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Re: st: Fwd: Panel Unit Root Test

 From urbain thierry YOGO To statalist@hsphsun2.harvard.edu Subject Re: st: Fwd: Panel Unit Root Test Date Thu, 6 Dec 2012 18:16:12 +0100

```A a simple answer, i can mention that with T>N, you should care about
non stationnarity. Then you should run a unit root test (xtunitroot in
stata). Best

2012/12/6, Yuval Arbel <yuval.arbel@gmail.com>:
> The government gave public-housing tenants the option to
> purchase their renal units after a major discount, which vary from one
> period to another. We had the trail of the discount for each and every
> tenant, but we had to check this trail is random walk (otherwise
> tenants will anticipate the momentum and wait until the last day to
> exercise). Under these circumstances we ran a panel unit-root test,
> and the unit root hypothesis was not rejected.
>
> On Thu, Dec 6, 2012 at 6:27 AM, Yuval Arbel <yuval.arbel@gmail.com> wrote:
>> Of course they do. After all in panel framework you are following each
>> subject across time. So any typical problems of  cross-section and
>> time-series are relevant for a panel.
>>
>> As an example consider the following interesting application, based on
>> a study I have just
>> completed. The government gave public-housing tenants the option to
>> purchase their renal units after a major discount, which vary from one
>> period to another. We had the trail of the discount for each and every
>> tenant, but we had to check this trail is random walk (otherwise
>> tenants will anticipate the momentum and wait until the last day to
>> exercise). Under these circumstances we ran a panel unit-root test,
>> and the unit root hypothesis was not rejected.
>> On Thu, Dec 6, 2012 at 6:20 AM, Francesoc Paldini <f.paldini@gmail.com>
>> wrote:
>>> Dear Yuval,
>>>
>>> technical part, which starts with: "In a nutshell, unit root is a very
>>> big problem in time-series" is missing).
>>>
>>> I know the problem of "unit roots" in time series data. I'm wondering,
>>> if those problems are existing in panel  applications (fixed effects
>>> estimator), too.
>>>
>>> Thanks again!
>>> Frances
>>>
>>>
>>> 2012/12/6 Yuval Arbel <yuval.arbel@gmail.com>:
>>>> There are several interesting applications to the unit-root hypothesis:
>>>>
>>>> According to the quantity theory of money M=kPY where M is the
>>>> quantity of money, P is price level and Y is the GDP. The prominent
>>>> prediction of the model is neutrality of money: if M increases by x%
>>>> and Y is fixed P is expected to increase by x%.
>>>>
>>>> Note that if we convert this into a logarithmic form, it turns out
>>>> that the model predicts a unit root as a coefficient of P and Y. In
>>>> fact, somebody checked the theory based on the unit-root hypothesis.
>>>>
>>>> Another interesting application is based on a study I have just
>>>> completed. The government gave public-housing tenants the option to
>>>> purchase their renal units after a major discount, which vary from one
>>>> period to another. We had the trail of the discount for each and every
>>>> tenant, but we had to check this trail is random walk (otherwise
>>>> tenants will anticipate the momentum and wait until the last day to
>>>> exercise). Under these circumstances we ran a panel unit-root test,
>>>> and the unit root hypothesis was not rejected.
>>>>
>>>> On Thu, Dec 6, 2012 at 6:11 AM, Yuval Arbel <yuval.arbel@gmail.com>
>>>> wrote:
>>>>> Frances,
>>>>>
>>>>> In a nutshell, unit root is a very big problem in time-series
>>>>> analysis. If there is a unit root the series is a random walk and
>>>>> explosive. This implies that the estimates are inefficient, and by
>>>>> backward induction we can show that the SD estimates do not converge
>>>>> anywhere. A simple way to explain it is to divide a series to
>>>>> sub-samples. Imagine that the mean and SD of each sub-sample is
>>>>> different.
>>>>>
>>>>> A simple way to address unit-root problems is to use a difference
>>>>> series, i.e., Yt-Yt-1
>>>>>
>>>>> On Thu, Dec 6, 2012 at 5:17 AM, Francesoc Paldini <f.paldini@gmail.com>
>>>>> wrote:
>>>>>> Hello Statalist,
>>>>>>
>>>>>> I'm working with dynamic panel data (unfortunately with small
>>>>>> data sets) and I'm performing simple fixed effect estimations. Since
>>>>>> T
>>>>>> is pretty large (T=30), I don't care about the Nickel Bias.
>>>>>>
>>>>>> Actually, I don't get the concept of unit root tests for panel data?
>>>>>> Under which circumstances are unit roots problematic?
>>>>>>
>>>>>> Does the asymptotic distribution theory require the estimator to
>>>>>> satisfy the usual (time series) conditions that rule out unit and
>>>>>> explosive roots? Do I need lots of cross-sectional units (in my case:
>>>>>> N=20)?
>>>>>>
>>>>>> Best wishes,
>>>>>> Frances
>>>>>> *
>>>>>> *   For searches and help try:
>>>>>> *   http://www.stata.com/help.cgi?search
>>>>>> *   http://www.stata.com/support/faqs/resources/statalist-faq/
>>>>>> *   http://www.ats.ucla.edu/stat/stata/
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Dr. Yuval Arbel
>>>>> 4 Shaar Palmer Street,
>>>>> Haifa 33031, Israel
>>>>> e-mail1: yuval.arbel@carmel.ac.il
>>>>> e-mail2: yuval.arbel@gmail.com
>>>>
>>>>
>>>>
>>>> --
>>>> Dr. Yuval Arbel
>>>> 4 Shaar Palmer Street,
>>>> Haifa 33031, Israel
>>>> e-mail1: yuval.arbel@carmel.ac.il
>>>> e-mail2: yuval.arbel@gmail.com
>>>> *
>>>> *   For searches and help try:
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>>>> *   http://www.ats.ucla.edu/stat/stata/
>>> *
>>> *   For searches and help try:
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>>
>>
>>
>> --
>> Dr. Yuval Arbel
>> 4 Shaar Palmer Street,
>> Haifa 33031, Israel
>> e-mail1: yuval.arbel@carmel.ac.il
>> e-mail2: yuval.arbel@gmail.com
>
>
>
> --
> Dr. Yuval Arbel
> 4 Shaar Palmer Street,
> Haifa 33031, Israel
> e-mail1: yuval.arbel@carmel.ac.il
> e-mail2: yuval.arbel@gmail.com
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/faqs/resources/statalist-faq/
> *   http://www.ats.ucla.edu/stat/stata/
>

--
*Urbain Thierry YOGO
Ph.D candidate in Economics*
*
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```