1. BURUNDI CASE STUDY

2. Methodology and Data
Data are monthly frequency : April 2010 – December 2013
Series are GDP, M2, CPI, NEER, LABOR & OILP
Steps:
Testing stationarity
Lag specification
VAR stability
Causality
Impulse response

3. STATIONARITY TEST trend constant Critical Values 2.79 and 3.53
GDP
-0.137
-1.873
INF M
-5.267
-1.097
TCEN
-0.348
-1.733
CPI
-1.561
-0.372
LABOR
-0.029
-2.168
OILP
-4.022
The series are difference stationary and are not depending up trend nor constant according to the results highlighted

4. VAR MODELLING Lag LogL LR FPE AIC SC HQ 326.4786445865379 NA 3.69e-12
The optimal lag chosen is 1 according to:
Lag
LogL LR
FPE
AIC SC HQ NA
3.69e-12 1
7.39e-17 * * 2 *
6.58e-17* * 3
9.64e-17

5. Stability test
Root
Modulus

6. Results LOG(GDP) LOG(CPI) LOG(M2) LOG(TCEN) LOG(GDP(-1)) 0.995719
LOG(GDP)
LOG(CPI)
LOG(M2)
LOG(TCEN)
LOG(GDP(-1))
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LOG(CPI(-1))
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LOG(M2(-1))
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LOG(TCEN(-1))
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LOG(LIBOR)
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LOG(OILP)
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7. Granger causality test
Null Hypothesis:
Obs
F-Statistic
Prob.
LOG(M2) does not Granger Cause LOG(GDP) 44
0.9368
LOG(GDP) does not Granger Cause LOG(M2)
0.0001
LOG(LIBOR) does not Granger Cause LOG(GDP)
0.0527
LOG(GDP) does not Granger Cause LOG(LIBOR)
0.1349
LOG(M2) does not Granger Cause LOG(CPI)
0.2293
LOG(CPI) does not Granger Cause LOG(M2)
0.0174

8. Impulse response