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st: Interpreting VEC output
From
Talal <[email protected]>
To
[email protected]
Subject
st: Interpreting VEC output
Date
Tue, 21 Sep 2010 15:35:03 -0700 (PDT)
Hi,
I have tested for a cointegrating relationship and one exists, so I've estimated a VECM with four variables [ ln_qt , ln_VKM , lnincome , Lnf ]. There are enough observations. I'm just wondering how exactly to rewrite the STATA output into equations, in particular the first equation as it is the modelling of demand (Ln_qt) that I am most interested in.
So what I want to know is: how do I rewrite the output (see below please) and what is the best approach in interpreting the results for demand (Ln_qt) modelling?
Thank you.
. vec ln_qt lnvkm lnincome lnf,
Vector error-correction model
Sample: 1981 - 2008 No. of obs = 28
AIC = -17.34228
Log likelihood = 253.792 HQIC = -17.18228
Det(Sigma_ml) = 1.57e-13 SBIC = -16.81892
Equation Parms RMSE R-sq chi2 P>chi2
D_ln_qt 2 .037116 0.2983 11.05193 0.0040
D_lnvkm 2 .031551 0.1127 3.303018 0.1918
D_lnincome 2 .014566 0.7176 66.06712 0.0000
D_lnf 2 .050123 0.3709 15.32712 0.0005
Coef. Std. Err. z P>z [95% Conf. Interval]
D_ln_qt
_ce1
L1. -.0866026 .241569 -0.36 0.720 -.5600691 .3868639
_cons -.0244341 .007835 -3.12 0.002 -.0397905 -.0090777
D_lnvkm
_ce1
L1. -.3730333 .2053474 -1.82 0.069 -.7755069 .0294403
_cons -.0050647 .0066602 -0.76 0.447 -.0181185 .0079892
D_lnincome
_ce1
L1. -.2016308 .0948057 -2.13 0.033 -.3874465 -.015815
_cons .0186817 .0030749 6.08 0.000 .012655 .0247085
D_lnf
_ce1
L1. -1.140481 .3262244 -3.50 0.000 -1.779869 -.5010932
_cons .0002092 .0105808 0.02 0.984 -.0205287 .0209471
Cointegrating equations
Equation Parms chi2 P>chi2
_ce1 3 4320.164 0.0000
Identification: beta is exactly identified
Johansen normalization restriction imposed
beta Coef. Std. Err. z P>z [95% Conf. Interval]
_ce1
ln_qt 1 . . . . .
lnvkm .3143633 .0472589 6.65 0.000 .2217376 .406989
lnincome .3286365 .0542557 6.06 0.000 .2222974 .4349756
lnf .7688382 .0583292 13.18 0.000 .6545151 .8831614
_cons -8.684245 . . . . .
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