ARIMA模型建立與應(yīng)用

1、 #聽EViews-Equation:UNTITLEDWorkFile:實(shí)鑿一UJntitL=旦11FileEditObjectViewProcQuickOEltionsWindowHelp_n1XViewPb匚Obj已匚t|FTintNam已FreezeEstimateFore匚已st|寶已乜|尺巴引日生DependentVariable:DEUROMethod:LeastSquaresDate:04/12/11Time:10:48Sample(adjusted):1993M032007M12Includedobservations:178afteradjustmentsConvergenc

2、eachievedafter3iterationsVariableCoefficientStd.Errort-StatisticProb.C-0.0009220.002097-0.4398900.6606AR0.3082790.0714844.3125520.0000R-squared0.095572Meandependentvar-.886AdjustedR-squared0.090433S.D.dependentvar0.020291S.E.ofregression0.019352Akaikeinfocriterion-5.040911Sumsquaredresid0.065909Schw

3、arzcriterion-5.005160Loglikelihood450.6411F-statistic18.59810Durbin-Watsonstat1.871573Prob(F-statistic)0.000027InvertedARRoots.31Path=匚:do匚uiti已itsandsettingsadministrdtormydo匚umentsDB=non已WF常數(shù)c的概率太大（0.6606），接受C=0的假設(shè)，所以模型應(yīng)該去掉常數(shù)。QuickEstimateLS(NLSandARMA)在對話框輸入d(euro)ar(1)EViews-Equation:UNTITLEDWor

4、kFile:實(shí)豊一UJntitL.I!FileEditObjectViewProcQuickOptionsWindowHelp_n1XViewProc|Obie匚PrintPJameFreezeEstimateForecast|Stats|ResidsDependentVariable:DEUROMethod:LeastSquaresDate:04/12/11Time:10:52Sample(adjusted):1993M032007M12Includedobservations:178afteradjustmentsConvergenceachievedafter2iterationsVar

5、iableCoefficientStd.Errort-StatisticProb.AR0.3095220.0712654.3432280.0000R-squared0.094579Meandependentvar-.0886AdjustedR-squared0.094579S.D.dependentvar0.020291S.E.ofregression0.019307Akaikeinfocriterion-5.051050Sumsquaredresid0.065982Schwarzcriterion-5.033174Loglikelihood450.5434Durbin-Watsonstat1

6、.871488InvertedARRoots.31模型為：w二0.309522wtt1t=(4.343228)p=(0.0000)w二,-(1L)xttt從p值看，系數(shù)是顯者的。從InvertedARRoots（自回歸特征方程根的倒數(shù)）是0.31，在單位圓之外，說明模型是平穩(wěn)的。但還要對殘差進(jìn)行白噪聲檢驗(yàn)：在QuickEstimateLS（NLSandARMA）在對話框輸入d（euro）ar（1）OK出結(jié)果的頁面上ViewResidualTestsCorrelogram-Q-statistics選K=13（由178/10或178平方根來）Date:04/11/11Time:10:51Sampl

7、e:1993M032007M12Includedobservations:178Q-statisticprobabilitiesadjustedfor1ARMAterm(s)AutocorrelationPartialCorrelationACPACQ-StatProb.|.|.|.|10.0650.0650.7661*|.|*|.|2-0.165-0.1705.73060.017*|.|*|.|3-0.104-0.0847.72760.021.|.|.|.|4-0.021-0.0387.80870.050.|*|.|.|50.0810.0569.01900.061.|.|.|.|60.000

8、-0.0279.01910.108.|.|.|.|70.0310.0519.19580.163.|*|.|*|80.1000.10611.0720.136.|.|.|.|9-0.0020.00111.0730.198.|*|.|*|100.0990.14312.9480.165.|.|.|.|110.0230.03413.0540.221.|.|.|.|12-0.0270.01113.1960.281.|.|.|.|130.0100.03013.2160.354從K=13一行找到Q統(tǒng)計(jì)量值為13.216,相伴概率（記為p-Q）為0.3540.05,接受序列不相關(guān)的假設(shè)，即認(rèn)為殘差序列是白噪聲。

9、類似地，對模型ARIMA（2，1，0）、ARIMA（0，1，1）、ARIMA（1，1，1）、ARIMA（2，1，1）進(jìn)行估計(jì)與檢驗(yàn)。ARIMA（1，1，0），ARIMA（2，1，0）、ARIMA（0，1，1）三個(gè)檢驗(yàn)都通過參數(shù)顯者性檢驗(yàn)，模型平穩(wěn)性和可逆性檢驗(yàn)，殘差序列白噪聲檢驗(yàn)。但是模型ARIMA（1,1，1）、ARIMA（2,1，1）沒有通過檢驗(yàn)。模型評價(jià)與比較模型e1e1e1ARIMA(1,1,0)0.3100.0950.340ARIMA(2,1,0)0.373-0.2020.1260.698ARIMA(0,1,1)0.3850.1220.727RA2和p-Q兩指標(biāo)越大越好，ARIMA(1,1,0)不好，在一樣好的兩模型ARIMA(2,1,0)和ARIMA(0,1,1)中，ARIMA(2,1,0)用自回歸信息預(yù)測，所以在預(yù)測方面ARIMA(2,1,0)明顯好。最終選擇ARIMA(2,1,0)：w二0.373w-0.202wtt1t2w二,-(1L)xttt即(1L)x=0.373(1L)x0.202(1L)xtt1t2x=1.373x0.575x+0.202xtt1t2t3(五)模型外推應(yīng)用已知2007：12,2007：