1. INFLATION, INFLATION UNCERTAINTY AND OUTPUT GROWTH IN THE WAMZ: IMPLICATIONS FOR CURRENCY UNION
Abiodun O. FOLAWEWO, PhD & Oluwafemi M. ADEBOJE
Department of Economics
University of Ibadan,
Ibadan, Nigeria
Paper Presented at the 9th Annual Conference on Regional Integration in Africa (ACRIA 9)
July 8-11, 2018, Banjul, The Gambia

2. Presentation Outline Background to the Study Motivation of the Study
Brief Literature Review
Methodology
Empirical Resu
Conclusion

3. Background to the Study
Different countries of the world (even in Africa) have embraced economic regionalism in order to meet the challenges of the emerging world economic order. This is also reflected in the increasing interest in establishing monetary union.
UEMOA and CEMAC in Africa.
WAMZ: The heads of state and government of five countries - The Gambia, Ghana, Guinea, Nigeria and Sierra Leone- formally launched, signed and established WAMZ in Dec
The ultimate expectations of economic policy makers is to ensure the achievement of macroeconomic goals. This is because any distortion in any of the macroeconomic variables could cause adverse economic effect.
For instance, distortion in inflation could result into inflation uncertainty, which causes an adverse output effect as uncertainty about future inflation distorts the allocative efficiency aspect of price mechanism (Fountas et al., 2006).

4. Background to the Study Contd.
Thus, the inter-relationship between inflation, its uncertainty and output growth is fundamental in taking decision on whether to create the monetary union or not and in taking monetary policy decision after the creation of the union.
Lack of uniform evidence on the transmission channel from inflation to output through inflation uncertainty could signal national disparities among the member countries and render common monetary policy less effective stabilization policy tool (Fountas, 2004).
A non-uniformity of the relationships could also signal that inflation rates arising from common monetary policy decision, will result in asymmetrical real effects on the countries.

5. Motivation for the Study
Different studies have been done to examine the interrelationship between inflation and its uncertainty and/or including economic growth across African countries.
Nigeria: Bamanga et al. (2016)
Tunisia: Hachicha (2013)
South Africa: Nasr et al.(2015)
Ghana: Oteng-Abayie and Doe (2013); Barimah and Amuakwa-Mensah (2014)
4 African rich countries ( Algeria, Gabon, Congo Rep. & Libya): Ndoricimpa (2014)
West Africa Monetary Zone as a whole?
Therefore, this study investigates the relationship between inflation, its uncertainty and output growth by observing monthly inflation and output growth data of the five WAMZ member countries for the 1999:01 to 2016:12 period.

6. The Literature
The relationship between inflation and inflation uncertainty has attracted a lot of empirical/theoretical studies globally.
The resultant positive impact of higher average inflation on higher uncertainty was first suggested by Okun (1971) and Friedman (1977). Ball (1992) later formalised Friedman’s idea.
During inflationary period, monetary authority may be tempted to adopt tight monetary policy measure but may fear the consequential effect of recession. Thus, they are faced with dilemma which creates uncertainty about future monetary policy and hence makes monetary policy less stable.
Also in the midst of inflation uncertainty, central bank seeks to respond more sharply to shocks than when there is no uncertainty, in order to avoid undesirable outcomes in the future (Soderstrom, 2002).

7. The Literature Cont’d
The negative relationship between inflation uncertainty and economic growth has been suggested by Grier and Perry (2000), Fountas et al. (2002) and Apergis (2004).
The reverse causation from inflation uncertainty to inflation was suggested by Cukierman and Meltzer (1986).
Positive unidirectional relationship from inflation to inflation uncertainty (Fountas et al, 2002; Bamanga et al, 2016);
Positive unidirectional relationship from inflation uncertainty to inflation (Wilson, 2006; Balaji et al., 2016).

8. Methodology Variance from GARCH model as measure of uncertainty
This study employs a two-step estimation approach:
Variance from GARCH model as measure of uncertainty
Granger causality tests: inflation and its uncertainty; nominal uncertainty and output growth.
Measure of Inflation Uncertainty
This study adopts the asymmetric exponential-GARCH (EGARCH) model.
The choice of this model among various GARCH models is based on the proposition that GARCH model sets symmetry restrictions on the conditional variance which are not coherent with notion of inflation uncertainty in Friedman hypothesis (Brunner and Hess, 1993).

9. Methodology Cont’d.
The GARCH model for the estimation of the time-varying conditional variance is given by :
Where πt is the rate of inflation at time t and εt is the shocks to the inflation process that cannot be forecasted with information known at time t. εt is also assumed to be normally distributed with zero mean with a time-varying conditional variance ht.

10. Methodology Contd. Granger Causality Tests
Causality between inflation and its uncertainty:
Equation (3) is used to test whether inflation (πt ) causes inflation uncertainty while equation (4) tests whether inflation uncertainty causes inflation.
The tests allow us to know whether inflation uncertainty (ht) precedes output growth (yt) and vice versa.

11. Methodology Contd.
Causality between Inflation Uncertainty and Output Growth:
The tests allow us to know whether inflation uncertainty (ht) precedes output growth (yt) and vice versa.

12. Empirical Results Descriptive Statistics
The dataset are monthly observations (Jan to Dec ) of the inflation and output growth of the five countries.
The inflation series for Guinea range from January 2005 to December 2016 while that of Sierra Lone range from January 2007 to December 2017.
All the data were sourced from International Financial Statistics database.
Table 1: Summary Statistics of Distribution of Inflation Series
Source: Authors’ Computations
Gambia
Ghana
Guinea
Nigeria
Sierra Leone
Mean
6.128
15.908
16.930
11.551
7.085
Median
5.591
14.732
13.842
11.356
6.890
Maximum
21.073
41.944
42.615
28.232
17.412
Minimum
0.177
5.312
1.474
-2.486
3.367
Std. Dev.
4.151
7.356
9.883
5.240
2.087
Skewness
1.575
1.627
0.824
0.261
1.662
Kurtosis
5.495
5.745
2.797
3.643
8.521
Jarque-Bera
16.544
6.167

13. Empirical Results Contd.
Guinea has the highest average monthly inflation rate of about 15.9% and also experienced the highest inflation rate of over 42% a month.
Sierra Leone has the lowest average inflation with a monthly rate of 7.09% and also characterised with the lowest standard deviation.
Table 2: Summary Statistics of Distribution of Output Growth
Gambia
Ghana
Guinea
Nigeria
Sierra Leone
Mean
3.756
6.116
3.684
6.816
6.183
Median
4.961
5.076
3.772
5.297
5.366
Maximum
8.598
14.517
6.829
36.054
28.989
Minimum
-5.154
3.639
-2.010
-0.327
Std. Dev.
3.527
2.591
1.907
7.089
8.210
Skewness
-1.030
1.719
-0.925
3.069
0.728
Kurtosis
3.084
5.539
4.039
12.249
3.813
Jarque-Bera
38.225
40.504
25.011
Source: Authors’ Computations

14. Empirical Results Contd.
Unit Root Tests
Most of the tests rejected the null hypothesis of presence of unit root in all the five countries.
Table 3: Unit Root Tests
Notes:* indicates significance at the 1% level; ** indicates 5% significance level and *** indicates 10% level of significance
The results indicate that the series is stationary over time.
ADF
(constant term )
DF-GLS
P-P
KPSS
Gambia
-2.70*
-2.47***
-2.18
0.13***
Ghana
-2.02
-2.02***
-2.73*
0.46***
Guinea
-2.14
1.52***
0.58
Nigeria
-3.22**
-3.02
-3.41**
0.09***
Sierra Leone
1.16
-1.11***
-0.02
0.17***

15. Empirical Results Contd.
Estimates of Inflation Uncertainty
The asymmetric effects of inflation on uncertainty are indicated by the γ coefficients.
The coefficients are positive and significant at the 1% level for all the WAMZ countries, indicating that an increase in inflation causes more inflation uncertainty and vice versa.
Table 4: GARCH Models for Inflation and Inflation Uncertainty
Gambia
Ghana
Guinea
Nigeria
Sierra Leone
Inflation Equation
Intercept
0.279 (0.000)
0.027 (0.792)
(0.764)
0.360 (0.196)
0.278 (0.001)
Πt-1
0.962 (0.000)
0.998 (0.000)
0.984 (0.000)
0.963 (0.000)
Variance Equation α0
0.044 (0.021)
(0.000)
(0.000)
(0.000)
(0.000) α1
(0.005)
2.301 (0.000)
2.242 (0.000)
0.209 (0.000)
1.446 (0.000)
ARCH (β)
0.085 (0.000)
0.120 (0.233)
(0.935)
0.094 (0.008)
(0.561)
GARCH (γ)
1.000 (0.000)
0.238 (0.000)
0.296 (0.000)
0.974 (0.000)
0.370 (0.000)

16. Empirical Results Contd.
Causal Relationship between Inflation and its uncertainty
The results of the Granger causality tests as stated in equations (3) and (4) are shown in Table 5.
We reject the null hypothesis that inflation does not granger cause inflation uncertainty for all the countries and establish that inflation granger causes inflation uncertainty in all the WAMZ member countries.
There is bidirectional positive causality between inflation and inflation uncertainty in Gambia and Guinea while there is unidirectional positive causality only from inflation to inflation uncertainty in Ghana, Nigeria and Sierra Leone.
Table 5: Granger Causality between Inflation and Inflation Uncertainty using AR(p):EGARCH
variance series:
Notes: The optimal lag length is determined by the Akaike Information Criterion
*** indicates significance at the 1% level
** indicates significance at the 5% level
H0: Inflation does not granger cause inflation uncertainty
H0: Inflation uncertainty does not granger cause inflation
Optimal lag length
Gambia
***
8.517** 3
Ghana
86.567***
0.108
Guinea
29.213***
11.088*** 2
Nigeria
***
2.516 7
Sierra Leone
20.257***
1.900 6

17. Empirical Results Cont’d.
Causal Relationship between Inflation Uncertainty and Output
The results of the Granger causality tests as defined in equations (5) and (6) are shown in Table 6.
We cannot reject the null hypothesis that inflation uncertainty does not Granger cause output growth for all the countries.
Output growth Granger causes inflation uncertainty only in Sierra Leone while for the other four countries; output growth does not Granger cause inflation uncertainty.
Table 6: Granger Causality tests between inflation uncertainty and output growth using AR(p):EGARCH variance series:
Notes:The optimal lag length is determined by the Akaike Information Criterion
*** indicates significance at the 1% level
H0: Inflation uncertainty does not granger cause output growth
H0: Output growth does not granger cause inflation uncertainty
Optimal lag length
Gambia
0.667
1.482 5
Ghana
0.063
0.808 3
Guinea
0.806
1.925
Nigeria
0.777
Sierra Leone
0.017
11.710*** 4

18. Conclusion
Evidence that inflation raises inflation uncertainty provides support for Holland’s stabilization hypothesis that if inflation causes inflation uncertainty, then monetary authorities will make attempt to put inflation uncertainty in check by taking restrictive monetary policy actions to lower inflation.
Therefore, the proposed common monetary policy for all the central banks in WAMZ might lead to a symmetric real effects.
Hence, the integration of the five countries as a monetary union seems feasible.
However, there is need for better monetary stabilisation through central bank independence in all the WAMZ member countries.

19. Thank you