Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita

Nathan Musembi; Antony Ngunyi; Anthony Wanjoya; Thomas Mageto

doi:doi:10.11648/j.ijdsa.20190503.11

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Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita

Nathan Musembi, Antony Ngunyi, Anthony Wanjoya, Thomas Mageto

Published in International Journal of Data Science and Analysis (Volume 5, Issue 3)

Received: 22 April 2019 Accepted: 27 May 2019 Published: 15 July 2019

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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.

Published in	International Journal of Data Science and Analysis (Volume 5, Issue 3)
DOI	10.11648/j.ijdsa.20190503.11
Page(s)	27-41
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

ARIMA Model, State Space Model, Kalman Filter, Kalman Smoother, GDP per Capita, Forecast

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APA Style

Nathan Musembi, Antony Ngunyi, Anthony Wanjoya, Thomas Mageto. (2019). Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita. International Journal of Data Science and Analysis, 5(3), 27-41. https://doi.org/10.11648/j.ijdsa.20190503.11

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ACS Style

Nathan Musembi; Antony Ngunyi; Anthony Wanjoya; Thomas Mageto. Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita. Int. J. Data Sci. Anal. 2019, 5(3), 27-41. doi: 10.11648/j.ijdsa.20190503.11

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AMA Style

Nathan Musembi, Antony Ngunyi, Anthony Wanjoya, Thomas Mageto. Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita. Int J Data Sci Anal. 2019;5(3):27-41. doi: 10.11648/j.ijdsa.20190503.11

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@article{10.11648/j.ijdsa.20190503.11,
  author = {Nathan Musembi and Antony Ngunyi and Anthony Wanjoya and Thomas Mageto},
  title = {Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita},
  journal = {International Journal of Data Science and Analysis},
  volume = {5},
  number = {3},
  pages = {27-41},
  doi = {10.11648/j.ijdsa.20190503.11},
  url = {https://doi.org/10.11648/j.ijdsa.20190503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190503.11},
  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.},
 year = {2019}
}

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TY  - JOUR
T1  - Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita
AU  - Nathan Musembi
AU  - Antony Ngunyi
AU  - Anthony Wanjoya
AU  - Thomas Mageto
Y1  - 2019/07/15
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdsa.20190503.11
DO  - 10.11648/j.ijdsa.20190503.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 27
EP  - 41
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20190503.11
AB  - 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.
VL  - 5
IS  - 3
ER  -

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Author Information

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

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APA Style

Nathan Musembi, Antony Ngunyi, Anthony Wanjoya, Thomas Mageto. (2019). Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita. International Journal of Data Science and Analysis, 5(3), 27-41. https://doi.org/10.11648/j.ijdsa.20190503.11

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ACS Style

Nathan Musembi; Antony Ngunyi; Anthony Wanjoya; Thomas Mageto. Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita. Int. J. Data Sci. Anal. 2019, 5(3), 27-41. doi: 10.11648/j.ijdsa.20190503.11

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AMA Style

Nathan Musembi, Antony Ngunyi, Anthony Wanjoya, Thomas Mageto. Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita. Int J Data Sci Anal. 2019;5(3):27-41. doi: 10.11648/j.ijdsa.20190503.11

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@article{10.11648/j.ijdsa.20190503.11,
  author = {Nathan Musembi and Antony Ngunyi and Anthony Wanjoya and Thomas Mageto},
  title = {Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita},
  journal = {International Journal of Data Science and Analysis},
  volume = {5},
  number = {3},
  pages = {27-41},
  doi = {10.11648/j.ijdsa.20190503.11},
  url = {https://doi.org/10.11648/j.ijdsa.20190503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190503.11},
  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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita
AU  - Nathan Musembi
AU  - Antony Ngunyi
AU  - Anthony Wanjoya
AU  - Thomas Mageto
Y1  - 2019/07/15
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdsa.20190503.11
DO  - 10.11648/j.ijdsa.20190503.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 27
EP  - 41
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20190503.11
AB  - 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.
VL  - 5
IS  - 3
ER  -

Copy | Download