Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: Re: Re: st: RE: dfuller: why do I get different results?
From
Yuval Arbel <[email protected]>
To
[email protected]
Subject
Re: Re: Re: st: RE: dfuller: why do I get different results?
Date
Sat, 19 Nov 2011 18:36:12 +0200
Here are the same outcomes with a constant.
The problem is that the maximum length of each panel will be 114
periods. So I might have many panels with less than 100 periods - so I
still need the -xtunitroot- commands. Am I correct?
What you said about the sample size required to make the dfuller valid
is interesting. I wonder how did econometricians work in the past when
they had small macro series of GDPs (take Milton Friedman for example
- theory of the consumption function)?
name: <unnamed>
log: D:\kingston\public_housing\dfuller_20111118.smcl
log type: smcl
opened on: 19 Nov 2011, 18:21:49
. dfuller reduct_per if appt==2851, regress
Dickey-Fuller test for unit root Number of obs = 27
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -0.891 -3.736 -2.994 -2.628
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.7910
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.0666667 .0748331 -0.89 0.381 -.2207884 .0874551
|
_cons | 6 6.136774 0.98 0.338 -6.638923 18.63892
------------------------------------------------------------------------------
. dfuller reduct_per if appt==2862, regress
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) -6.784 -3.668 -2.966 -2.616
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.0000
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.6557562 .0966619 -6.78 0.000 -.8519902 -.4595222
|
_cons | 4.71219 1.944633 2.42 0.021 .7643737 8.660005
------------------------------------------------------------------------------
. dfuller reduct_per if appt==2906, regress
Dickey-Fuller test for unit root Number of obs = 94
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -1.313 -3.518 -2.895 -2.582
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.6233
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.0437803 .0333491 -1.31 0.193 -.1100146 .0224539
|
_cons | 1.406441 1.244567 1.13 0.261 -1.065375 3.878258
------------------------------------------------------------------------------
. dfuller reduct_per if appt==2907, regress
Dickey-Fuller test for unit root Number of obs = 103
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -2.647 -3.509 -2.890 -2.580
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.0836
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.0798791 .0301724 -2.65 0.009 -.1397331 -.0200251
|
_cons | .877063 .6982839 1.26 0.212 -.5081445 2.262271
------------------------------------------------------------------------------
. dfuller reduct_per if appt==2908, regress
Dickey-Fuller test for unit root Number of obs = 99
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -0.603 -3.511 -2.891 -2.580
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.8703
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.0143708 .0238291 -0.60 0.548 -.0616649 .0329233
|
_cons | .9428312 1.059461 0.89 0.376 -1.159906 3.045569
------------------------------------------------------------------------------
. dfuller reduct_per if appt==2907, regress
Dickey-Fuller test for unit root Number of obs = 103
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -2.647 -3.509 -2.890 -2.580
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.0836
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.0798791 .0301724 -2.65 0.009 -.1397331 -.0200251
|
_cons | .877063 .6982839 1.26 0.212 -.5081445 2.262271
------------------------------------------------------------------------------
. dfuller reduct_per if appt==2908, regress
Dickey-Fuller test for unit root Number of obs = 99
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -0.603 -3.511 -2.891 -2.580
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.8703
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.0143708 .0238291 -0.60 0.548 -.0616649 .0329233
|
_cons | .9428312 1.059461 0.89 0.376 -1.159906 3.045569
------------------------------------------------------------------------------
. dfuller reduct_per if appt==2912, regress
Dickey-Fuller test for unit root Number of obs = 85
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -2.030 -3.531 -2.902 -2.586
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.2736
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.0826385 .0407099 -2.03 0.046 -.1636088 -.0016683
|
_cons | 1.403384 1.497567 0.94 0.351 -1.575217 4.381985
------------------------------------------------------------------------------
. dfuller reduct_per if appt==2915, regress
Dickey-Fuller test for unit root Number of obs = 87
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -1.276 -3.528 -2.900 -2.585
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.6403
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.0375646 .0294495 -1.28 0.206 -.0961181 .0209888
|
_cons | .8777472 1.084656 0.81 0.421 -1.27884 3.034335
------------------------------------------------------------------------------
. dfuller reduct_per if appt==3035, regress
Dickey-Fuller test for unit root Number of obs = 47
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -2.921 -3.600 -2.938 -2.604
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.0430
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.280148 .0959161 -2.92 0.005 -.4733329 -.086963
|
_cons | 1.635018 1.943664 0.84 0.405 -2.279723 5.54976
------------------------------------------------------------------------------
. dfuller reduct_per if appt==3051, regress
Dickey-Fuller test for unit root Number of obs = 39
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -2.863 -3.655 -2.961 -2.613
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.0499
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.2573525 .089898 -2.86 0.007 -.4395032 -.0752017
|
_cons | 5.53697 2.505182 2.21 0.033 .460989 10.61295
------------------------------------------------------------------------------
. dfuller reduct_per if appt==3057, regress
Dickey-Fuller test for unit root Number of obs = 26
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -0.784 -3.743 -2.997 -2.629
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.8238
------------------------------------------------------------------------------
D.reduct_per | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
reduct_per |
L1. | -.0677992 .0864757 -0.78 0.441 -.2462763 .1106779
|
_cons | 4.904603 5.209682 0.94 0.356 -5.847653 15.65686
------------------------------------------------------------------------------
. log close
name: <unnamed>
log: D:\kingston\public_housing\dfuller_20111118.smcl
log type: smcl
closed on: 19 Nov 2011, 18:25:56
------------------------------------------------------------------------------
On Sat, Nov 19, 2011 at 5:42 PM, Christopher Baum <[email protected]> wrote:
> <>
>
> I still need the formal statistical test - for a research paper a
> general plot will not be sufficient.
>
> BTW: I just ran manually the first 10 panels. It is not a
> representative sample, but as you can see below indeed, in most of
> them the unit-root hypothesis was not rejected at the 1% significance
> level:
>
> Unit root tests have notoriously low power with < 100 observations -- that's why we have panel unit root tests.
> Three rejections at 5% out of 10 samples suggests that "most" might be I(1), but then a df test with no constant nor trend is a queer bird indeed. The process you are modeling has to make sense under both null and alternative hypotheses for the test to be valid. A bit of algebra shows that a DF regression without a constant implies that the mean of Y is the mean of epsilon == 0 under the alternative hypothesis of stationarity. If your data in levels do not have a mean of zero, this model is incapable of reproducing the data with any choice of ]beta < 0, and so the test is flawed.
>
> Kit
>
> Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html
> An Introduction to Stata Programming | http://www.stata-press.com/books/isp.html
> An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html
>
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
--
Dr. Yuval Arbel
School of Business
Carmel Academic Center
4 Shaar Palmer Street, Haifa, Israel
e-mail: [email protected]
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/