Question about The results of sjd.vecm1.ols

classic Classic list List threaded Threaded
1 message Options
Reply | Threaded
Open this post in threaded view
|

Question about The results of sjd.vecm1.ols

veralybb
This post has NOT been accepted by the mailing list yet.
When I do Johansen co integration test, using urca package
 first,  I use  ca.jo

> sjd.vecm1=ca.jo(sjd,type = c("trace"), ecdet = c("const"), K = 2,season = NULL, dumvar = NULL)
> summary(sjd.vecm1)

######################
# Johansen-Procedure #
######################

Test type: trace statistic , without linear trend and constant in cointegration

Eigenvalues (lambda):
[1] 7.399724e-02 1.387206e-03 7.792703e-19

Values of teststatistic and critical values of test:

           test 10pct  5pct  1pct
r <= 1 |   2.61  7.52  9.24 12.97
r = 0  | 147.30 17.85 19.96 24.60

Eigenvectors, normalised to first column:
(These are the cointegration relations)

                op.l2         pc.l2      constant
op.l2       1.0000000  1.000000e+00       1.00000
pc.l2      -0.9846721  6.378036e+00      11.30855
constant -228.6018916 -1.224528e+05 -437607.90811

Weights W:
(This is the loading matrix)

           op.l2         pc.l2      constant
op.d -0.08647653 -0.0003358313  2.222901e-18
pc.d  0.12198245 -0.0003137395 -3.583104e-18

what do op.l1 and op.l2 represent?

and then I use cajools to  return the OLS regressions of an unrestricted VECM




 sjd.vecm1.ols=cajools(sjd.vecm1)
> summary(sjd.vecm1.ols)
Response op.d :

Call:
lm(formula = op.d ~ op.dl1 + pc.dl1 + op.l2 + pc.l2 + constant -
    1, data = data.mat)

Residuals:
    Min      1Q  Median      3Q     Max
-922.22  -56.28    4.57   64.33  809.46

Coefficients:
         Estimate Std. Error t value Pr(>|t|)  
op.dl1    0.07283    0.03725   1.955  0.05073 .
pc.dl1    0.09089    0.03718   2.445  0.01460 *
op.l2    -0.08681    0.02741  -3.168  0.00156 **
pc.l2     0.08301    0.02702   3.072  0.00216 **
constant 60.89219   28.05725   2.170  0.03011 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 167.4 on 1877 degrees of freedom
Multiple R-squared: 0.02936,    Adjusted R-squared: 0.02678
F-statistic: 11.36 on 5 and 1877 DF,  p-value: 8.188e-11


Response pc.d :

Call:
lm(formula = pc.d ~ op.dl1 + pc.dl1 + op.l2 + pc.l2 + constant -
    1, data = data.mat)

Residuals:
     Min       1Q   Median       3Q      Max
-1192.00   -45.63     0.26    47.79  2014.66

Coefficients:
         Estimate Std. Error t value Pr(>|t|)    
op.dl1    0.58962    0.03625  16.265  < 2e-16 ***
pc.dl1   -0.41460    0.03618 -11.459  < 2e-16 ***
op.l2     0.12167    0.02667   4.562 5.39e-06 ***
pc.l2    -0.12211    0.02629  -4.644 3.65e-06 ***
constant 10.53286   27.30228   0.386      0.7    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 162.9 on 1877 degrees of freedom
Multiple R-squared: 0.127,      Adjusted R-squared: 0.1247
F-statistic: 54.61 on 5 and 1877 DF,  p-value: < 2.2e-16

what is pc.dl1?