Re: st: Test for autocorrelation - "sample may not include multiple panels"

[Felix Wädlich <fwaedlich@gmail.com>]

Hi Markus,

thanks for ur answer, I did not consider that. Unfortunately -dwstat2-
and -durbina2- give me the same answer "sample may not include
multiple panels".

Regarding the unit root test: I also read, that non-stationarity could
be a problem, and my regression does include GDP and GDP per capita,
which are, as I understand, textbook variables for non-stationarity.
The problem here is that most unit root test require a balanced panel.
The one that seems to work for me, is the Dickey-Fuller-Test. But
honestly, I am not sure how to interpret my findings, since the
textbooks dont really cover this issue.
When I use -xtunitroot fisher loggdppc, dfuller lags(1)-, I get this:

Fisher-type unit-root test for loggdppc
Based on augmented Dickey-Fuller tests
---------------------------------------
Ho: All panels contain unit roots           Number of panels       =    135
Ha: At least one panel is stationary        Avg. number of periods =  21.85

AR parameter: Panel-specific                Asymptotics: T -> Infinity
Panel means:  Included
Time trend:   Not included
Drift term:   Not included                  ADF regressions: 1 lag
------------------------------------------------------------------------------
                                  Statistic      p-value
------------------------------------------------------------------------------
 Inverse chi-squared(270)  P       101.9727       1.0000
 Inverse normal            Z        12.1821       1.0000
 Inverse logit t(674)      L*       12.6890       1.0000
 Modified inv. chi-squared Pm       -7.2307       1.0000
------------------------------------------------------------------------------
 P statistic requires number of panels to be finite.
 Other statistics are suitable for finite or infinite number of panels.

As I understand the literature a p-value>1 means nonstationarity. Am i right?
Best regards,

Felix


2011/3/7 Markus Eberhardt <markus.eberhardt@economics.ox.ac.uk>:
> Hi Felix
>
> The tests routines you mention are for single time series only. For
> the panel you can use the equivalents created by Kit Baum (panelauto:
> http://ideas.repec.org/c/boc/bocode/s435102.html).
> Given your data I should perhaps worry more about stationarity than
> serial correlation. A detailed canon of panel unit root tests is
> available here:
> http://sites.google.com/site/medevecon/code#TOC-Panel-Time-Series-Tools
> where you'll also find a number of other related issues (cross-section
> correlation; coinegration; estimation).
> If you insist on staying in the micro-panel estimator world, despite
> your data being macro (which is the common attitude in the applied
> literature), you should have a look at an explicitly dynamic model.
> Bond (2002) in the Portuguese Journal discusses this is great detail
> (naturally, for the micro panel case).
>
>
> Markus Eberhardt
> ESRC Post-doctoral Research Fellow, Centre for the Study of African
> Economies, Department of Economics, University of Oxford
> Stipendiary Lecturer, St Catherine's College, Oxford
>
> web: http://sites.google.com/site/medevecon/home
> email: markus.eberhardt@economics.ox.ac.uk
> twitter: http://twitter.com/sjoh2052
> mail: Centre for the Study of African Economies, Department of
> Economics, Manor Rd, Oxford OX1 3UQ, England
>
>
>
>
> On 7 March 2011 16:11, Felix Wädlich <fwaedlich@gmail.com> wrote:
>> Hi Statalist,
>>
>> I have an unbalanced panel and need to test for autocorrelation (1978
>> to 2004, 100 to 140 countries). Due to my research design, I am very
>> sure that I need to consider autocorrelation. Therefore I am including
>> a lagged dependent variable (which also makes sense for theoretical
>> reasons) as well as dummies for period (and unit effects). Basically I
>> will first estimate an -xtpcse, corr(ar1)- and then -xtreg i.year,
>> fe-.
>> Judging from the literature the standard test for autocorrelation is
>> -dwstat- (or -bgodfrey-). Unfortunately, Stata tells me "sample may
>> not include multiple panels", and therefore cannot test for serial
>> correlation.( No matter whether i use define my data set as -xtset- or
>> -tsset-)
>> I also tried -xtserial-, which works and indicates that my regression
>> suffers from autocorrelation, but only without my fixed effects
>> specification. So how can I tell, that autocorrelation is sufficiently
>> adressed after my fe(timewise)-specification?
>> Since I need such a test for my regression diagnostics, what can i do,
>> what other options are there? Are there maybe graphical options as
>> well?
>> Thanks.
>>
>> Best regards,
>>
>> Felix
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