010.0000x22.9620050.21647613.682860.0000r-squared0.890591 mean dependent var35976.74adjusted r-squared0.885834 s.d. dependent var34444.88s.e. of regression11638.38 akaike info criterion21.63862sum squared resid3.12e+09 schwarz criterion21.73613log likelihood-268.4828 f-statistic187.2206durbin-watson stat0.159690 prob(f-statistic)0.000000(表2。1.4)dependent variable: ymethod: least squaresdate: 06/03/05 time: 21:15sample: 1978 2002included observations: 25variablecoefficientstd. errort-statisticprob. c-2268.943348.7497-6.5059360.0000x327.317560.186822146.22260.0000r-squared0.998925 mean dependent var35976.74adjusted r-squared0.998879 s.d. dependent var34444.88s.e. of regression1153.406 akaike info criterion17.01544sum squared resid30597948 schwarz criterion17.11295log likelihood-210.6931 f-statistic21381.06durbin-watson stat0.841840 prob(f-statistic)0.000000(表2.1.5)dependent variable: ymethod: least squaresdate: 06/03/05 time: 21:17sample: 1978 2002included observations: 25variablecoefficientstd. errort-statisticprob. c617.77131669.7900.3699690.7148x42.7522820.09462029.087870.0000r-squared0.973536 mean dependent var35976.74adjusted r-squared0.972385 s.d. dependent var34444.88s.e. of regression5723.931 akaike info criterion20.21932sum squared resid7.54e+08 schwarz criterion20.31683log likelihood-250.7415 f-statistic846.1039durbin-watson stat0.557749 prob(f-statistic)0.000000( 表2.1.6)dependent variable: ymethod: least squaresdate: 06/03/05 time: 21:21sample: 1978 2002included observations: 25variablecoefficientstd. errort-statisticprob. c-1873.193712.2024-2.6301420.0150x52.7009770.03797171.131730.0000r-squared0.995475 mean dependent var35976.74adjusted r-squared0.995278 s.d. dependent var34444.88s.e. of regression2366.912 akaike info criterion18.45318sum squared resid1.29e+08 schwarz criterion18.55069log likelihood-228.6647 f-statistic5059.723durbin-watson stat0.288053 prob(f-statistic)0.000000(表2.1.7)dependent variable: ymethod: least squaresdate: 06/03/05 time: 21:23sample: 1978 2002included observations: 25variablecoefficientstd. errort-statisticprob. c5875.2661531.2303.8369580.0008x62.2220340.07579029.318450.0000r-squared0.973940 mean dependent var35976.74adjusted r-squared0.972807 s.d. dependent var34444.88s.e. of regression5680.092 akaike info criterion20.20394sum squared resid7.42e+08 schwarz criterion20.30145log likelihood-250.5493 f-statistic859.5713durbin-watson stat0.739809 prob(f-statistic)0.000000（表2.2.1）dependent variable