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st: Re: test for time trend
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.
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 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?
Thank you for your help. Best wishes,
Cornelia
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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