Challenges in the Era of Globalization - iaabd

Challenges in the Era of Globalization
Edited by Emmanuel Obuah
The application of Granger causality test to panel data is a recent occurrence. Panel data analysis provides
more observations and thus increases degree of freedom associated with econometric analysis. According
to Hsiao (2001, 2003), Hsiao et al (2002) and Anderson and Hsiao (1982) panel data analysis gives a
more accurate prediction of outcomes than time series analysis.
With i = 1, 2, ….N; t= K+1, K+2, …, T and y, x are the two variables being tested.
The error term, εit is assumed to satisfy the orthogonality conditions:
In Equation (5), bi captures country-specific effects thereby allowing the relationship between x and y to
vary across the panel of data. However, the approach of Holtz-Eakin et al. (1988) is adopted by allowing
the lag coefficients of δkt and βkt to be constant for each t. In order to get rid of the individual effects
represented by bi, Holtz-Eakin et al. employ a differencing transformation of (5). Moreover, based on
Anderson and Hsiao (1982), Chowdhury (2001) and Hood et al. (2006) an instrumental variable
framework is employed to deal with possible correlation between the error term of the transformed model
and the right-hand-side explanatory variables. Granger causality from x to y is tested by a joint hypothesis
that all βkt are zero.
Empirical Results
When a Granger causality test is performed, four possible outcomes are possible:
i. Per capita health expenditure Granger causes per capita GDP
ii. Per capita GDP Granger causes per capita health expenditure
iii. Per capita GDP Granger causes per capita health expenditure and vice versa
iv. No Granger causality exits
In this paper, an attempt is made to explore the causal relationship between health expenditures and
output in fourteen of ECOWAS countries using panel data from 1998 through 2006. Table 1 show the
results of unit root tests which are applied to the data. The results show that both GDP and per capita
health expenditures (HEC) are nonstationary in levels. The application of unit root tests to the first
difference of the series reveals that the series are stationary at the conventional 5 percent level. The results
for the Levin, Lin & Chu; Im, Pesaran &Shin; ADF-Fisher and PP-Fisher tests are all consistent.
Table 1: Panel Unit Root Test Results (With Individual Effects)
Test Statistic GDP HEC ∆GDP ∆HEC
Levin, Lin & Chu statistic
Im, Pesaran & Shin statistic
ADF-Fisher chi-square statistic
PP-Fisher chi-square statistic
3.328
5.070
11.864
15.642
0.342
2.452
20.226
17.583
-11.574*
-8.398*
111.960*
147.594*
(5)
(6)
-11.052*
-7.619*
103.352*
126.264*
389