Volume 5, Issue 3, June 2019, Page: 27-41
Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita
Nathan Musembi, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Antony Ngunyi, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Thomas Mageto, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Received: Apr. 22, 2019;       Accepted: May 27, 2019;       Published: Jul. 15, 2019
DOI: 10.11648/j.ijdsa.20190503.11      View  126      Downloads  39
Abstract
Real GDP per capita is an important indicator of a country’s or regional economic activity and is often used by decision makers in the development of economic policies. Expectations about future GDP per capita can be a primary determinant of investments, employment, wages, profits and stock market activities. This study employed both the frequentist and the Bayesian approaches to Kenya’s GDP per capita time series data for the period between 1980-2017 as obtained from the World Bank data portal. The autoregressive integrated moving average (ARIMA) and the state space models were fitted. The results of the study showed that the local linear trend model and the ARIMA(1,2,1) model are appropriate for forecasting the GDP per capita but the former outperforms the latter. The local linear trend model was used to perform a 3-step ahead forecast and the forecasted value was found to be U.S $ 1717.694, U.S $ 1844.446 and U.S $ 1971.198 for 2018, 2019 and 2020 respectively. The findings of this study showed that the state space models, which utilize the Bayesian approach, outperform the ARIMA models which use the frequentist approach in time series forecasting.
Keywords
ARIMA Model, State Space Model, Kalman Filter, Kalman Smoother, GDP per Capita, Forecast
To cite this article
Nathan Musembi, Antony Ngunyi, Anthony Wanjoya, Thomas Mageto, Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita, International Journal of Data Science and Analysis. Vol. 5, No. 3, 2019, pp. 27-41. doi: 10.11648/j.ijdsa.20190503.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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