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Re: st: unit-root test

From   Muhammad Anees <>
Subject   Re: st: unit-root test
Date   Wed, 22 Aug 2012 19:05:58 +0500


You can find more information on theoritical part from the references
given in the helpfile.

Moreoever, alternative sources of information is available from:
-xtunitroot- Check from Stata's command line if -help xtunitroot-
gives you these and if not then install it from -ssc install
xtunitroot- or find it using -xtunitroot-.

The help file contains more information but there are some points
which can be used to answer your questions:

1. You can not use varlist, so it means you have to run the command
for varname. It means may be using it for one variable at a time or
for each variable seperately.
2. You can not use -xtunitroot- for data with gaps or what we can say
some observations on some series are missing. The data should be
strongly balanced.
3. Once confirmed, you can use the first difference to overcome the
issue of non-stationarity or this can be confirmed that the series are
stationary in first difference.

I hope more discussion will help us learn more on these issues. Also I
hope this helps you somehow.


Muhammad Anees
Assistant Professor/Programme Coordinator
COMSATS Institute of Information Technology
Attock 43600, Pakistan

On Wed, Aug 22, 2012 at 6:13 PM, L P <> wrote:
> Hi there,
> I would be grateful if I could receive your help since I am new to
> econometrics and STATA.
> I am using STATA 9.0 an I am working on a panel data based on
> observations for 30 countries (id) and 25 years from 1981 to 2005
> (time). The database is unbalanced since it contains gaps in id and
> time dimensions. The model specification looks like
> Ln Emissions[it] = a + b1 Ln GDP[it] + b2 LnGDP^2[it] + b3 Ln
> Trade(lag-1)[id] + ... + e
> According to what I read in statalist, I am trying to test the
> variables of my model specification for stationarity with
> Levin-Lin-Chu test and the use of the followwing STATA commands:
> . tsset id year
> . levinlin variable name, lag(1)
> My questions:
> 1. shall I run the test for each single dependent variable? What about
> the independent variable?
> 2. how can I overtake the problem of the gaps to allow the test for
> those variables characterised by gaps in the databse?
> Furthermore, if I find a p-value > or = 0.05, I have to accept the
> null hypothesis (that is the panels contain unit-roots). With the aim
> of overtaking this problem, is it enough to build first differences of
> the variable performing in this way?
> Thanks in advance.
> Lino
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