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st: dfuller: why do I get different results?

 From Yuval Arbel To statalist Subject st: dfuller: why do I get different results? Date Fri, 18 Nov 2011 13:30:15 +0200

```Dear Statalist Participants,

when I run:

. dfuller reduct_per if appt==2862,noconstant regress

I get the following outcome:

Dickey-Fuller test for unit root                   Number of obs   =        37

---------- Interpolated Dickey-Fuller ---------
Test         1% Critical       5% Critical      10% Critical
Statistic           Value             Value             Value
------------------------------------------------------------------------------
Z(t)             -6.026            -2.641            -1.950            -1.605

------------------------------------------------------------------------------
D.reduct_per |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. |  -.5409015   .0897625    -6.03   0.000    -.7229484   -.3588546
------------------------------------------------------------------------------

Those outcomes imply that the calculated statistic for the unit-root
test is -6.03

But when I define:

bysort appt: gen reduct1=reduct_per[_n-1]
bysort appt: gen dreduct1=reduct_per-reduct_per[_n-1]

and I run:

regress dreduct1 reduct1 if appt==2862,noconst

I get:

. regress dreduct1 reduct1  if appt==2862,noconst

Source |       SS       df       MS              Number of obs =      36
-------------+------------------------------           F(  1,    35) =    0.00
Model |           0     1           0           Prob > F      =  1.0000
Residual |         625    35  17.8571429           R-squared     =  0.0000
Total |         625    36  17.3611111           Root MSE      =  4.2258

------------------------------------------------------------------------------
dreduct1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct1 |          0   .0509647     0.00   1.000    -.1034639    .1034639
------------------------------------------------------------------------------

.
Shouldn't I get exactly the same outcomes in both regressions?

--
Dr. Yuval Arbel