• 中国资本外逃的成因解释与计量分析 不要轻易放弃。学习成长的路上,我们长路漫漫,只因学无止境。


    中国本钱外逃的成因阐明

    顺叙与计量剖析

    内容提要:

    ?本文在对本钱外逃做出界说的基础上,经由进程测算本钱外逃数据,试图经由进程树立批改

    休学计量模子对本钱外逃产生的缘由做出计量经济学阐明

    顺叙。对本钱外逃的传统阐明

    顺叙是利率差距,经济和政治危险以及内债累赘,但咱们发觉这些都缺乏

    不置可否以阐明

    顺叙中国本钱外逃,更首要的要素与转型经济下不凡情形有关,即历久对外资的优惠以及证券市场的不凡性等。

    关键词:

    ?本钱外逃 计量经济

    一、本钱外逃的界说

    ?对什么是“本钱外逃”目前理论界还不统一的尺度,不外有一点是能够必定的:它是一种非正常的本钱运动。咱们综合Kindleberger(1937年)、Dooley(1986年)和Cuddington(1986年)提出的三种界说,将本钱外逃阐明

    顺叙为“违犯现实利率指向的出于躲避危险、躲避牵制、投契套利斟酌的本钱非正常外流”。

    二、1991——2003年间中国本钱外逃领域测算体式格局挑选

    (一)、直接法

    ?直接法又被称为卡廷顿法或国际收支平衡表法,是经由进程一个或几个对海内异常危险反应迅速的短时间本钱外流名目来估量本钱外逃额。

    ?测算公式:本钱外逃=过错与脱漏项+私家非银行部门短时间本钱流出

    ?这类体式格局具有两个缺点:一是对本钱外逃的估量规模过于狭隘,仅包孕短时间本钱外逃,不包孕在二级市场上具有较强运动性的、能够作为短时间本钱替代品的历久本钱外逃(李心丹,钟伟,1998);二是过错与脱漏项中即包孕未记载的本钱运动,也包孕了现实意思上的统计误差。

    (二)、直接法

    ?直接法又称为世界银行法或残存法,是经由进程国际收支平衡表中四个名目的残存局部来直接地对本钱外逃额举行估量。

    ?测算公式:本钱外逃=时常名目顺差+本国直接投资净流入+内债添加-贮备资产添加

    ?时常名目顺差、本国直接投资净流入和内债添加属于东道国的“资金起源”,而外汇贮备添加属于东道国的“资金使用”,当“资金起源”大于“资金使用”时,就意味着产生了本钱外逃,这是直接法的基本思路。直接法在一定水平上弥补了直接法的缺点,然而产生了新的问题:一是它没法反应经由进程时常名目子虚交易举行的运动额,因而直接法夸张了本钱外逃的领域。

    ?本文从简捷计,不采纳更为准确的摩根公司法和克莱因法,而是使用直接法和直接法二者均匀盘算出的本钱外逃量作为模子中的应变量。

    表1? 我国1991—2003年本钱外逃数额(单元:亿美圆)

    年份?1991?1992?1993?1994?1995?1996?1997?1998?1999?2000?2001?2002?2003

    直接?67.43?117.39?133.72?134.77?173.22?164.80?257.27?287.52?241.35?183.18?-85.42?-114.97?-395.9

    直接?101.79?243.79?236.95?181.58?267.66?233.55?503.56?790.86?499.30?471.79?318.16?79.55?-16.49

    均匀?84.61?180.59?185.335?158.175?220.44?199.175?380.415?539.19?370.325?327.485?116.37?-17.71?-206.195

    三、本钱外逃的成因

    ?咱们查找到的良多文献都对本钱外逃给出了的缘由,这里联合中国的现实情形逐个加以剖析。

    (一)、躲避危险

    ? “危险”一方面是指通货膨胀惹起的危险:一国通货膨胀率较高会下降本钱的现实收益率,产生货泉贬值预期,从而安慰人们执行货泉替代行为来躲避危险。咱们用中美通胀率之差来权衡这类要素;另一方面,危险指投资收益危险,因为股市的剧烈颠簸也许会使人们对将来经济走势产生悲观预期,从而出于躲避投资危险的斟酌而把本钱转移到外洋。咱们采纳Garman-klass(1980)第T日方差估量式来盘算股市颠簸率,此中ut、dt、ct别离默示第T日股价的最高值、最低值和开盘价减去收盘代价差,并以股市颠簸率来权衡这类要素。

    ?

    (二)、躲避牵制

    ?“牵制”一方面指外汇牵制:人们为了保存今后用汇的灵活性,不情愿将外汇汇回海内或保具有海内的征象,被称之为“老鼠夹子效应”。而我国96年时常名目下实现可自在兑换,然而恰恰相反的是本钱外逃更重大了,以是“老鼠夹子效应”不克不及阐明

    顺叙我国本钱外逃的转变;另一方面,牵制指的是国度政策的牵制,即金融压制:生长中国度金融压制(Financial Repression)的一个首要默示在于其现实利率往往低于发达国度,如许的利率差往往导致生长中国度海内储蓄外流。然而中国的利率还不齐全市场化,并且中国住民储蓄更多是为了对付将来的不确定性,而不是投资理财的需求,因而利率差距不应当用在这里阐明

    顺叙本钱外逃。

    (三)、内债累赘

    ?一些转型国度涌现的本钱外逃情形,很大水平上是因为连年添加的内债(如俄罗斯、巴西),因为内债的添加作为一项次要的资金起源对本钱外逃起着较较着的鞭策性作用。咱们将内债余额做为一个变量来阐明

    顺叙本钱外逃。

    ?

    (四)、外资轨制

    ?我国自改造开放以来为鼎力吸引外资对其开列了一系列优惠前提,使其享有“超国民回报”,次要默示在税收、外汇政策、地方性优惠、产业政策、银行金融支撑以至市场准入多个方面。鼎力吸引外资生长经济是坏事,然而却形成了内外资不平等,同时相干法律法规的实施与监视不尽严正,如许就会有人哄骗轨制破绽产生“寻租行为”:中、外方共谋,以高报外方什物投资代价或中方替外方垫付投资资金的体式格局,经由进程设立合资企业向境外转移境内资产或权利;同时,因为一些社会中介机构为外商投资企业子虚验资,产生外商直接投资高报。这些虚增投资终极都邑以利润汇回或以清盘方式要求换汇汇出,本钱先流入再流出,从而形成曲折的本钱外逃。咱们用外商直接投资(FDI)的数据来权衡这类缘由的本钱外逃。

    (五)、庇护公有产权,埋没造孽支出

    ?资产所有者情愿为资产保守秘密付出代价,从而要求寻觅最好“避风港”的投资所在。这些投资者的支出既有正当的,也有非法所得。在我国上世纪关于保障公有产权的法律和轨制支配尚不健全时,良多人情愿将财产转移到西文发达国度;还有一局部贪污、败北、受贿者,以及侵吞国有资产者出于隐匿证据和躲避处分的目的而将资产转移到外洋,都形成了本钱外逃。然而因为这局部统计资料难于查找并且即使是估算也不免会带有很大的主观性,因而,咱们不对这局部举行量化,但咱们以为此局部是值得注重的影响要素。

    ?综上所述,咱们将模子方式设定为:Y=B0+B1*X1+B2*X2+B3*X3+B4*X4+U

    ?此中Y 为用直接法和直接法均匀失掉的本钱外逃量,B0为截距项,X1为内债余额,X2为股市颠簸率,X3中美通胀率之差 ,X4为FDI。

    表2 计量经济模子自变量数据表

    X1、X4单元:亿美圆??? X2、X3单元:%

    年份?本钱外逃额

    Y?内债余额

    X1?股市颠簸率

    X2?中美通胀差

    X3?FDI

    X4

    1991? 84.61000? 605.6100? 7495.330?-0.700000? 43.66000

    1992? 180.5900? 693.2100? 569010.1? 3.300000? 110.0800

    1993? 185.3350? 835.7300? 374497.7? 11.60000? 275.1500

    1994? 158.1750? 928.0600? 274326.4? 21.60000? 337.6700

    1995? 220.4400? 1065.900? 100576.0? 14.10000? 375.2100

    1996? 199.1750? 1162.750? 244938.5? 5.400000? 417.2600

    1997? 380.4150? 1309.600? 216168.3? 0.500000? 452.5700

    1998? 539.1900? 1460.430? 2670830.?-2.400000? 454.6300

    1999? 370.3250? 1518.300? 295692.1?-3.600000? 403.1900

    2000? 327.4850? 1457.300? 265068.3?-2.980000? 407.1500

    2001? 116.3700? 1701.100? 356573.0?-2.130000? 468.7800

    2002?-17.71000? 1713.600? 126705.4?-2.390000? 527.4300

    2003?-206.1950? 1933.400? 141625.5?-2.620000? 535.0500

    资料起源:IMF《国际金融统计年鉴2004》、《中国证券期货统计年鉴2004》上海证券交易所指数。

    四、计量经济剖析

    (一)、初步回归

    咱们对1991年到2003年的数据做简略线性回归:

    表3

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 20:12

    Sample: 1991 2003

    Included observations: 13

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?589.3213?242.2525?2.432674?0.0410

    X1?-0.869555?0.423151?-2.054951?0.0739

    X2?0.000131?6.67E-05?1.960063?0.0856

    X3?15.71841?10.05263?1.563612?0.1565

    X4?1.874040?1.017033?1.842654?0.1026

    R-squared?0.580716???? Mean dependent var?195.2465

    Adjusted R-squared?0.371075???? S.D. dependent var?188.8626

    S.E. of regression?149.7772???? Akaike info criterion?13.13990

    Sum squared resid?179465.6???? Schwarz criterion?13.35719

    Log likelihood?-80.40933???? F-statistic?2.770042

    Durbin-Watson stat?1.197965???? Prob(F-statistic)?0.102694

    ?了局发觉各个T值后果欠好,R-squared和Adjusted R-squared偏小,F值也太小。

    ?

    ?推就其经济缘由在于:因为遭到1997年到1998年亚洲金融风暴的影响,本钱外逃量在98年到达峰值,而在此进程中,人民币坚持坚硬,在此之后中国经济一支独秀,生长坚持快捷不变,本钱便起头以各种方式悄然内流,到2002年时本钱外逃额已成为负值,以是咱们将会用91年到00年的数据举行最小二乘估量,最初引人虚构变量检讨99年前后,看本钱外逃量是否有较着转变。

    ?咱们对1991年到2000年的数据做简略线性回归:

    ?表4

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 20:28

    Sample: 1991 2000

    Included observations: 10

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?-14.90173?110.5250?-0.134827?0.8980

    X1?0.171394?0.196393?0.872710?0.4227

    X2?7.71E-05?2.16E-05?3.560162?0.0162

    X3?3.326379?3.688420?0.901844?0.4085

    X4?0.205133?0.408642?0.501987?0.6370

    R-squared?0.936064???? Mean dependent var?264.5740

    Adjusted R-squared?0.884915???? S.D. dependent var?136.3162

    S.E. of regression?46.24405???? Akaike info criterion?10.81260

    Sum squared resid?10692.56???? Schwarz criterion?10.96389

    Log likelihood?-49.06298???? F-statistic?18.30085

    Durbin-Watson stat?3.095533???? Prob(F-statistic)?0.003452

    ?如今失掉的了局绝对较好:X2经由进程T检讨,阐明

    顺叙股市颠簸率对本钱外逃有较着影响;回归方程经由进程了F检讨,阐明

    顺叙自变量和应变量之间具有较为较着的线性关连;可决系数R-squared为0.936064,阐明

    顺叙总变差中由模子阐明

    顺叙的局部占93.60%。

    ?然而X1、X3和X4都不经由进程T检讨,以是模子有待改良。

    ?

    (二)、多重共线性

    ?咱们对数据举行多重共线性检讨,使用OLS法逐一求Y对各个阐明

    顺叙变量的回归。

    表5

    ?Y?X1?X2?X3?X4

    Y? 1.000000? 0.859217? 0.711553? 0.529161? 0.721359

    X1? 0.859217? 1.000000? 0.361546? 0.450377? 0.880883

    X2? 0.711553? 0.361546? 1.000000? 0.282517? 0.292872

    X3? 0.529161? 0.450377? 0.282517? 1.000000? 0.046439

    X4? 0.721359? 0.880883? 0.292872? 0.046439? 1.000000

    发觉X1和X4之间具有较较着的多重共线关连。

    用逐步回归法对模子举行批改:

    表6:? Y与X1回归

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 20:32

    Sample: 1991 2000

    Included observations: 10

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?-125.0424?85.29211?-1.466048?0.1808

    X1?0.353013?0.074316?4.750159?0.0014

    R-squared?0.738254???? Mean dependent var?264.5740

    Adjusted R-squared?0.705536???? S.D. dependent var?136.3162

    S.E. of regression?73.97134???? Akaike info criterion?11.62209

    Sum squared resid?43774.08???? Schwarz criterion?11.68261

    Log likelihood?-56.11045???? F-statistic?22.56401

    Durbin-Watson stat?1.766033???? Prob(F-statistic)?0.001445

    表7:?? Y与X2回归

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 20:33

    Sample: 1991 2000

    Included observations: 10

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?201.8958?38.87018?5.194106?0.0008

    X2?0.000125?4.36E-05?2.864336?0.0210

    R-squared?0.506308???? Mean dependent var?264.5740

    Adjusted R-squared?0.444596???? S.D. dependent var?136.3162

    S.E. of regression?101.5902???? Akaike info criterion?12.25663

    Sum squared resid?82564.58???? Schwarz criterion?12.31715

    Log likelihood?-59.28314???? F-statistic?8.204422

    Durbin-Watson stat?1.019485???? Prob(F-statistic)?0.021011

    表8:? Y与X3回归

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 20:33

    Sample: 1991 2000

    Included observations: 10

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?304.3415?44.87121?6.782557?0.0001

    X3?8.493708?4.815349?1.763882?0.1158

    R-squared?0.280011???? Mean dependent var?264.5740

    Adjusted R-squared?0.190012???? S.D. dependent var?136.3162

    S.E. of regression?122.6836???? Akaike info criterion?12.63395

    Sum squared resid?120410.2???? Schwarz criterion?12.69447

    Log likelihood?-61.16976???? F-statistic?3.111280

    Durbin-Watson stat?0.879554???? Prob(F-statistic)?0.115760

    表9:?? Y与X4回归

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 20:33

    Sample: 1991 2000

    Included observations: 10

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?39.78166?82.61285?0.481543?0.6430

    X4?0.686060?0.232876?2.946035?0.0185

    R-squared?0.520359???? Mean dependent var?264.5740

    Adjusted R-squared?0.460403???? S.D. dependent var?136.3162

    S.E. of regression?100.1341???? Akaike info criterion?12.22775

    Sum squared resid?80214.72???? Schwarz criterion?12.28827

    Log likelihood?-59.13877???? F-statistic?8.679124

    Durbin-Watson stat?1.151907???? Prob(F-statistic)?0.018538

    ?经由进程以上四个回归的比较,失掉Y与X1回归的可决系数0.738254为最大值,进一步举行三三组合的回归,失掉Y与X1,X2回归失掉可决系数为0.923148最大:

    表10

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 20:40

    Sample: 1991 2000

    Included observations: 10

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?-90.05706?50.13742?-1.796204?0.1155

    X1?0.284506?0.046173?6.161804?0.0005

    X2?8.09E-05?1.97E-05?4.103783?0.0046

    R-squared?0.923148???? Mean dependent var?264.5740

    Adjusted R-squared?0.901191???? S.D. dependent var?136.3162

    S.E. of regression?42.84952???? Akaike info criterion?10.59659

    Sum squared resid?12852.57???? Schwarz criterion?10.68737

    Log likelihood?-49.98295???? F-statistic?42.04235

    Durbin-Watson stat?2.777157???? Prob(F-statistic)?0.000126

    ?把X2插手模子,再进一步举行四四组合的回归,了局为X3插手模子后使可决系数进步到0.932842为最大值:

    表11

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 20:38

    Sample: 1991 2000

    Included observations: 10

    ????

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    ????

    C?-60.08981?59.99809?-1.001529?0.3552

    X1?0.266166?0.050615?5.258630?0.0019

    X2?7.83E-05?2.01E-05?3.888297?0.0081

    X3?1.788595?1.921968?0.930606?0.3880

    ????

    R-squared?0.932842???? Mean dependent var?264.5740

    Adjusted R-squared?0.899263???? S.D. dependent var?136.3162

    S.E. of regression?43.26555???? Akaike info criterion?10.66176

    Sum squared resid?11231.45???? Schwarz criterion?10.78280

    Log likelihood?-49.30882???? F-statistic?27.78048

    Durbin-Watson stat?2.956579???? Prob(F-statistic)?0.000646

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    ?经由三元回归失掉的了局绝对四元回归更好,X1,X2经由进程T检讨,R-squared为0.932842

    F检讨也较好。故模子改良为:Y= -60.08981+0.266166X1+7.83E-05X2+1.788595X3+U

    (三)、异方差检讨:

    ?因为咱们把握的数据缺乏

    不置可否,不满足WHITE检讨的大样本要求,故直接用ARCH检讨法:

    表12:? ARCH检讨(p=3)

    ARCH demo:

    F-statistic?0.411276???? Probability?0.757654

    Obs*R-squared?2.039949???? Probability?0.564157

    ????

    demo Equation:

    Dependent Variable: RESID^2

    Method: Least Squares

    Date: 06/14/05?? Time: 20:53

    Sample(adjusted): 1994 2000

    Included observations: 7 after adjusting endpoints

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?1962.629?1674.784?1.171870?0.3258

    RESID^2(-1)?0.261660?0.560290?0.467008?0.6723

    RESID^2(-2)?-0.459623?0.526586?-0.872835?0.4470

    RESID^2(-3)?-0.120201?0.572383?-0.210001?0.8471

    R-squared?0.291421???? Mean nba竞猜篮球彩票,nba竞猜篮球彩票直播,nba竞彩篮球彩票怎么买 dependent var?1482.792

    Adjusted R-squared?-0.417157???? S.D. dependent var?2276.217

    S.E. of regression?2709.709???? Akaike info criterion?18.94263

    Sum squared resid?22027576???? Schwarz criterion?18.91172

    Log likelihood?-62.29920???? F-statistic?0.411276

    Durbin-Watson stat?2.129635???? Prob(F-statistic)?0.757654

    ?P值到达0.757654,意味着谢绝H0犯过错的也许性约为75.77%,并且RESID^2(-1),RESID^2(-2),RESID^2(-3)的T值均不超过2,以是咱们应当接收HO,即不具有异方差。

    (四)、自相干性:

    ?E 和E(-1)的残差散点图以下:

    ?

    ?由图能够看出也许具有负的自相干,咱们进一步用DW法检讨自相干:

    ?由表4咱们能够看到拟合了局中DW值为2.956579,n=10,k’=3,查表得dL=0.525,dU=2.016,那末没法判断的区域为:1.984到3.475,DW值落入了没法判断区域,但联合E 和E(-1)的残差散点图,能够判断具有负的一阶自相干。

    自相干的批改:

    狭义差分法

    ?因为咱们的数据属于小样本,也许狭义差分法批改的了局会不抱负,但不妨一试。DW = 2.956579, p=1-DW/2 故p=-0.4782895,产生序列:GENE DY=Y+0.4782895*Y(-1),同理天生:DX1 DX2 DX3

    用OLS法估量参数:

    ?表13

    Dependent Variable: DY

    Method: Least Squares

    Date: 06/14/05?? Time: 21:42

    Sample(adjusted): 1992 2000

    Included observations: 9 after adjusting endpoints

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?-57.18712?65.91436?-0.867597?0.4253

    DX1?0.241975?0.035512?6.813960?0.0010

    DX2?9.52E-05?1.74E-05?5.482540?0.0028

    DX3?1.981471?1.372566?1.443625?0.2084

    R-squared?0.976915???? Mean dependent var?407.7697

    Adjusted R-squared?0.963063???? S.D. dependent var?181.3758

    S.E. of regression?34.85844???? Akaike info criterion?10.24157

    Sum squared resid?6075.554???? Schwarz criterion?10.32923

    Log likelihood?-42.08706???? F-statistic?70.52904

    Durbin-Watson stat?2.992579???? Prob(F-statistic)?0.000164

    ?咱们发觉使用狭义差分法后,了局并不抱负,DW值反而从2.956579增大到了2.992579,自相干水平减轻。

    2.Cochrane-Orcutt迭代法

    ?表14

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 21:45

    Sample(adjusted): 1992 2000

    Included observations: 9 after adjusting endpoints

    Convergence achieved after 8 iterations

    ????

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    ????

    C?-44.67290?35.53624?-1.257108?0.2771

    X1?0.242456?0.028848?8.404653?0.0011

    X2?0.000102?1.77E-05?5.772255?0.0045

    X3?1.714332?1.159164?1.478938?0.2132

    AR(1)?-0.792707?0.332423?-2.384629?0.0756

    ????

    R-squared?0.962418???? Mean dependent var?284.5700

    Adjusted R-squared?0.924836???? S.D. dependent var?128.0885

    S.E. of regression?35.11680???? Akaike info criterion?10.25542

    Sum squared resid?4932.758???? Schwarz criterion?10.36499

    Log likelihood?-41.14938???? F-statistic?25.60850

    Durbin-Watson stat?2.706947???? Prob(F-statistic)?0.004131

    ????

    Inverted AR Roots?????? -.79

    ????

    ?

    ?可见,经由Cochrane-Orcutt迭代法批改后的DW值有所下降,但仍然不落入咱们想要的区域,因为中美通胀率差具有正数,从而不克不及哄骗对数线性回归来批改,自相干批改只能到此为止了。

    ?Y = -44.67290+ 0.242456*X1 + 0.000102*X2 + 1.714332*X3 + [AR(1)=-0.792707]

    (五)、散布滞后问题:

    ?从经济意思斟酌,本钱外逃量与模子中的各个经济阐明

    顺叙变量之间的因果联络不也许在刹时实现,这一时间进程也许具有滞后,产生滞后效应。

    ?当随机扰动项具有自相干时,D-W检讨倾向于得出非自相干的论断,咱们用德宾h-检讨举行估量。

    ?

    表15

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/14/05?? Time: 22:23

    Sample(adjusted): 1992 2000

    Included observations: 9 after adjusting endpoints

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    C?-46.69715?110.6676?-0.421959?0.6947

    X1?0.301933?0.119640?2.523691?0.0651

    X2?7.68E-05?2.25E-05?3.412730?0.0270

    X3?2.988362?2.709664?1.102853?0.3320

    Y(-1)?-0.177326?0.234915?-0.754850?0.4923

    R-squared?0.930709???? Mean dependent var?284.5700

    Adjusted R-squared?0.861419???? S.D. dependent var?128.0885

    S.E. of regression?47.68291???? Akaike info criterion?10.86720

    Sum squared resid?9094.639???? Schwarz criterion?10.97677

    Log likelihood?-43.90242???? F-statistic?13.43194

    Durbin-Watson stat?2.938552???? Prob(F-statistic)?0.013738

    ?按照德宾h检讨法算出︱h︱=1.98477只是比ha/2=1.96大一点, 注意到德宾h检讨法是针对大样本的,用于小样本后果较差,这一了局值得再斟酌;另外Y(-1)的p值=0.4923较大,t值=-0.754850较小,并且R^2也比表11中的R^2小,以是应综合斟酌,接收原假定p=0,以为模子的扰动项不具有一阶自相干。

    (六)、虚构变量

    ?因为遭到1997年到1998年亚洲金融风暴的影响,本钱外逃量在98年到达峰值,而在此进程中,人民币坚持坚硬,以及在此之后中国经济一支独秀,增进快捷而不变,本钱有也许起头以各种方式悄然内流,咱们斟酌到这点,便引人虚构变量以证实咱们的预测,以乘法方式引入虚构变量

    dt= 1 (1999年之前)

    dt= 0 (1999年当前)

    表16

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/07/05?? Time: 09:50

    Sample: 1991 2003

    Included observations: 13

    ????

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    ????

    C?2018.446?727.3506?2.775066?0.0216

    DTX?-2143.432?732.5085?-2.926153?0.0169

    X1?-1.152348?0.407276?-2.829401?0.0197

    DTX*X1?1.505297?0.414235?3.633922?0.0055

    ????

    R-squared?0.880902???? Mean dependent var?195.2362

    Adjusted R-squared?0.841203???? S.D. dependent var?188.8532

    S.E. of regression?75.25677???? Akaike info criterion?11.72735

    Sum squared resid?50972.23???? Schwarz criterion?11.90118

    Log likelihood?-72.22777???? F-statistic?22.18938

    Durbin-Watson stat?2.163759???? Prob(F-statistic)?0.000171

    表17

    Dependent Variable: Y

    Method: Least Squares

    Date: 06/07/05?? Time: 09:51

    Sample: 1991 2003

    Included observations: 13

    Variable?Coefficient?Std. Error?t-Statistic?Prob.

    C?292.0083?177.8893?1.641517?0.1289

    X1?-0.076780?0.134501?-0.570852?0.5796

    R-squared?0.028772?Mean dependent var?195.2362

    Adjusted R-squared?-0.059521?S.D. dependent var?188.8532

    S.E. of regression?194.3923?Akaike info criterion?13.51827

    Sum squared resid?415672.2?Schwarz criterion?13.60519

    Log likelihood?-85.86877?F-statistic?0.325872

    Durbin-Watson stat?0.472040?Prob(F-statistic)?0.579575

    失掉的了局为:

    2000年之前对X1的函数为:Y=292.0083-0.076780X1

    2000年当前的为:Y=2018.446-1.1523X1

    具有较着的转变,阐明

    顺叙在2000年前后本钱外逃情形有所改良。

    (七)、ADF检讨、协整、GRANGER因果检讨

    1、在LEVEL的基础上,咱们用有截距项有趋向的UNIT ROOT demo检讨了局以下:

    表18:? 零阶滞后

    ADF demo Statistic? 0.020079???? 1%?? Critical Value*?-4.9893

    ????? 5%?? Critical Value?-3.8730

    ????? 10% Critical Value?-3.3820

    *MacKinnon critical values for rejection of hypothesis of a unit root.

    ????

    ????

    Augmented Dickey-Fuller demo Equation

    Dependent Variable: D(Y)

    Method: Least Squares

    Date: 06/14/05?? Time: 20:13

    Sample(adjusted): 1992 2003

    Included observations: 12 after adjusting endpoints

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    Y(-1)?0.004567?0.227474?0.020079?0.9844

    C?126.1659?82.79925?1.523757?0.1619

    @TREND(1991)?-23.29910?9.575827?-2.433117?0.0378

    R-squared?0.400004???? Mean dependent var?-24.23375

    Adjusted R-squared?0.266671???? S.D. dependent var?132.6937

    S.E. of regression?113.6317???? Akaike info criterion?12.51612

    Sum squared resid?116209.5???? Schwarz criterion?12.63735

    Log likelihood?-72.09672???? F-statistic?3.000046

    Durbin-Watson stat?1.891016???? Prob(F-statistic)?0.100385

    表19:?? 一阶滞后

    ADF demo Statistic?-0.116574???? 1%?? Critical Value*?-5.1152

    ????? 5%?? Critical Value?-3.9271

    ????? 10% Critical Value?-3.4104

    *MacKinnon critical values for rejection of hypothesis of a unit root.

    ????

    ????

    Augmented Dickey-Fuller demo Equation

    Dependent Variable: D(Y)

    Method: Least Squares

    Date: 06/14/05?? Time: 20:14

    Sample(adjusted): 1993 2003

    Included observations: 11 after adjusting endpoints

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    Y(-1)?-0.039919?0.342439?-0.116574?0.9105

    D(Y(-1))?0.089477?0.486319?0.183988?0.8592

    C?128.1929?116.2949?1.102309?0.3068

    @TREND(1991)?-21.83870?15.89589?-1.373858?0.2119

    R-squared?0.350436???? Mean dependent var?-35.16227

    Adjusted R-squared?0.072052???? S.D. dependent var?133.3861

    S.E. of regression?128.4909???? Akaike info criterion?12.82488

    Sum squared resid?115569.4???? Schwarz criterion?12.96957

    Log likelihood?-66.53685???? F-statistic?1.258822

    Durbin-Watson stat?1.961298???? Prob(F-statistic)?0.359465

    表20:?? 二阶滞后

    ADF demo Statistic?-0.466517???? 1%?? Critical Value*?-5.2735

    ????? 5%?? Critical Value?-3.9948

    ????? 10% Critical Value?-3.4455

    *MacKinnon critical values for rejection of hypothesis of a unit root.

    ????

    ????

    Augmented Dickey-Fuller demo Equation

    Dependent Variable: D(Y)

    Method: Least Squares

    Date: 06/14/05?? Time: 20:15

    Sample(adjusted): 1994 2003

    Included observations: 10 after adjusting endpoints

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    Y(-1)?-0.284948?0.610798?-0.466517?0.6605

    D(Y(-1))?0.293012?0.668398?0.438380?0.6794

    D(Y(-2))?0.264305?0.664511?0.397744?0.7072

    C?178.5436?155.1397?1.150857?0.3018

    @TREND(1991)?-18.94503?26.78272?-0.707360?0.5109

    R-squared?0.419693???? Mean dependent var?-39.15550

    Adjusted R-squared?-0.044552???? S.D. dependent var?139.9065

    S.E. of regression?142.9891???? Akaike info criterion?13.07027

    Sum squared resid?102229.4???? Schwarz criterion?13.22156

    Log likelihood?-60.35133???? F-statistic?0.904033

    Durbin-Watson stat?2.177703???? Prob(F-statistic)?0.525697

    表21:?? 三阶滞后

    ADF demo Statistic?-0.667152???? 1%?? Critical Value*?-5.4776

    ????? 5%?? Critical Value?-4.0815

    ????? 10% Critical Value?-3.4901

    *MacKinnon critical values for rejection of hypothesis of a unit root.

    ????

    ????

    Augmented Dickey-Fuller demo Equation

    Dependent Variable: D(Y)

    Method: Least Squares

    Date: 06/14/05?? Time: 20:16

    Sample(adjusted): 1995 2003

    Included observations: 9 after adjusting endpoints

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    Y(-1)?-0.691240?1.036105?-0.667152?0.5524

    D(Y(-1))?0.499741?0.975543?0.512270?0.6438

    D(Y(-2))?0.610738?0.977396?0.624862?0.5764

    D(Y(-3))?0.301781?0.796486?0.378890?0.7300

    C?294.4294?211.9747?1.388984?0.2590

    @TREND(1991)?-18.90662?41.97180?-0.450460?0.6829

    R-squared?0.583390???? Mean dependent var?-40.48556

    Adjusted R-squared?-0.110960???? S.D. dependent var?148.3262

    S.E. of regression?156.3389???? Akaike info criterion?13.17665

    Sum squared resid?73325.54???? Schwarz criterion?13.30813

    Log likelihood?-53.29493???? F-statistic?0.840196

    Durbin-Watson stat?2.199325???? Prob(F-statistic)?0.597733

    ?

    ?能够看出,不论是几阶的滞后,咱们的ADF检讨失掉的值都大于麦金龙临界值,即原序列具有单元根,咱们不克不及谢绝原假定,那末本钱外逃的序列也许是非安稳的,由本钱外逃的时序图能够佐证:

    ?咱们用一样的体式格局,别离对三个阐明

    顺叙变量做了ADF检讨,此中惟独X3中美通胀差在一阶滞后的情形下不具有单元根,为一阶差分安稳序列,ADF检讨以下:

    ?

    表22

    ADF demo Statistic?-4.367672???? 1%?? Critical Value*?-5.1152

    ????? 5%?? Critical Value?-3.9271

    ????? 10% Critical Value?-3.4104

    *MacKinnon critical values for rejection of hypothesis of a unit root.

    ????

    ????

    Augmented Dickey-Fuller demo Equation

    Dependent Variable: D(X3)

    Method: Least Squares

    Date: 06/14/05?? Time: 21:03

    Sample(adjusted): 1993 2003

    Included observations: 11 after adjusting endpoints

    Variable?Coefficient?Std. Error?t-Statistic?Prob.?

    X3(-1)?-0.753242?0.172458?-4.367672?0.0033

    D(X3(-1))?0.633474?0.179702?3.525143?0.0097

    C?-11.96263?3.614666?-3.309469?0.0130

    @TREND(1991)?1.351285?0.427940?3.157650?0.0160

    R-squared?0.794871???? Mean dependent var?0.538182

    Adjusted R-squared?0.706959???? S.D. dependent var?5.768287

    S.E. of regression?3.122562???? Akaike info criterion?5.390472

    Sum squared resid?68.25274???? Schwarz criterion?5.535161

    Log likelihood?-25.64760???? F-statistic?9.041642

    Durbin-Watson stat?2.679921???? Prob(F-statistic)?0.008342

    由X3的趋向图也能够看出序列为安稳的。

    2、对X1 X2 X3在不滞后的情形下做协整

    表23

    Date: 06/14/05?? Time: 20:35

    Sample: 1991 2003

    Included observations: 12

    demo assumption: Quadratic deterministic trend in the data?????

    Series: Y X1 X2 X3

    Lags interval: No lags

    ?Likelihood?5 Percent?1 Percent?Hypothesized?

    Eigenvalue?Ratio?Critical Value?Critical Value?No. of CE(s)?

    ?0.964518? 58.14258? 54.64? 61.24?????? None *

    ?0.610537? 18.07778? 34.55? 40.49??? At most 1

    ?0.429494? 6.761949? 18.17? 23.46??? At most 2

    ?0.002262? 0.027169?? 3.74?? 6.40??? At most 3

    ?*(**) denotes rejection of the hypothesis at 5%(1%) significance level?????

    ?L.R. demo indicates 1 cointegrating equation(s) at 5% significance level?????

    ?????

    ?Unnormalized Cointegrating Coefficients:

    Y?X1?X2?X3??

    ?0.000424? 0.005201?-4.79E-07? 0.008502??

    -0.000761? 0.003525? 3.06E-07?-0.011742??

    -0.000896? 0.001002? 1.17E-07? 0.042778??

    -0.002568? 0.002131? 1.07E-07?-0.003806??

    ?????

    ?Normalized Cointegrating Coefficients: 1 Cointegrating Equation(s)?????

    Y?X1?X2?X3?@TREND(92)?C

    ?1.000000? 12.26115?-0.001130? 20.04555?-1285.835?-6047.997

    ? (4.95146)? (0.00039)? (10.0885)??

    ?????

    ?Log likelihood?-335.4706????

    ?????

    ?Normalized Cointegrating Coefficients: 2 Cointegrating Equation(s)?????

    Y?X1?X2?X3?@TREND(92)?C

    ?1.000000? 0.000000?-0.000601? 16.69526?-12.12765? 184.7054

    ?? (0.00031)? (15.4066)??

    ?0.000000? 1.000000?-4.31E-05? 0.273244?-103.8816?-508.3293

    ?? (2.7E-05)? (1.35043)??

    ?????

    ?Log likelihood?-329.8126????

    ?????

    ?Normalized Cointegrating Coefficients: 3 Cointegrating Equation(s)?????

    Y?X1?X2?X3?@TREND(92)?C

    ?1.000000? 0.000000? 0.000000?-74.63914? 86.11620?-1051.547

    ??? (89.9912)??

    ?0.000000? 1.000000? 0.000000?-6.278011?-96.83471?-597.0035

    ??? (9.47482)??

    ?0.000000? 0.000000? 1.000000?-151882.0? 163371.8?-2055791.

    ??? (173945.)??

    ?????

    ?Log likelihood?-326.4452????

    3、GRANGER因果检讨:

    Y和X1做因果别离滞后一阶,二阶,三阶,了局以下:

    表24

    Pairwise Granger Causality demos

    Date: 06/14/05?? Time: 20:40

    Sample: 1991 2003

    Lags: 1

    ? Null Hypothesis:?Obs?F-Statistic?Probability

    ? X1 does not Granger Cause Y?12? 4.76033? 0.05701

    ? Y does not Granger Cause X1? 0.65357? 0.43969

    表25

    Pairwise Granger Causality demos

    Date: 06/14/05?? Time: 20:42

    Sample: 1991 2003

    Lags: 2

    ? Null Hypothesis:?Obs?F-Statistic?Probability

    ? X1 does not Granger Cause Y?11? 0.80069? 0.49179

    ? Y does not Granger Cause X1? 8.04778? 0.02002

    表26

    Pairwise Granger Causality demos

    Date: 06/14/05?? Time: 20:42

    Sample: 1991 2003

    Lags: 3

    Null Hypothesis:?Obs?F-Statistic?Probability

    X1 does not Granger Cause Y?10?0.08139?0.96575

    Y does not Granger Cause X1?11.8179?0.03612

    Y和X2做因果别离滞后一阶,二阶,三阶,了局以下:

    表27

    Pairwise Granger Causality demos

    Date: 06/14/05?? Time: 20:42

    Sample: 1991 2003

    Lags: 1

    ? Null Hypothesis:?Obs?F-Statistic?Probability

    ? X2 does not Granger Cause Y?12? 1.85629? 0.20617

    ? Y does not Granger Cause X2? 3.66478? 0.08785

    表28

    Pairwise Granger Causality demos

    Date: 06/14/05?? Time: 20:42

    Sample: 1991 2003

    Lags: 2

    ? Null Hypothesis:?Obs?F-Statistic?Probability

    ? X2 does not Granger Cause Y?11? 1.40840? 0.31515

    ? Y does not Granger Cause X2? 3.60525? 0.09369

    表29

    Pairwise Granger Causality demos

    Date: 06/14/05?? Time: 20:43

    Sample: 1991 2003

    Lags: 3

    ? Null Hypothesis:?Obs?F-Statistic?Probability

    ? X2 does not Granger Cause Y?10? 2.64298? 0.22294

    ? Y does not Granger Cause X2? 6.20495? 0.08403

    Y和X3做因果别离滞后一阶,二阶,三阶,了局以下:

    表30

    Pairwise Granger Causality demos

    Date: 06/14/05?? Time: 20:44

    Sample: 1991 2003

    Lags: 1

    ? Null Hypothesis:?Obs?F-Statistic?Probability

    ? X3 does not Granger Cause Y?12? 1.60216? 0.23738

    ? Y does not Granger Cause X3? 0.45220? 0.51818

    表31

    Pairwise Granger Causality demos

    Date: 06/14/05?? Time: 20:44

    Sample: 1991 2003

    Lags: 2

    ? Null Hypothesis:?Obs?F-Statistic?Probability

    ? X3 does not Granger Cause Y?11? 1.11025? 0.38883

    ? Y does not Granger Cause X3? 0.68539? 0.53940

    表32

    Pairwise Granger Causality demos

    Date: 06/14/05?? Time: 20:44

    Sample: 1991 2003

    Lags: 3

    ? Null Hypothesis:?Obs?F-Statistic?Probability

    ? X3 does not Granger Cause Y?10? 4.36351? 0.12875

    ? Y does not Granger Cause X3? 0.43159? 0.74596

    ?

    汇总了局以下:

    在二阶、三阶滞后的情形的下X1是Y的缘由,而Y不是X1的缘由。

    在一阶、二阶、三阶滞后的情形的下X2是Y的缘由,而Y不是X2的缘由。

    在一阶、二阶、三阶滞后的情形的下X3和Y都互为因果。

    五、回归了局剖析

    ?Y = -44.67290+ 0.242456*X1 + 0.000102*X2 + 1.714332*X3 +U

    (一)、内债累赘X1

    ?模子阐明

    顺叙,我海内债每添加1亿美圆,就会惹起本钱外逃添加2424万美圆,内债的添加作为一项次要的资金起源对本钱外逃起到了较较着的鞭策性作用。

    (二)、本钱市场危险X2

    ?模子阐明

    顺叙,我国股市颠簸率每添加100%,就会惹起本钱外逃添加100万美圆,在回归模子的各个参数中,股市颠簸率的回归后果很好。一种也许的阐明

    顺叙是我国的证券市场尤其是股票市场还不成熟,股价的颠簸很容易成为影响人们对经济走势预期的要素,从而惹起本钱向本国流出。

    (三)、中美通胀率差X3

    ?模子阐明

    顺叙,中美通胀率差每扩大1%,就会惹起本钱外逃添加1.714亿美圆,本钱外逃与中美通胀率差的正向关连合乎模子设定,然而T检讨不较着,缘由次要是97年之后中美通胀率差产生逆转,本钱外逃有所缓解形成的。

    (四)、内外资的不同回报

    ?经由进程对模子举行批改删去了X4这一变量,缘由在于FDI和本钱外逃之间具有彼此阐明

    顺叙的关连。1995年联合国贸发会议(UNCTAD)估量,中国所排汇的外商直接投资中,大约有20%是先投到外洋再曲折投入海内的本钱。据外洋学者估量这类“曲折投资”(Round-tripping)的领域约为GDP的1/4—1/3(刘信一,1997)。可见在一定水平上是本钱外逃惹起了FDI虚增,而并非单纯的FDI阐明

    顺叙了本钱外逃,阐明

    顺叙变量与被阐明

    顺叙变量的关连并不很合乎经济意思,以是终极的模子中删去了FDI。

    参考文献

    庞皓,《计量经济学》,东北财经大学出版社,2002年8月第2版

    陈鹤,《俄罗斯的本钱外逃及所引发的思考》《国际经济合作》,2000年第1期

    韩继云,《中国本钱外逃的现状.成因与防治战略》,《改造》,19991120

    胡援成,《中国本钱外逃问题再思考》,《摩登财经》,2001年第4期

    孟昊,《影响中国本钱外逃要素的定量剖析》,《古代财经》,2001年第5期

    任惠,《中国本钱外逃揭秘》, 经济日报,2001年7月10日

    宋文兵,《中国的本钱外逃问题研讨:1987-1997》,《经济研讨》,1999年第5期

    修晶、张明:《中国本钱外逃的领域测算和要素剖析》,《世界经济文汇》,2002年第一期

    王廷惠,《俄罗斯本钱外逃:缘由、影响及政府的对策》《国际金融研讨》,2000年第10期

    后记

    ?经由教室听议和上机练习,咱们的计量经济论文底稿已实现,但因为学问水平无限,这一底稿中仍然具有不少完善以至过错,咱们将会继承对它举行批改

    休学加工,心愿在这一进程中能更结壮地把握计量经济学学问,实现深造目的。

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