VAR函数的结果怎样分析

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summary(mvar)

VAR Estimation Results:
=========================
Endogenous variables: ic, rd
Deterministic variables: both
Sample size: 67
Log Likelihood: 248.408
Roots of the characteristic polynomial:
0.6021 0.5791 0.5791 0.07685
Call:
VAR(y = lnm.diff, p = 2, type = "both")


Estimation results for equation ic:
===================================
ic = ic.l1 + rd.l1 + ic.l2 + rd.l2 + const + trend

        Estimate Std. Error t value Pr(>|t|)   
ic.l1  0.3136596  0.1104997   2.839  0.00615 **
rd.l1 -1.7886207  0.3932512  -4.548 2.63e-05 ***
ic.l2  0.7772773  0.1753786   4.432 3.96e-05 ***
rd.l2 -0.7246406  0.2597718  -2.790  0.00703 **
const  0.0185852  0.0187965   0.989  0.32669   
trend -0.0001900  0.0004487  -0.423  0.67347   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Residual standard error: 0.06956 on 61 degrees of freedom
Multiple R-Squared: 0.481,        Adjusted R-squared: 0.4385
F-statistic: 11.31 on 5 and 61 DF,  p-value: 9.678e-08


Estimation results for equation rd:
===================================
rd = ic.l1 + rd.l1 + ic.l2 + rd.l2 + const + trend

        Estimate Std. Error t value Pr(>|t|)   
ic.l1  0.3141889  0.0366191   8.580 4.45e-12 ***
rd.l1 -0.0510766  0.1303217  -0.392   0.6965   
ic.l2 -0.0067795  0.0581197  -0.117   0.9075   
rd.l2  0.0262809  0.0860872   0.305   0.7612   
const  0.0130195  0.0062291   2.090   0.0408 *  
trend -0.0002332  0.0001487  -1.568   0.1221   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Residual standard error: 0.02305 on 61 degrees of freedom
Multiple R-Squared: 0.625,        Adjusted R-squared: 0.5942
F-statistic: 20.33 on 5 and 61 DF,  p-value: 6.978e-12



Covariance matrix of residuals:
          ic         rd
ic  0.004838 -0.0002840
rd -0.000284  0.0005313

Correlation matrix of residuals:
        ic      rd
ic  1.0000 -0.1772
rd -0.1772  1.0000



Estimation results for equation ic:
===================================
ic = ic.l1 + rd.l1 + ic.l2 + rd.l2 + const + trend

        Estimate Std. Error t value Pr(>|t|)   
ic.l1  0.3136596  0.1104997   2.839  0.00615 **
rd.l1 -1.7886207  0.3932512  -4.548 2.63e-05 ***
ic.l2  0.7772773  0.1753786   4.432 3.96e-05 ***
rd.l2 -0.7246406  0.2597718  -2.790  0.00703 **
const  0.0185852  0.0187965   0.989  0.32669   
trend -0.0001900  0.0004487  -0.423  0.67347   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


Residual standard error: 0.06956 on 61 degrees of freedom
Multiple R-Squared: 0.481,        Adjusted R-squared: 0.4385
F-statistic: 11.31 on 5 and 61 DF,  p-value: 9.678e-08



Covariance matrix of residuals:
          ic         rd
ic  0.004838 -0.0002840
rd -0.000284  0.0005313

Correlation matrix of residuals:
        ic      rd
ic  1.0000 -0.1772
rd -0.1772  1.0000

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关键词：R函数 VaR correlation Estimation covariance