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st: RE: dfuller: why do I get different results?
From
Nick Cox <[email protected]>
To
"'[email protected]'" <[email protected]>
Subject
st: RE: dfuller: why do I get different results?
Date
Fri, 18 Nov 2011 11:37:24 +0000
bysort appt: gen reduct1=reduct_per[_n-1]
and
gen reduct1 = L1.reduct
give identical results only under certain conditions. One is that sorting by -appt- does _not_ itself guarantee that values for each -appt- are sorted in time order. There can be other problems with omitted observations, etc.
Use time-series operators after -tsset- to generate lagged variables.
Nick
[email protected]
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Yuval Arbel
Sent: 18 November 2011 11:30
To: statalist
Subject: st: dfuller: why do I get different results?
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
-------------+------------------------------ Adj R-squared = -0.0286
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?
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