请教如何根据BIC图来判断ARMA模型的p,q值

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library(TSA)
res=armasubsets(y=Nile,nar=15,nma=15,y.name='test',ar.method='ols')
plot(res)
运行后就出现这个图，请教高手怎么读得ARMA模型的p,q

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关键词：arma模型 MA模型 ARMA ARM BIC library 模型 如何

同问

同问。好奇

我也在苦苦探索中

我的猜想：
从上往下一行一行看。
比如，第一行只有两个参数，根据BIC最小推荐模型arma(1,0,1)也就是ar1,ma1
第五行可以有6个参数，根据BIC最小推荐ar1,ar4,ar9,ma4,ma12

test-lag1 和error-lag12是最显著的（即方块越黑越显著），那么使得BIC最小的ARMA模型就是ARMA(1，12)

从上往下一行一行看。
比如，第一行只有两个参数，根据BIC最小推荐模型arma(1,0,1)也就是ar1,ma1
越上面，模型越好。但应选择滞后只有1个或没有，误差只有1个或没有的那一行
《时间序列分析及应用：R语言》这本书有介绍

如图：

Order determination is related to the problem of finding the subset of nonzero coefficients of an ARMA model with sufficiently high ARMA orders. A subset ARMA(p,q) model is an ARMA(p,q) model with a subset of its coefficients known to be zero. For example, the model
Yt = 0.8Yt−12 + et + 0.7et−12   (6.5.4)
is a subset ARMA(12,12) model useful for modeling some monthly seasonal time series. For ARMA models of very high orders, such as the preceding ARMA(12,12) model, finding a subset ARMA model that adequately approximates the underlying process is more important from a practical standpoint than simply determining the ARMA orders. The method of Hannan and Rissanen (1982) for estimating the ARMA orders can be extended to solving the problem of finding an optimal subset ARMA model

Indeed, several model selection criteria (including AIC and BIC) of the subset ARMA(p,q) models (2^ (p + q) of them!) can be approximately, exhaustively, and quickly computed by the method of regression by leaps and bounds (Furnival and Wilson, 1974) applied to the subset regression of Yt on its own lags and on lags of the residuals from a high-order autoregression of {Yt}.