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This outline provides lessons learnt on testing for stationarity using appropriate models, conducting VAR analysis, and interpreting impulse response functions. The case study focuses on the test for stationarity of the CPI variable in Kenya and estimates a VAR model to analyze the relationship between GDP, money supply, exchange rates, and CPI.
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Outline • Lessons learnt • Case of Kenya: Overview • Test for Stationarity of CPI variable • VAR analysis • Policy insights
1. Lessons Learnt • How to test for stationarity using appropriate models, that is, testing for validity of including trend and/or constant term. • Testing for stability / stationarity of a VAR model. • How to use Eviews. • How to interpret impulse response functions.
2. Case of Kenya: Overview • Study period 2000:1 to 2013:3 • Data source: Central Bank of Kenya and Kenya National Bureau of Statistics • Frequency of data: Quarterly • Variables: CPI, M3, RGDP, TB3, e, libor, oilprice • Methodology: Granger causality, Johansen cointegration test, impulse response analysis
2. Test for Stationarity of CPI variable • Step 1: View time graph of variable
2. Test for Stationarity of CPI variable • Step 2: Conduct ADF test using trend and intercept F3 = 2.656 against CV = 6.50 Indicating that the trend term is not significant
2. Test for Stationarity of CPI variable • Step 3: Conduct ADF test using intercept F3 = 0.033 against CV = 8.73 Indicating that the constant term is not significant
2. Test for Stationarity of CPI variable • Step 4: Conduct ADF test without trend or intercept Conclusion: CPI is non stationary
2. Test for Stationarity of CPI variable • Step 5: Conduct ADF test on DCPI Conclusion: CPI is I(1)
3. Estimate a VAR • Step 1: Estimate unresticted VAR on log(rgdp) log(m3) tb3 log(e) log(cpi) and exogenous variables c libor log(oilprice) and 2 lags • Step 2: Test lag length criteria Choose lag length of 1
3. Estimate a VAR • Step 3: Estimate unresticted VAR on log(rgdp) log(m3) tb3 log(e) log(cpi) and exogenous variables c libor log(oilprice) and 1 lag • Step 4: Test for VAR stability
4. Analyse the VAR results • Step 1: Estimate impulse response functions • M3 has no significant impact on GDP. • TB3 has negative impact on GDP four quarters after initial shock. • Exchange rate has negative impact on GDP 5-6 quarters after the initial shock. • M3 positively impacts on CPI after 5 quarters and persists thereafter. • Interest rates and exchange rates have no significant impact on CPI.
4. Analyse the VAR results • Step 2: Variance Decomposition • After 10 quarters, 77.6% of variations in GDP is attributed to itself, 7.5% to exchange rate, 7.3% to CPI, 5.2% to interest rates and 2.5% to M3. • After 10 quarters, 64.8% of variations in CPI is attributed to itself, 17.1% to GDP, 11.9% to M3, 4.5% to exchange rates and 1.5% to interest rates.
4. Analyse the VAR results • Step 3: Granger Causality test • M3 and CPI granger cause GDP, while TB3 and exchange rates do not cause granger GDP. • M3 granger causes CPI.
4. Analyse the VAR results • Step 4: Cointegration test • Residual seems stationary, therefore, variables are cointegrated.
5. Policy insights • Excessive money is not good for inflation in the medium-term. • Raising short-term interest rates and depreciating the shilling will impact negatively on growth in the short term.
