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Re: st: unit-root test
Nick Cox <email@example.com>
Re: st: unit-root test
Wed, 22 Aug 2012 15:42:59 +0100
On #1, -xtunitroot- is an official Stata command added in Stata 11.
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.
On Wed, Aug 22, 2012 at 3:29 PM, L P <firstname.lastname@example.org> 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-)
> 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 <email@example.com>:
>> 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 <firstname.lastname@example.org> 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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