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Re: st: Test for autocorrelation - "sample may not include multiple panels"
From
Markus Eberhardt <[email protected]>
To
[email protected]
Subject
Re: st: Test for autocorrelation - "sample may not include multiple panels"
Date
Mon, 7 Mar 2011 17:09:37 +0000
Hi again
Yes, that's right. You can have a look at my notes/reader on the wider
field of 'Panel Time Series', of which unit root testing is one
example:
http://sites.google.com/site/medevecon/publications-and-working-papers#TOC-Miscellaneous
There are a number of other issues coming in you need to worry about.
All of this is laid out in the (slightly dated) reader.
Best
m
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: [email protected]
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 17:05, Felix Wädlich <[email protected]> wrote:
> 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 <[email protected]>:
>> 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: [email protected]
>> 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 <[email protected]> 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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