1. Esman M. Nyamongo Central Bank of Kenya
Panel data analysis
Econometrics Course organized by the COMESA Monetary Institute (CMI) on 2-11 June 2014, KSMS Nairobi Kenya
Esman M. Nyamongo
Central Bank of Kenya

2. Non stationary panel data
DAY 10
Non stationary panel data

3. Non stationary panel data
Previous panel data tools did not deal with the possibility of non-stationary data
Data limitations- T was short
Large T dimensions:
Allow estimation of heterogenous panels
Allow investigation of non-stationary spurious regression and cointegrtion
Background knowledge of stationarity/unit root in time series environment is assumed here.
Tests of panel unit root
Panel cointegration

4. A. Panel unit-root tests
A number of tests are available:
Levin and Lin panel unit root test
The Im, Pesaran and Shin (IPS) panel unit root test
The Maddala and Wu (1999) panel unit root test
etc

5. 1. The Levin and Lin (1993) panel unit-root test
Pioneering work by Levin and Lin ( ).
Allows fixed individual and time effects (through an individualized trend); is homogenous across the panel
The test is ‘Dickey-Fuller’ based:
But can assume as case of
Practical exercises

6. 2. Im, pesaran and shin (1997)
In this test, IPS allow for more heterogeneity. They construct a panel test first on DF-test and then ADF test:
Because the lag lengths, pi, can differ across equations, separate lag length test for each equation is advised.
However, it works within a balanced panel setup.
In addition, if time trend is included in one equation it should be included in all.
Hypothesis testing
for all i
for at least one cross-section

7. Then we construct the statistic as follows:
Once all the pi have been estimated, we obtain the t-statistic. We then compute the sample mean of the n different t-statistics:
Then we construct the statistic as follows:
where:
t bar is the average ADF test statistic of all the individual cross-section statistics
E(tbar) and var(tbar) are means and variances that must be computed based on Monte Carlo simulated moments provided by IPS in their paper.
Paper to be provided, if possible.
Practical exercises to be done as well

8. 3. Maddala and wu (2000)
This test is constructed with the idea of concentrating on the shortcomings of Levin and Lin and IPS.
IPS assumes T is constant for all i.
Both Levin and Lin, and IPS have critical values which depend on pi.
This test:
Does not require balanced panel (as IPS)
Can accommodate different unit root tests
Can be adapted fro less restrictive assumptions about cross-correlations
The maddala and Wu test is a Fisher (1932) based test that combines information on the unit root test p-values
It has the advantage of being exact
Does not depend on asymptotics for distribution

9. The test is stated as follows:
This test suggests that we can do any number of individual tests and combine the.
The hypothesis is stated as:
xxxxx
Practical session is

10. b. Panel cointegration A number of tests have emerged:
With null hypothesis of no cointegration
Pedroni (heteroscedasticity)
Kao (homoscedasticity)
With null hypothesis of cointegration
McCososkey and Kao (1998) xx