Hi Statalisters,
I'm running a VECM on data coming from a dataset with 87 obs regarding two variables over some years observed on a unique statistical unit (quarterly data). More specifically the variables being used are variable1 and variable2. Now, running the code to fit the VECM I get this:
According to these results all works fine. Now it does not seem unreasonable that there could be some endogeneity bias. In fact, it is not straighforward that the cointegrated vector is explaining adjustments of variable1 to variable2. Due to this, I proceed to impose restrictions on adjustment coefficients (alpha). Before doing this I run the following code to see better alpha coefficients:
The first restriction I test would be alpha=(alpha1, 0) and the second restriction would be alpha=(0,alpha2). The first restriction would imply that variable2 does not error-correct whereas variable1 does. Viceversa in the second restriction (variable1 does error-correct and variable2 does not). I want to test these restrictions for my VECM using likelihood ratio tests. Now, I put the first restriction and I get this error:
Why do I get this error? Are the commands correctly specified?
Many thanks for your helpful suggestions in advance.
Best.
Francesco
I'm running a VECM on data coming from a dataset with 87 obs regarding two variables over some years observed on a unique statistical unit (quarterly data). More specifically the variables being used are variable1 and variable2. Now, running the code to fit the VECM I get this:
Code:
vec variable1 variable2, lag(1) trend(rt)
Vector error-correction model
Sample: 1998q2 - 2019q3 Number of obs = 86
AIC = 2.098512
Log likelihood = -84.23601 HQIC = 2.167425
Det(Sigma_ml) = .0243122 SBIC = 2.269745
Equation Parms RMSE R-sq chi2 P>chi2
----------------------------------------------------------------
D_variable1 2 .46599 0.1386 13.35213 0.0013
D_variable2 2 .37733 0.2107 22.15087 0.0000
----------------------------------------------------------------
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
D_variable1 |
_ce1 |
L1. | -.1296324 .0584052 -2.22 0.026 -.2441045 -.0151603
|
_cons | .1331069 .0509223 2.61 0.009 .033301 .2329129
-------------+----------------------------------------------------------------
D_variable2 |
_ce1 |
L1. | -.2118148 .0472929 -4.48 0.000 -.3045071 -.1191224
|
_cons | -.0814626 .0412337 -1.98 0.048 -.1622792 -.0006459
------------------------------------------------------------------------------
Cointegrating equations
Equation Parms chi2 P>chi2
-------------------------------------------
_ce1 1 9.250962 0.0024
-------------------------------------------
Identification: beta is exactly identified
Johansen normalization restriction imposed
------------------------------------------------------------------------------
beta | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_ce1 |
variable1 | 1 . . . . .
variable2 | .261243 .0858917 3.04 0.002 .0928984 .4295877
_trend | -.1412712 .007956 -17.76 0.000 -.1568647 -.1256777
_cons | -48.29221 . . . . .
------------------------------------------------------------------------------
Code:
vec, alpha nobtable noetable
Vector error-correction model
Sample: 1998q2 - 2019q3 Number of obs = 86
AIC = 2.098512
Log likelihood = -84.23601 HQIC = 2.167425
Det(Sigma_ml) = .0243122 SBIC = 2.269745
Adjustment parameters
Equation Parms chi2 P>chi2
-------------------------------------------
D_variable1 1 4.926337 0.0265
D_variable2 1 20.05953 0.0000
-------------------------------------------
------------------------------------------------------------------------------
alpha | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
D_variable1 |
_ce1 |
L1. | -.1296324 .0584052 -2.22 0.026 -.2441045 -.0151603
-------------+----------------------------------------------------------------
D_variable2 |
_ce1 |
L1. | -.2118148 .0472929 -4.48 0.000 -.3045071 -.1191224
------------------------------------------------------------------------------
Code:
constraint define 1 [D_variable2]L1._ce1=0 . vec variable1 variable2, lag(1) trend(rt) aconstraint(1) Iteration 1: log likelihood = -88.811238 Iteration 2: log likelihood = -88.810546 Iteration 3: log likelihood = -88.81052 Iteration 4: log likelihood = -88.810519 Iteration 5: log likelihood = -88.810518 Iteration 6: log likelihood = -88.810518 Iteration 7: log likelihood = -88.810518 Iteration 8: log likelihood = -88.810518 Iteration 9: log likelihood = -88.810518 Iteration 10: log likelihood = -88.810518 lags() invalid -- invalid numlist has elements outside of allowed range r(125);
Many thanks for your helpful suggestions in advance.
Best.
Francesco
