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帮帮创意 > 其他文档 > 课后习题答案-时间序列分析及应用(r语言原书第2版).pdf

课后习题答案-时间序列分析及应用(r语言原书第2版).pdf

课后习题答案-时间序列分析及应用(r语言原书第2版).pdf
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课后习题答案-时间序列分析及应用(r语言原书第2版),It can be derived that parenleftbiggpi1pi2parenrightbigg=parenleftbigg p21p21+p12p12p21+p12parenrightbigg.(b) Assume {Yt} is stationary with stationary distribution F. Then for every y ∈ R, wehaveF (y?2 ?1 0 1 2?2?101Normal Q?Q PlotTheoretical QuantilesSample QuantilesFigure 10: QQ Normal Plot of Standard ResidualsExercise 15.9(a) Let P = (pij),i,j = 1,2 be the transition probability matrix of Rt.0 5 10 15 20 25?2?101tStandardized Residuals2 4 6 8 10 12 14?0.4?0.20.00.20.4LagACF of Residuals5 10 15 200.00.20.40.60.81.0Number of LagsP?valuesFigure 9: Diag of Lagged Regression of Sqrt TransformeEstimate Std. Error t-statistic p-value?d 1?r 3.873Lower Regime (7)?φ1,0 3.89 1.21 3.22 0.049?φ1,1 1.25 0.32 3.85 0.03?φ1,2 0.38 0.69 0.56 0.61?φ1,3 ?1.41 0.52 ?2.71 0.07?σ21 1.04Upper Regime (21)?φ2,$p.value [1] 0.09923$order [1] 3> pvaluem=NULL> for (d in 1:3){+ res=tlrt(sqrt(hare),p=5,d=d,a=0.25,b=0.75) + pvaluem= cbind(pvaluem, c(d,res$test.statistic, + res$p.value))}> rownames(pvaluem)=c(’d’,0 2 4 6 8 10246810lag?1 regression plotooooo oooooooooooo ooooooo oooooo0 2 4 6 8 10246810lag?2 regression plotoooo oooooooooooo ooooooooooooo0 2 4 6 8 10246810lag?3 regression plotooooooooooooooo ooo0 200 400 600 800 10004681012tY tFigure 6: Simulated Series of the Fitted Model (n = 1000)0.0 0.1 0.2 0.3 0.4 0.55e?021e+005e+01frequencyspectrumSeries: xSmoothed PeriodogramFigure 7: Spectra of SimulExercise 15.4The long-run behavior of the skeleton of the fitted TAR(2;3,4) model for the relative sunspotdata is given below. The fitted model is stationary and its skeleton converges to a l

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