| dc.contributor.author | Cheruiyot, Braden Kipkirui | |
| dc.contributor.author | Onyango, Rodgers Otieno | |
| dc.contributor.author | Mogesi, Maurine Boke | |
| dc.contributor.author | Kisina, Maureen Mumbua | |
| dc.contributor.author | Arani, Michelle Mokeira | |
| dc.date.accessioned | 2024-07-12T14:03:20Z | |
| dc.date.available | 2024-07-12T14:03:20Z | |
| dc.date.issued | 2024-04-19 | |
| dc.identifier.uri | https://doi.org/10.51867/Asarev.Maths.1.1.7 | |
| dc.identifier.uri | https://asarev.net/ojs/index.php/asarev/article/view/8 | |
| dc.identifier.uri | http://ir-library.mmust.ac.ke:8080/xmlui/handle/123456789/2934 | |
| dc.description.abstract | This study was to investigates the dynamics of inflation in Kenya through the application of advanced time series modeling techniques, specifically Autoregressive Integrated Moving Average (ARIMA) analysis. Inflation is a critical economic indicator that directly influences monetary policy, investment decisions, and overall economic stability. Given the dynamic of inflation in emerging economies such as Kenya, a fine understanding of its patterns and the ability to make accurate forecasts are imperative for policymakers, businesses, and investors. The ARIMA (2,2,2) model was employed to capture the underlying trend and seasonality in the inflation data, providing insights into the historical behavior of inflation in Kenya. In this study, we used R programming software and STATA to analyze and generate meaningful information from the data. The data was obtained from World Bank for a period from 1960 to 2022. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | AFRICAN SCIENTIFIC ANNUAL REVIEW | en_US |
| dc.subject | Forecasting, Inflation, ARIMA, Model | en_US |
| dc.title | Forecasting Inflation in Kenya Using ARIMA Model | en_US |
| dc.type | Article | en_US |
