VAR案例分析报告

VAR模型的应用举例案例分析的目的股市对居民储蓄存款存在分流的作用。一般来说，若股市出现牛市，资金会从存款性金融机构流向股市，居民储蓄存款下降或者增速会减缓。从当前我国经济发展趋势来看，居民储蓄存款与股市交易额均呈上升趋势。那么两者是否存在相互影响呢？本案例将分析居民储蓄与股市之间的这种联动效应。实验数据本实验选取从 1996年到2008年4月的月度数据。整理如下。表1 股市交易额与居民存款余额 单位：亿元沪深股市居民储蓄沪深股市交居民储蓄存日期日期易总额款余额交易总额存款余额1996.1143.493230356.542002.43114.39279728.21996.263.9416432026.252002.51924.59480394.31996.3217.75633296.482002.64303.07681711.791996.4873.422134018.532002.73227.30582527.91996.51163.7434622.12002.81946.36683275.971996.61812.86235457.912002.91460.39184139.051996.7 2718.62 36048.67 2002.10 1181.989 84725.131996.81685.59136705.82002.111908.61785693.491996.91862.16437085.172002.121781.22886910.651996.104012.437671.422003.13060.93390677.631996.113818.9737917.262003.21666.80292824.211996.124238.9238520.82003.32120.63794567.841997.11829.17939038.182003.45848.29795194.121997.21493.92540869.122003.53209.4596351.671997.34263.841580.972003.62557.1597674.571997.45127.9242112.162003.72351.91798590.91997.54640.9642295.162003.81519.42799255.581997.63080.9542771.162003.91661.203100888.61997.72415.7343312.52003.101615.799101381.91997.81858.37843914.922003.112824.502102235.41997.91625.71344139.452003.124359.423103617.71997.102081.67344720.332004.13649.76109232.71997.111586.9545068.432004.27215.755110646.41997.121570.19546279.82004.35792.181111872.21998.11716.977464832004.45270.107112175.41998.21171.90648537.542004.51823.421112610.21998.31703.31248686.482004.62627.057113792.51998.43624.4448984.62004.72548.009114253.21998.53322.3497002004.81880.649114489.61998.62593.349949.892004.93978.274115458.71998.71761.80550749.822004.102893.0651160011998.81497.88250900.912004.113045.002117617.91998.92149.00851580.742004.122094.784119555.41998.101861.7952247.772005.11754.347122237.31998.112194.2452952.322005.22021.07127823.41998.121040.05853407.52005.33047.432129259.41999.11138.81954293.672005.43036.061129816.81999.2334.019656767.452005.51497.376130577.41999.31757.27757814.652005.63177.354132339.11999.42063.44558369.072005.72243.73133656.41999.52719.5958967.842005.84776.3331345051999.69562.5959173.482005.94132.053136316.31999.75538.0259147.552005.102097.632136827.11999.83665.4759187.262005.112301.058138504.31999.92705.1259364.312005.122350.0861410511999.101250.08759269.92006.13635.936148008.41999.111558.04959185.382006.23726.141151179.61999.121482.01359621.82006.34074.6251528192000.14443.45860241.82006.47308.7261534012000.26621.81962270.32006.510926.12153523.42000.38877.35562492.292006.69159.456154996.92000.45960.92662536.122006.78197.536155131.92000.54298.7162195.392006.85526.955156282.12000.66251.17762842.382006.96705.497158108.92000.75436.68662841.52006.106793.858158033.42000.86650.38762861.112006.1110586.65159716.72000.93167.35963243.272006.1215861.8161587.32000.102706.93163122.342007.126191.65161968.62000.115235.81863492.062007.217845.01171042.62000.123985.7964332.382007.332526.3172607.72001.13161.01666547.312007.449865.94170932.72001.22055.59167343.362007.559864.231680402001.35368.46568365.132007.655444.85169651.62001.45845.64668618.462007.733764.63169567.22001.54752.868393.542007.855638.96169171.52001.65190.08669628.582007.947008.27169038.12001.73344.07469677.772007.1035870.9163957.62001.82677.71170558.482007.1125750.72166561.12001.102147.89271818.812007.1229632.95172616.12001.122193.07173762.432008.147340.36174347.92002.1 2072.056 74953.71 2008.2 21457.51 183960.22002.2 1341.433 78114.33 2008.3 29058.96 187414.92002.3 4917.915 78728.3 2008.4 27832.14 188389.14.3VAR 模型的构建 数据平稳性检验考虑到本例中的数据是宏观经济月度数据，先消除季节性特征后再进行分析。另外数据变动趋势过大，本例还对数据进行了对数平滑处理。下图是两个变量经过季节性调整并取对数后的新序列，其中 lsa表示居民储蓄额， ltr表示股市交易总额。在主窗口命令行中输入：genrlsa=log(savingsa)genrltr=log(tradingsa)141210864296 97 98 99 00 01 02 03 04 05 06 07LSD LTA图1 居民储蓄额与股市交易额对数值的对比图根据图形特征选取同时存在截距项和趋势项进行单位根检验。分别在 lsa和ltr窗口中点击view/unitroottest ⋯/。Lsa单位根检验的结果：NullHypothesis:LSAhasaunitrootExogenous:Constant,LinearTrendLagLength:0(AutomaticbasedonSIC,MAXLAG=13)t-StatisticProb.*AugmentedDickey-Fullerteststatistic-3.2957650.0711Testcriticalvalues:1%level-4.0225865%level-3.44111110%level-3.145082*MacKinnon(1996)one-sidedp-values.Ltr单位根检验的结果：NullHypothesis:LTRhasaunitrootExogenous:Constant,LinearTrendLagLengt