论文GDP

兰州交通大学毕业设计（论文）-I-摘要国内生产总值(GrossDomesticProduct)是国民经济核算的核心指标。它不仅能从总体上度量国民产出和收入规模，也能从整体上度量经济波动和经济周期状态，成为宏观经济中最受关注的经济数据，被认为是衡量国民经济发展、判断宏观经济运行状况的一个重要指标，也是政府制定经济发展战略和经济政策的重要依据。因此，准确的分析预测GDP具有重要的理论和实际意义。时间序列是指同一种现象在不同时间上的相继观察值排列而成的一组数字序列。时间序列预测方法则是通过时间序列的历史数据揭示现象随时间变化的规律，将这种规律延伸到未来，从而对该现象的未来做出预测。民勤县是典型的农业县，严酷的自然环境和脆弱的工业基础，严重制约着地方经济的发展。因此更应该对当地GDP进行深入分析研究，以便及时了解其经济发展动向，作出有利于提高当地经济水平的相关决策。本文以民勤县1961年至2010年人均GDP数据资料为依据，利用SPSS软件对数据进行时间序列分析，建立时间序列模型，并对模型进行检验，最后利用所建模型对民勤县未来10年的生产总值做出预测。通过分析，得出以下结论：（1）通过SPSS软件对民勤县1961-2010年人均GDP数据建立ARIMA（0,2,12）模型，拟合效果较好，比较准确地对未来10年的数据进行了预测；（2）民勤县实际人均GDP值有着明显的上升趋势，且近年来增速加快，说明该县正处于经济高速发展阶段。关键词：GDP；时间序列；预测；ARIMA模型；趋势分析兰州交通大学毕业设计（论文）-II-AbstractGDP(GrossDomesticProduct)isthecoreofthenationaleconomicaccounting.Itcannotonlyweighthenationalproductsandincomesizeasawhole,butalsoweightheeconomicfluctuationandtheperiodicstatusoftheeconomyingeneral.ThusthedataofGDPhasbecomethemostconcernedeconomicstatisticsinmacroeconomyandisregardedasanimportantindexforassessingthenationaleconomicdevelopmentandforjudgingtheoperatingstatusofmacroeconomy.Besides,itisalsothevitalbasisforgovernmenttosetdowneconomicdevelopmentalstrategiesandeconomicpolicies.Thereforeithasgreattheoreticalandrealisticsignificancetoanalyzeandforecastthiscriterionaccurately.Timeseriesisaseriesofnumberwhichgotbyobservingthesamephenomenonindifferentperiodoftime.Thepredictingwayoftimeseriesisachievedbyexploringthelawsthatphenomenalchangewithtime,inthehistoricalstatisticsoftimeseries.Timeseriesextendthelawstothefuturesoastopredictthefutureofaphenomenon.Minqincountyisatypicalagriculturalcounty,theharshnaturalenvironmentandtheweakfoundationofindusty,seriouslyrestrictsthedevelopmentoflocaleconomy.SoitisthelocalGDPthatshouldbestudiedinadeep-goingway,thentounderstanditseconomicdevelopmenttrendontime,tomakedecisionsthatimprovestheleveloflocaleconomic.BasedonthedataoftheGDPofMinqincountyfrom1961to2010.SPSSsoftwareisappliedinthisthesis.ItfindsthebestofthemodeltomakeapredictiononthegrossproductofMinqincountyinthenexttenyears.Andthisthesismainlydothefollowingaspectsstudies.Ononehand,ItuseSPSSsoftwaretobuildanARIMA(0,2,12)modelfortheGDPofMinqincountyfrom1961to2010,thefittingeffectisbetteranditforecastesthenext10yearsdatawell.Ontheotherhand,theGDPofMinqincountyhasobviousrisingtrend,andacceleratedinrecentyears,itisnothardtoseethatthecountyisinastageofrapideconomicgrowth.Keywords:GDP,Mathoftimeseriesanalysis,Forecating,ARIMAmodel,Trendanalysis兰州交通大学毕业设计（论文）-III-目录摘要.....................................................................................................................................IAbstract.....................................................................................................................................II1绪论........................................................................................................................................11.1研究的目的和意义.....................................................................................................11.2课题研究现状.............................................................................................................11.3本课题的技术路线、组织结构.................................................................................31.3.1技术路线..........................................................................................................31.3.2组织结构..........................................................................................................32时间序列分析的基本理论....................................................................................................42.1时间序列.....................................................................................................................42.1.1时间序列的平稳性..........................................................................................42.1.2时间序列的纯随机性......................................................................................62.1.2时间序列的季节性..........................................................................................72.2差分运算.....................................................................................................................82.2.1差分运算和延迟算子......................................................................................82.2.2线性差分方程..................................................................................................82.2.3差分运算的实质及差分方式的选择..............................................................92.3时间序列的常用模型...............................................................................................102.3.1AR（自回归）模型.......................................................................................102.3.2MA（移动平均）模型..................................................................................132.3.3ARMA（自回归移动平均）模型.................................................................142.3.4ARIMA（求和自回归移动平均）模型........................................................152.4ARIMA（p,d,q）模型的建立...................................................................................172.4.1非平稳序列建