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st: RE: Problem with foreach in Stata 10.1
Your problem report is very clear and I can reproduce it.
But this is nothing to do with -foreach- (mentioned in your title) or
-forvalues- (used in your example). The same behaviour is seen if you
just do your example the slow way
webuse air2
dfuller air, lags(1)
estat ic
dfuller air, lags(2)
estat ic
dfuller air, lags(3)
estat ic
I don't why you get the same results, but the problem if there is one
lies between -dfuller- and -estat ic-.
Nick
[email protected]
Joao Ricardo F. Lima
I`m with one problem using foreach with Stata 10.1. I was teaching
unit root test, I was demonstrating how to use the foreach command
too. The, the surprise:
***begin example****
webuse air2
forvalues i=1/3 {
dfuller air, lags(`i')
estat ic
}
***end example****
This works fine with Stata 9.2, but don't with 10.1. With Stata 9.2:
. webuse air2
(TIMESLAB: Airline passengers)
. forvalues i=1/3 {
2. dfuller air, lags(`i')
3. estat ic
4. }
Augmented Dickey-Fuller test for unit root Number of obs =
142
---------- Interpolated Dickey-Fuller
---------
Test 1% Critical 5% Critical 10%
Critical
Statistic Value Value
Value
------------------------------------------------------------------------
------
Z(t) -2.345 -3.496 -2.887
-2.577
------------------------------------------------------------------------
------
MacKinnon approximate p-value for Z(t) = 0.1579
------------------------------------------------------------------------
------
Model | Obs ll(null) ll(model) df AIC
BIC
-------------+----------------------------------------------------------
------
. | 142 -701.1962 -691.5394 3 1389.079
1397.946
------------------------------------------------------------------------
------
Augmented Dickey-Fuller test for unit root Number of obs =
141
---------- Interpolated Dickey-Fuller
---------
Test 1% Critical 5% Critical 10%
Critical
Statistic Value Value
Value
------------------------------------------------------------------------
------
Z(t) -1.811 -3.496 -2.887
-2.577
------------------------------------------------------------------------
------
MacKinnon approximate p-value for Z(t) = 0.3751
------------------------------------------------------------------------
------
Model | Obs ll(null) ll(model) df AIC
BIC
-------------+----------------------------------------------------------
------
. | 141 -696.6955 -684.4298 4 1376.86
1388.655
------------------------------------------------------------------------
------
Augmented Dickey-Fuller test for unit root Number of obs =
140
---------- Interpolated Dickey-Fuller
---------
Test 1% Critical 5% Critical 10%
Critical
Statistic Value Value
Value
------------------------------------------------------------------------
------
Z(t) -1.536 -3.497 -2.887
-2.577
------------------------------------------------------------------------
------
MacKinnon approximate p-value for Z(t) = 0.5158
------------------------------------------------------------------------
------
Model | Obs ll(null) ll(model) df AIC
BIC
-------------+----------------------------------------------------------
------
. | 140 -692.2411 -678.5628 5 1367.126
1381.834
------------------------------------------------------------------------
------
.
end of do-file
.
With Stata 10.1:
. webuse air2
(TIMESLAB: Airline passengers)
. forvalues i=1/3 {
2. dfuller air, lags(`i')
3. estat ic
4. }
Augmented Dickey-Fuller test for unit root Number of obs =
142
---------- Interpolated Dickey-Fuller
---------
Test 1% Critical 5% Critical 10%
Critical
Statistic Value Value
Value
------------------------------------------------------------------------
------
Z(t) -2.345 -3.496 -2.887
-2.577
------------------------------------------------------------------------
------
MacKinnon approximate p-value for Z(t) = 0.1579
------------------------------------------------------------------------
-----
Model | Obs ll(null) ll(model) df AIC
BIC
-------------+----------------------------------------------------------
-----
. | 142 -701.1962 -691.5394 3 1389.079
1397.946
------------------------------------------------------------------------
-----
Note: N=Obs used in calculating BIC; see [R] BIC note
Augmented Dickey-Fuller test for unit root Number of obs =
141
---------- Interpolated Dickey-Fuller
---------
Test 1% Critical 5% Critical 10%
Critical
Statistic Value Value
Value
------------------------------------------------------------------------
------
Z(t) -1.811 -3.496 -2.887
-2.577
------------------------------------------------------------------------
------
MacKinnon approximate p-value for Z(t) = 0.3751
------------------------------------------------------------------------
-----
Model | Obs ll(null) ll(model) df AIC
BIC
-------------+----------------------------------------------------------
-----
. | 142 -701.1962 -691.5394 3 1389.079
1397.946
------------------------------------------------------------------------
-----
Note: N=Obs used in calculating BIC; see [R] BIC note
Augmented Dickey-Fuller test for unit root Number of obs =
140
---------- Interpolated Dickey-Fuller
---------
Test 1% Critical 5% Critical 10%
Critical
Statistic Value Value
Value
------------------------------------------------------------------------
------
Z(t) -1.536 -3.497 -2.887
-2.577
------------------------------------------------------------------------
------
MacKinnon approximate p-value for Z(t) = 0.5158
------------------------------------------------------------------------
-----
Model | Obs ll(null) ll(model) df AIC
BIC
-------------+----------------------------------------------------------
-----
. | 142 -701.1962 -691.5394 3 1389.079
1397.946
------------------------------------------------------------------------
-----
Note: N=Obs used in calculating BIC; see [R] BIC note
.
end of do-file
How you can see, the estat ic is repeated in each looping...
Can someone reproduce my problem and explain what am I doing wrong?
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