Abstract:
The e ects of in
ation on the economy are diverse and can both be bene cial or detrimental
to the economy. The negative e ects are however most pronounced and comprises a decrease
in real value of money as well as other monetary variables. As a result uncertainty over future
in
ation rate may discourage consumers. It may also lead into decrease in foreign investments
in a country. The purpose of this study was to determine an e ective Arch-type model for
forecasting Kenya's in
ation. Using Kenya monthly in
ation data from January 1990 to
December 2015, the performance of GARCH and EGARCH type models were analyzed to
come up with the best model for forecasting Kenyan in
ation data. Since the in
ation series
is non-stationary, the Consumer Price Index (CPI) was rst transformed to return series
by logarithmic transformation. Afterwards, the data was tested for the presence of ARCH
e ects and serial correlation using both Ljung Box Pierce Q test and Engle Arch test. The
test showed presence of heteroscedasticity and correlation in the in
ation return series which
is a key feature of a nancial time series data.The project adopted AIC and BIC approach
in selecting the the best model. From the tted models EGARCH(1,1) had the smallest AIC
and BIC values followed by the GARCH(1,1) model. Model diagnostic test was conducted
on the selected model EGARCH (1,1) to determine its adequacy and goodness of t. QQ
plot was tted to the residuals of the model and fairly straight line was produced looking
roughly linear.Furthermore weighted Ljung Box Test on standard squared residuals showed
the absence of correlation in the model. In conclustion, EGARCH(1,1) model is the best
model for forecasting Kenyan in
ation data.
