Re: st: unit-root test

[Nick Cox <njcoxstata@gmail.com>]

On #1, -xtunitroot- is an official Stata command added in Stata 11.

http://www.stata.com/help.cgi?xtunitroot
http://www.stata.com/help.cgi?whatsnew10to11

Note that, as above, you can find this out even given your version of Stata.

On everything, note that the spelling is "Stata".

On everything else: Others are better able to comment.

Nick

On Wed, Aug 22, 2012 at 3:29 PM, L P <pas2612@gmail.com> wrote:
> Dear Muhammad,
>
> Many thanks for your suggestion. If I can take some more minutes of
> your time, I would like to share what follows:
>
> 1. as I said I am using STATA 9. For this reason when I typed the
> command . ssc install xtunitroot I got the message
> ssc install: "xtunitroot" not found at SSC, type -findit xtunitroot-
> (To find all packages at SSC that start with x, type -ssc describe x-)
> r(601);
>
> 2. as suggested in the error message I typed . findit xtunitroot and
> STATA opened a page with the link to four unit-root tests packages,
> which I installed all. Among them, I found the XTFISHER test (which is
> referred to be suitable for unbalanced data). I have tried to run the
> test and it looks like things are working in the right way now. For
> example, I run
>
> xtfisher  Ln_MKTopn_2, lag(1)
>
> and the result is
>
> Fisher Test for panel unit root using an augmented Dickey-Fuller test (1 lags)
>
> Ho: unit root
>
>          chi2(60)     =  119.9346
>          Prob > chi2  =      0.0000
>
> My questions:
>
> 1. what is the right interpretation of this result? I think I have to
> reject the null hyothesis because the p-value is < or = to 0.05.
> Hence, I can conclude that the variable is stationary and its use in
> the specification model is valid. Am I right?
>
> 2. what should be the right ammount of lags to be considered in the lag option?
>
> 3. does this test must be run for each single independent variable I
> consider in my model specification?
>
> 4. shall I run the test for the dependent variable as well?
>
> Thanks again for your really helpful support.
>
>
> 2012/8/22 Muhammad Anees <anees@aneconomist.com>:
>> Hello,
>>
>> 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.
>>
>>
>> On Wed, Aug 22, 2012 at 6:13 PM, L P <pas2612@gmail.com> 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?
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