Forecasting of the inflation rates in Kenya: a comparison of ANN, ARIMA and SARIMA
| dc.contributor.author | Kogei, Victor Kiprono | |
| dc.date.accessioned | 2022-06-13T06:30:10Z | |
| dc.date.available | 2022-06-13T06:30:10Z | |
| dc.date.issued | 2021 | |
| dc.description | A Research Thesis Submitted to the Graduate School in partial fulfillment of the requirements for the Award of Master of Science Degree in Statistical Sciences at Strathmore University | en_US |
| dc.description.abstract | Monetary policies like price stability are regulated by the Central Bank of Kenya (CBK). Price stability is a key indicator of stable and predictable inflation. Accuracy and reliability in forecasting the inflation rates or predicting its trend correctly are very essential to investors, academia and policymakers. This call for the need to have models with an accurate prediction of the inflation rates to spur investment and economic growth. The use of an intelligence-based model has been found to be robust in forecasting financial and economic series like inflation rates and stock prices. This research, therefore, employs the use of the artificial neural network to forecast the inflation rates in Kenya and compared its performance with statistical models ARIMA and SARIMA. The artificial neural network models emulate the information processing capabilities of neurons of the human brain, thus making them flexible to map input and output well. A major advantage of ANNs is its ability to capture linear and non-linear data due to lack of assumptions, unlike statistical models. The inflation rates data, Gross domestic product (GDP) and exchange rates were the variables used. The variables are monthly data from January 2012 to February 2021. The prediction performances of the three models were evaluated through RMSE, MAE and MAPE. The results obtained show that artificial neural networks outperformed ARIMA and SARIMA models. The implication is that the government can adopt an artificial neural network for forecasting inflation rates in Kenya. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11071/12815 | |
| dc.language.iso | en | en_US |
| dc.publisher | Strathmore University | en_US |
| dc.subject | Monetary policies | en_US |
| dc.subject | Inflation rates | en_US |
| dc.subject | Central Bank of Kenya (CBK) | en_US |
| dc.title | Forecasting of the inflation rates in Kenya: a comparison of ANN, ARIMA and SARIMA | en_US |
| dc.type | Thesis | en_US |