>data.new<-data.frame(lncpi,lnneer)
>var<-VAR(data.new,lag.max=2,ic="AIC")
> summary(var)

VAR Estimation Results:
=========================
Endogenous variables: lncpi, lnneer
Deterministic variables: const
Sample size: 135
Log Likelihood: 873.227
Roots of the characteristic polynomial:
0.9621 0.9621 0.3829 0.1847
Call:
VAR(y = data.new, lag.max = 2, ic = "AIC")


Estimation results for equation lncpi:
======================================
lncpi = lncpi.l1 + lnneer.l1 + lncpi.l2 + lnneer.l2 + const

          Estimate Std. Error t value Pr(>|t|)   
lncpi.l1   0.80917    0.08578   9.433   <2e-16 ***
lnneer.l1 -0.09248    0.04705  -1.965   0.0515 .  
lncpi.l2   0.12907    0.08483   1.521   0.1306   
lnneer.l2  0.08381    0.04712   1.779   0.0777 .  
const      0.32677    0.15855   2.061   0.0413 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Residual standard error: 0.007369 on 130 degrees of freedom
Multiple R-Squared: 0.8791,     Adjusted R-squared: 0.8754
F-statistic: 236.3 on 4 and 130 DF,  p-value: < 2.2e-16


Estimation results for equation lnneer:
=======================================
lnneer = lncpi.l1 + lnneer.l1 + lncpi.l2 + lnneer.l2 + const

          Estimate Std. Error t value Pr(>|t|)   
lncpi.l1  -0.18430    0.14954  -1.232 0.220030   
lnneer.l1  1.31275    0.08203  16.003  < 2e-16 ***
lncpi.l2   0.28850    0.14789   1.951 0.053238 .  
lnneer.l2 -0.31998    0.08215  -3.895 0.000156 ***
const     -0.44726    0.27641  -1.618 0.108065   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Residual standard error: 0.01285 on 130 degrees of freedom
Multiple R-Squared: 0.9908,     Adjusted R-squared: 0.9905
F-statistic:  3504 on 4 and 130 DF,  p-value: < 2.2e-16



Covariance matrix of residuals:
           lncpi    lnneer
lncpi  5.430e-05 7.761e-06
lnneer 7.761e-06 1.650e-04

Correlation matrix of residuals:
         lncpi  lnneer
lncpi  1.00000 0.08198
lnneer 0.08198 1.00000


R 运行的结果如上，那么拟合出的VAR方程是多少呢？在线等！