var出力からの抽出係数と分散共分散行列（varsパッケージで推定）

Question

推定varモデル（varsパッケージで推定）の出力から係数と分散共分散行列を抽出します。係数を配列に保存し、var-cov行列を行列に保存する方法があるので、後でこれらの関数から特定の数値を抽出し、後の関数（これは私の最終目標）の入力として使用できます。

これはモック推定と結果です。

sale1_ts <- ts(sale1, frequency=12, start=c(2012,1)) 
sale2_ts <- ts(sale2, frequency=12, start=c(2012,1)) 

#VARMODEL 
varendo<-data.frame(sale1_ts,sale2_ts) 
names(varendo)<-c("sale1_ts","sale2_ts") 
attach(varendo) 

library(vars) 
fitvar<- VAR(varendo, type = c("both"), season = NULL, lag.max = 12,ic = c("AIC")) 
summary(fitvar) 

これは出力

Endogenous variables: sale1_ts, sale2_ts 
Deterministic variables: both 
Sample size: 21 
Log Likelihood: -43.463 
Roots of the characteristic polynomial: 
1.19 1.06 1.06 0.961 0.961 0.96 0.96 0.914 0.914 0.883 0.883 0.84 0.84 0.75 
Call: 
VAR(y = varendo, type = c("both"), lag.max = 12, ic = c("AIC")) 


Estimation results for equation sale1_ts: 
========================================= 
sale1_ts = sale1_ts.l1 + sale2_ts.l1 + sale1_ts.l2 + sale2_ts.l2 + sale1_ts.l3 + sale2_ts.l3 + sale1_ts.l4 + sale2_ts.l4 + sale1_ts.l5 + sale2_ts.l5 + sale1_ts.l6 + sale2_ts.l6 + sale1_ts.l7 + sale2_ts.l7 + const + trend 

      Estimate Std. Error t value Pr(>|t|) 
sale1_ts.l1 -0.0576  0.3603 -0.16  0.88 
sale2_ts.l1 -0.0384  0.3538 -0.11  0.92 
sale1_ts.l2 -0.4768  0.3933 -1.21  0.28 
sale2_ts.l2 0.1963  0.3137 0.63  0.56 
sale1_ts.l3 -0.0837  0.3245 -0.26  0.81 
sale2_ts.l3 -0.2097  0.6108 -0.34  0.75 
sale1_ts.l4 -0.5665  0.3653 -1.55  0.18 
sale2_ts.l4 -0.3511  0.5174 -0.68  0.53 
sale1_ts.l5 -0.1479  0.4199 -0.35  0.74 
sale2_ts.l5 0.1263  0.4640 0.27  0.80 
sale1_ts.l6 -0.3143  0.3684 -0.85  0.43 
sale2_ts.l6 0.1174  0.2700 0.43  0.68 
sale1_ts.l7 -0.4727  0.4810 -0.98  0.37 
sale2_ts.l7 0.3255  0.3071 1.06  0.34 
const  12.5453 13.4200 0.93  0.39 
trend   0.1062  0.0759 1.40  0.22 

Residual standard error: 1.5 on 5 degrees of freedom 
Multiple R-Squared: 0.741, Adjusted R-squared: -0.0376 
F-statistic: 0.952 on 15 and 5 DF, p-value: 0.575 

Estimation results for equation sale2_ts: 
========================================= 
sale2_ts = sale1_ts.l1 + sale2_ts.l1 + sale1_ts.l2 + sale2_ts.l2 + sale1_ts.l3 + sale2_ts.l3 + sale1_ts.l4 + sale2_ts.l4 + sale1_ts.l5 + sale2_ts.l5 + sale1_ts.l6 + sale2_ts.l6 + sale1_ts.l7 + sale2_ts.l7 + const + trend 

      Estimate Std. Error t value Pr(>|t|) 
sale1_ts.l1 -0.7135  0.3899 -1.83 0.127 
sale2_ts.l1 -0.0598  0.3829 -0.16 0.882 
sale1_ts.l2 -0.4054  0.4257 -0.95 0.385 
sale2_ts.l2 -0.0300  0.3395 -0.09 0.933 
sale1_ts.l3 -0.7129  0.3512 -2.03 0.098 . 
sale2_ts.l3 0.6945  0.6611 1.05 0.342 
sale1_ts.l4 -0.7165  0.3954 -1.81 0.130 
sale2_ts.l4 0.4709  0.5600 0.84 0.439 
sale1_ts.l5 0.1324  0.4545 0.29 0.783 
sale2_ts.l5 0.7127  0.5022 1.42 0.215 
sale1_ts.l6 0.2318  0.3987 0.58 0.586 
sale2_ts.l6 -0.0859  0.2922 -0.29 0.781 
sale1_ts.l7 0.4826  0.5206 0.93 0.397 
sale2_ts.l7 0.5011  0.3324 1.51 0.192 
const  -0.6180 14.5252 -0.04 0.968 
trend   0.1744  0.0822 2.12 0.087 . 
--- 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 1.6 on 5 degrees of freedom 
Multiple R-Squared: 0.819, Adjusted R-squared: 0.276 
F-statistic: 1.51 on 15 and 5 DF, p-value: 0.343 


Covariance matrix of residuals: 
     sale1_ts sale2_ts 
sale1_ts  2.14 -1.25 
sale2_ts -1.25  2.51 

Correlation matrix of residuals: 
     sale1_ts sale2_ts 
sale1_ts 1.000 -0.541 
sale2_ts -0.541 1.000 

以下、私が試してみましたプリント（）と係数（につながる）が、これは私に奇妙な結果を取得します。例えば。アクセス係数に

test<-array(coefficients(fitvar), c(16,4,2)) 

> test 
, , 1 

     [,1]  [,2]  [,3]  [,4]  
[1,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[2,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[3,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[4,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[5,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[6,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[7,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[8,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[9,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[10,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[11,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[12,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[13,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[14,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[15,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[16,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 

, , 2 

     [,1]  [,2]  [,3]  [,4]  
[1,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[2,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[3,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[4,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[5,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[6,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[7,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[8,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[9,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[10,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[11,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[12,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[13,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[14,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[15,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 
[16,] Numeric,64 Numeric,64 Numeric,64 Numeric,64 

Answer 1

、あなたは、例えば、名前のベクトルを与える

fitvar$varresult$sale1_ts$coefficients 

を実行する必要がありますmatrixオブジェクト与える

summary(fitvar)$covres 
summary(fitvar)$corres 

共分散/相関使用にアクセスするには

> fitvar$varresult$sale1_ts$coefficients 
sale1_ts.l1 sale2_ts.l1  const  trend 
-0.1799802 -0.1048722 -0.3722163 1.2865624 

：

> summary(fitvar)$covres 
      sale1_ts sale2_ts 
sale1_ts 0.7955669 -0.1140167 
sale2_ts -0.1140167 0.7013895 
> summary(fitvar)$corres 
      sale1_ts sale2_ts 
sale1_ts 1.0000000 -0.1526337 
sale2_ts -0.1526337 1.0000000