由弹性价格货币模型论中国汇率和利率的联动性

1计量经济课程论文----由弹性价格货币模型论中国汇率和利率的联动性于中国这种汇率并分析了背后的原因。弹性价格货币模型，汇率，实际国民收入水平，利率水平，货币供给水平导论汇率决定理论是西方外汇理论的核心，也一直是国际经济学中最为活跃的领域之一。随着世界经济的变化和国际货币体制的变迁，汇率决定理论也在不断地发展货币模型是西方汇率决定理论中资产市场分析法的一个重要的分支。其中资产市场分析法是20世纪七十年代中期开始迅速成长起来的汇率决定理论。货币法(MonetaryApproach)和资产组合平衡法(PortfolioBalanceApproach)是资产市场法的两个主要的分支。货币2是粘性价格货币模型(Sticky-PriceMonetaryModel)。我们检验的重点就是弹性价格货币经济解释一、弹性价格货币模型1．弹性价格货币模型的基本思想n等人。它是在1975年瑞典斯德哥尔摩附近召开的关于“浮动汇率与稳定政策”的国际研讨会此汇率水平应主要由货币市场的供求状况决定。2．弹性货币法的论述(1)稳定的货币需求方程，即货币需求同某些经济变量存在着稳定的关系；(2)购买力平价持续有效。S=α(y*-y)+β(i-i*)+(Ms-Ms*)水造成本国货币的贬值数据收集实际利率，货币供给M1，M2。现在的问题是M1,M2都是衡量货币供给的指标，应当选哪个？我们选择了M2.因为在FredericS.Mishkin(米什金)的《TheEconomicsofMoney,3M1=Currency+Traveler’schecks+Demanddeposits+OthercheckabledepositsM2=M1+Smalldenominationtimedeposits+savingsdepositsandmoneymarketdepositaccounts+Moneymarketmutualfundshares作者在p560写道:”TherelativestabilityofM2velocitysuggeststhatmoneydemandfunctionsinwhichthemoneysupplyisdefinedasM2mightperformedsubstantiallybetterthanthoseinwhichthemoneysupplyisdefinedasM1.”美美国实际利.529701.79874.6572562.45.984.4447美国国民收55985.6557264.0259578.4462681.0164121.7264915.7365061.6566602.9968257.0171056.987332275932.5279473.8883736.6587407.5891502.37收20405.9923502.6726798.6730525.2533496.536830.4840400.2543205.346178.0549249.852826.994874.96348.67957.49602.124327.335680.846920.360743.576095.391867.81.06.23.5428010.4830980.8331939.333946.5435906.3837674.3638900.3639547.6440137.5840153.5142418.0345010.0247968.6452809.7757122.2261088.8669677.6672688.63S2.94457272774.78325176623531292828282828.02172.72.792.334999369224.864.6162年度200020012002s,y,IMs是指汇率，实际国民收入，货币供给量的自然对数值，于是作如下数据处理：(s=lnS,y=lnY,i=I,m=lnM)4年度s0784095812383742313137236683137236683270750015644405476714733037065646237509374752.1540850852.122261539117459609y*-y2.6075762.6897742.686492.6210392.612042.8144572.830982.74822.6858652.9990132.9056812.840979ii-0.0854-0.028-0.0229-0.0849-0.0446-0.0238-0.0286-0.0304-0.0664-0.124-0.075-0.0227mm-2.82689-2.82351-2.70346-2.57653-2.47498-2.5068-2.40937-2.19247-1.86864-1.99834-1.76317-1.5923651150499692.7916420.014-1.465251138429682.7755570.0198-1.421261138429682.751920.0177-1.362920001138429682.729797-0.0212-1.3138120011138429682.6631860.0017-1.3052320021138429682.5980120.0215-1.17004y一阶差分，滞后0期ADFTestStatistic-4.4857741%Critical-4.6712Value*5%CriticalValue-3.734710%CriticalValue-3.3086*MacKinnoncriticalvaluesforrejectionofhypothesisofaunitroot.AugmentedDickey-FullerTestEquationDependentVariable:D(Y,2)Method:LeastSquaresDate:06/14/05Time:09:35Sample(adjusted):19872002Includedobservations:16afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.DY-1))-1.2116820.270117-4.4857740.0006C0.0568010.0658300.8628410.4039@TREND(1985)-0.0065050.006251-1.0407610.3170uaredAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihood0.607580Meandependentvar0.5472080.1116480.162049S.D.dependentvarAkaikeinfocriterionSchwarzcriterionatistic-0.0092110.165921-1.379568-1.2347076ADFTestStatistic-6.0988751%CriticalValue*-4.73155%CriticalValue-3.761110%CriticalValue-3.3228*MacKinnoncriticalvaluesforrejectionofhypothesisofaunitroot.AugmentedDickey-FullerTestEquationDependentVariable:D(M,3)Method:LeastSquaresDate:06/14/05Time:09:36Sample(adjusted):19882002Includedobservations:15afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.I的单位根检验ADFTestStatistic-4.4092171%Critical-4.8025Value*5%CriticalValue-3.792110%CriticalValue-3.3393*MacKinnoncriticalvaluesforrejectionofhypothesisofaunitroot.AugmentedDickey-FullerTestEquationDependentVariable:D(I,3)Method:LeastSquaresDate:06/14/05Time:09:29E一阶Sample(adjusted):19892002Includedobservations:14afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.ADFTestStatistic-3.4153881%CriticalValue*-4.731575%CriticalValue-3.761110%CriticalValue-3.3228*MacKinnoncriticalvaluesforrejectionofhypothesisofaunitroot.AugmentedDickey-FullerTestEquationDependentVariable:D(E,2)Method:LeastSquaresDate:06/14/05Time:09:13Sample(adjusted):19882002Includedobservations:15afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.DE-1))-1.4804320.433459-3.4153880.0058DE-1),2)0.2971920.2855761.0406770.3204C0.2043620.0983912.0770420.0620@TREND(1985)-0.0119810.007830-1.5301470.1542uared0.610438Meandependentvar-0.005023AdjustedR-squared0.504194S.D.dependentvar0.168304S.E.ofregression0.118509Akaikeinfocriterion-1.204485Sumsquaredresid0.154487Schwarzcriterion-1.015671Loglikelihood13.03364F-statistic5.745612Durbin-Watsonstat2.071058Prob(F-statistic)0.012930E与m2互为因果PairwiseGrangerCausalityTestsDate:06/14/05Time:09:23Sample:19852002Lags2NullHypothesis:sisticbabilityMdoesnotGrangerCauseEEdoesnotGrangerCauseM6.993920.000940.0109789898E与y：互不为因果PairwiseGrangerCausalityTestsDate:06/14/05Time:09:28Sample:19852002Lags:1NullHypothesis:sisticbabilityYdoesnotGrangerCauseEEdoesnotGrangerCauseY0.938130.090720.349200.76769E与I互不为因果PairwiseGrangerCausalityTestsDate:06/15/05Time:11:50Sample:19852002Lags3NullHypothesis:sisticbabilityIdoesnotGrangerCauseEEdoesnotGrangerCauseI0.441950.831040.729430.51325参数的估计最小二乘回归得DependentVariable:EMethod:LeastSquaresDate:05/18/04Time:21:02Sample:19852002Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C-0.6177650.246003-2.5112040.0249Y0.08533415.117310.0000I-0.1422620.263086-0.5407440.5972M0.5754300.01836631.330850.0000uared0.993250MeandependentvarAdjustedR-squared0.991804S.D.dependentvar0.385816S.E.ofregression0.034928Akaikeinfocriterion-3.67790Sumsquaredresid0.017080Schwarzcriterion-3.48004Loglikelihood37.10118atistic686.7379Durbin-WatsonstatProb(F-statistic)0.0000009线性关系显著(由F统计量得知),R2=0.993250说明拟合优度很好，理回归所得的I的系数符号与经济意义不符，其他变量经济意义符合计量经济学检验IYMI0-0.2387630.526896Y-0.238763000.176456M0.5268960.176456DependentVariable:EMethod:LeastSquaresDate:05/18/04Time:13:08Sample:19852002Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C-3.2696842.051116-1.5941000.1305Y0.7475272.4638620.0255uaredAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-Watsonstat0.2750540.2297450.3386081.834484-4.9884190.115207MeandependentvarS.D.dependentvarAkaikeinfocriterionSchwarzcriterionF-statisticProb(F-statistic)0.3858160.7764910.8754216.0706150.025456DependentVariable:EMethod:LeastSquaresDate:05/18/04Time:13:08Sample:19852002Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.CJ.88\8030.JJse3eJe.\e0J\I0.Jses??0.0\Je?30.3\J\s80.?e3J3ss.sJ0?JsJ.0es0e3-e.ee8J?ss.3Js8?80.s\J0eQCWs.??8Q0\0.Js8QJ3s3.33ss80.eJ300\0.0esJ8s?.8Q8300?\J8e08J.J?寸sJ?CW人0.sJe\030.00\JJ\.J0?s81varAdjustedR-squared0.992191S.D.dependentvar0.385816S.E.ofregression0.034095Akaikeinfocriterion-3.76834Sumsquaredresid0.017437Schwarzcriterion-3.61995Loglikelihood36.91514atisticDurbin-WatsonstatProb(F-statistic)0.000000DependentVariable:EMethod:LeastSquaresDate:05/18/04Time:13:20Sample:19852002Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.CMI3.0702070.677111-1.7193330.1273270.0687210.97115624.112849.853034-1.7703980.00000.00000.0970uaredAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-Watsonstat0.883072Meandependentvar0.8674820.1404490.295887S.D.dependentvarAkaikeinfocriterionSchwarzcriterionatisticProb(F-statistic)0.385816-0.936939-0.78854456.642230.000000DependentVariable:EMethod:LeastSquaresDate:05/18/04Time:13:20Sample:19852002Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.CMY-0.6750750.216703-3.1152040.00710.5695180.01440539.536540.00001.3083240.0764690.0000R-squared0.993109Meandependentvar1.780155AdjustedR-squared0.992191S.D.dependentvar0.385816S.E.ofregression0.034095Akaikeinfocriterion-3.768349Sumsquaredresid0.017437Schwarzcriterion-3.619953Loglikelihood36.91514sticDurbin-WatsonstatProb(F-statistic)0.000000检验做ARCH(P=3)检验：ARCHTest:ticObs*R-squared5.4618018.974892ProbabilityProbability0.0151830.029627TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:06/13/05Time:20:26Sample(adjusted):19882002Includedobservations:15afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C0.0001540.0003180.4821940.6391RESID^2(-1)0.3298393.1498420.0092RESID2(-2)0.1003790.4230710.2372630.8168RESID2(-3)-0.0743750.227784-0.3265170.7502uared0.598326Meandependentvar0.000925AdjustedR-squared0.488779S.D.dependentvar0.000874S.E.ofregression0.000625Akaikeinfocriterion-11.69368Sumsquaredresid4.30E-06Schwarzcriterion-11.50487Loglikelihood91.70261atistic5.461801Durbin-WatsonstatProb(F-statistic)0.015183redPWhiteHeteroskedasticityTest:ticObs*R-squared2.788935ProbabilityProbability0.0669260.092780TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:06/13/05Time:20:46Sample:19852002Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C0.0936890.1994970.4696270.6478M0.0068670.0051870.21240.0015060.0012490.2533I0.0021660.0149630.1447480.88750.1060440.1676490.6325360.5400Y-0.0586970.145379-0.4037520.6941Y^20.0099880.0263640.3788650.7120uaredAdjustedR-squaredS.E.ofregressionSumsquaredresidLoglikelihoodDurbin-Watsonstat0.603369Meandependentvar0.3870250.0007466.12E-06S.D.dependentvarAkaikeinfocriterionSchwarzcriterionatisticProb(F-statistic)0.0009490.000952-11.27944-10.933182.7889350.066926所有参数的估计量对应的T值都小于二，所以结果还是显示无异方差。故可以认为不存在异方差3．相关检验DependentVariable:DEMethod:LeastSquaresDate:06/13/05Time:21:10Sample(adjusted):19862002Includedobservations:17afteradjustingendpointsVariableCoefficientStd.Errort-StatisticProb.C-0.4419350.5610080.1722450.022233-2.56573225.232880.02350.000055DI-0.1592940.268378-0.5935440.563055DY1.2918970.09039814.291180.0000uared0.985570MeandependentvarAdjustedR-squared0.982240S.D.dependentvar0.239952S.E.ofregression0.031977Akaikeinfocriterion-3.84524Sumsquaredresid0.013293Schwarzcriterion-3.64919Loglikelihood36.68458atistic295.9691Durbin-WatsonstatProb(F-statistic)0.000000修正后不存在自相关模型经济意义解释及存在的问题通过以上的检验修正，我们发现无论如何修正,利率I对汇率e的关联度很低。经过翻阅大量的相关书籍及请教相关老师，我们分析得到如下经济原因：年底实现了人民币经常项目下的可自由兑换，但离人民币真正自由兑换还相差甚远。外汇管人民币汇率和利率相关率低的一个根本原因。程较慢，远滞后于汇率市场化，利率管理权限过于集中，资金价格不能随市场资金供求关系金，专业银行包企业资金，结果使资金杠杆作用微弱。可以说，现行利率的市场属性还很不充分，难以与汇率相配合。经验教训选题在实际做论文时就因为只考虑到便于做计量经济学课程论文而盲目选择了一个从未接触过的模型。对模型的不了解，导致在做的过程中遇见了非常多的问题。模型的理解来理解目标模型。从我们惨痛的经历中，我们总结出，应从以下角度认识一个模型：了购买力平价持续有效的假设而采用了美国和日本的数据，导致的结果是无论我们如何修正回归的模型都与经济意义不符。个变量的确切含义以及应当用什么数据来衡量。我们在做论文时一开始就只根据一个公式：e=α(y*-y)+β(i-i*)+(Ms-Ms*)平以及货币供给水平通过对各自物价水平的影响而决定了汇率水平”主观地认为e， 型明确写着e=lnEy=lnYms=lnM，做了这么久的成果只得再次被全盘否决，感觉就像自己的孩子夭折一样难过。据数据收集老师常说，找数据是百分之七十的工作。