I use [TS] varsoc to obtain the optimum lag length for the Granger causality test in Stata. This command reports the optimal number of lags based on different criteria such as Akaike's information criterion (AIC).
Is there any way to store the optimal lag number (obtained based on AIC) in a variable and use it in the next command to estimate causality? Something like this:
Lag= varsoc X Y
tvgc X Y, p(Lag) d(Lag) trend window(30) prefix(_) graph
Here I adapt the first example in the help for varsoc. You can sort the matrix of statistics so that minimum AIC is in the first row, and read off the lag concerned.
. webuse lutkepohl2, clear
(Quarterly SA West German macro data, Bil DM, from Lutkepohl 1993 Table E.1)
. varsoc dln_inv dln_inc dln_consump
Lag-order selection criteria
Sample: 1961q2 thru 1982q4 Number of obs = 87
+---------------------------------------------------------------------------+
| Lag | LL LR df p FPE AIC HQIC SBIC |
|-----+---------------------------------------------------------------------|
| 0 | 696.398 2.4e-11 -15.9402 -15.9059 -15.8552* |
| 1 | 711.682 30.568 9 0.000 2.1e-11 -16.0846 -15.9477* -15.7445 |
| 2 | 724.696 26.028 9 0.002 1.9e-11* -16.1769* -15.9372 -15.5817 |
| 3 | 729.124 8.8557 9 0.451 2.1e-11 -16.0718 -15.7294 -15.2215 |
| 4 | 738.353 18.458* 9 0.030 2.1e-11 -16.0771 -15.632 -14.9717 |
+---------------------------------------------------------------------------+
* optimal lag
Endogenous: dln_inv dln_inc dln_consump
Exogenous: _cons
.
. mata
------------------------------------------------- mata (type end to exit) ---------------
: stats = st_matrix("r(stats)")
: _sort(stats, 7)
: st_numscalar("opt_lag_AIC", stats[1,1])
: end
-----------------------------------------------------------------------------------------
.
. di opt_lag_AIC
2
To plug into a later command automatically, use expressions like
`=opt_lag_AIC'
as arguments to options.