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Re: st: Re: test for time trend
Cornelia Schmidt schrieb:
Dear Mike and Clive,
thank you very much for your suggestions. To provide a bit more
information:
the dependent variable of my analysis is education spending (% GDP). I
decided to limit the period under investigation to 1980-2000. The
panel is highly unbalanced due to a large number of missings on the
dependent variable.
In order to test whether there is a positive time-trend in he
education spending data, I first did a Fisher test for panel data.
Compared to other stationarity tests, it has the advantage of being
feasible for unbalanced panels. Based on the p-values of individual
unit root tests, it assumes that all series are non-stationary under
the null hypothesis against the alternative that at least one series
in the panel is stationary. This reveals drawbacks of the test. In my
analysis, I would actually be interested in testing a null assuming
stationarity of the data. The p-values of the Fisher test do not tell
anything about that. If the null hypothesis is rejected this does not
imply that all series are stationary. Therefore, in my opinion, the
power of the test is quite limited.
The Hadri-LM-Test (for which an ado-file-exists) allows to test the null
of stationarity of all series. Unfortunately, according to simulation
studies, its size explodes as soon as there is some serial correlation
in the data, so it is quite useless in practice.
To get a better understanding of whether the series of some countries
are non-stationary, I tried to run time-series stationarity-tests for
every country. And here I have some questions: I tried a DF-GLS
unit-root test but it seems that it does not work with missings in the
time series. Alternatively, I used a Phillips-Perron and an Augmented
Dickey-Fuller unit-root test which both worked with my data. Do you
know which of these tests best fits? Can they be used alternatively or
in which respects do they differ?
The basic difference between the ADF and Phillips Perron-Tests is that
the former uses a parametric correction for serial correlation (adding
lagged differences), whereas the latter a non-parametric one, which is
known to be very problematic (i.e.: leading to large size distortions)
if there is a negative MA-process in the residuals; so ADF might be
preferable. Independent of the test you use, the power will be very low
with series of this length (T=20), so it is no wonder that the
unit-root hypothesis cannot be rejected.
The test results indicate that I cannot reject the null hypothesis of
non-stationarity for most of the countries. As I have so many missings
in my dataset, it is hardly possible to use a first-difference
estimation to correct for the time trend. Do you know an alternative
way? Would it be an option to take a time variable into the model?
Including a time variable would be valid only under the assumption that
there is a deterministic time trend in your data, while your results
point to a stochastic trend. You might think about period-specific fixed
effects , which would take a stochastic trend common to all units out of
the data (but not individual-specific unit-root processes).
Alternatively, if you are interested in long-run relationships and some
of the covariates are also non-stationary, you might consider testing
for cointegration (unfortunately there are no such tests available in
STATA, but a rough test would be to check for stationarity of the
regression residuals) and estimating an Error-Correction-Model.
Hope my comments are of use.
Christoph
From: Michael Hanson <[email protected]>
Reply-To: [email protected]
To: [email protected]
Subject: Re: st: test for time trend
Date: Sat, 4 Feb 2006 19:38:40 -0500
On Feb 4, 2006, at 4:29 PM, Cornelia Schmidt wrote:
I know it is a basic question, but it would be great if you could
help me.
I have a panel dataset for a sample of 50 countries over a period of
40 years.
How can I test if there is a time trend in the data and how can I
correct for it?
Individual trends or common? Deterministic or stochastic?
Linear or otherwise? You'll need to provide a bit more information
to get an appropriate answer to your question. While you could
simply dummy for years, that may be effectively throwing away useful
information in the time domain (certainly year dummies won't help
identify a time trend); with T = 40 as you have, you may want to
consider an explicit model of the time-series process(es)
contributing to your data generation process. Hope that helps.
-- Mike
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--
Christoph Birkel, M.A.
Martin-Luther-Universit�t Halle-Wittenberg
Institut f�r Soziologie
D-06099 Halle (Saale)
GERMANY
Tel.: ++ 49 3 45 / 55 24 22 5
Fax.: ++ 49 3 45 / 55 27 14 9
http://www.soziologie.uni-halle.de/birkel/
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