| dc.description.abstract | Inflation is the persistent rise in the prices of selected goods and services over time.
The rate of inflation measures economic performance of a country and is an important
economic indicator to economists of any given government. High rates of inflation lead to
slow economic growth and has the effect of lowering the living standards of a population
by eroding their purchasing power. In the period November 2016 to June 2017, Kenya
experienced an unprecedented rise in the inflation rate to a high of 11.7% causing harsh
economic and social repercussions to her population [5]. To cushion its population against
such strain, the government should be able to estimate and predict the rate of inflation.
Previous research by Bilal Kargi for Turkey’s case [6] indicates a relationship between the
changes in price levels of imported crude oil and the rate of inflation. The objective of this
study was to determine if there is a long-run relationship between Kenya’s Inflation rate
and the price of imported crude oil, fit a VARMA (p,q) model and use the fitted model
to forecast Kenya’s inflation rate using the previous rates of inflation and the price of
imported crude oil since there was a cointegrated association between the two time series.
This will enable the government plan strategically for the mid- and long-term effects of
inflation in Kenya. Cross-correlation analysis was used to determine whether there is a
significant correlation between the two time series and a test of cointegration was used
to determine a significant association. A VARMA model was fitted to the data using
the SCM approach. The study showed that there exists a moderate negative correlation
between the two time series with a correlation coefficient of -0.21, with a p-value of < 0.05
that implies that the correlation is statistically significant. The study further showed that
there is a moderate statistically significant association between the two time series at lags
6 and that there exists cointegration and dependencies between the price of imported
crude oil and the Kenya’s Inflation rate by a CADF test which a statistically significant
Dickey Fuller Statistic of −8.3394, with a p − value = 0.01, implying cointegrating association
between the two time series. A VARMA (2, 1) model was fitted to the data and
used to forecast Kenya’s inflation rates to six steps (months) behind for comparison to
the actual available data and further a eleven-steps ahead forecast. The forecasts were
accurate with a Mean Absolute Error (MAE) of 0.66% which are good forecasts according
to [17] for planning purposes. From the study results it shows that there exists a
statistically significant association between the price of crude oil and Kenya’s previous
inflation rates and therefore used in forecasting future Kenya’s inflation rates. This study
therefore provides better inflation forecasts(Kenya) to be used for strategical planning for
the mid- and long-term effects of inflation by the government. | en_US |
