Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita
Nathan Musembi,
Antony Ngunyi,
Anthony Wanjoya,
Thomas Mageto
Issue:
Volume 5, Issue 3, June 2019
Pages:
27-41
Received:
22 April 2019
Accepted:
27 May 2019
Published:
15 July 2019
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.
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 frequentis...
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Data Augmentation and Bayesian Methods for Multicategory Support Vector Machines
Issue:
Volume 5, Issue 3, June 2019
Pages:
42-51
Received:
30 June 2019
Accepted:
24 July 2019
Published:
7 August 2019
Abstract: The support vector machine (SVM) has become very popular within the machine learning literature. Recently, SVM has received much attention from statisticians. It is well known that for multicategory classification problem, the commonly used multicategory SVM is based on the frequentist framework. In this paper, we develop a multi-class support vector machine under the Bayesian framework. Numerical studies were performed by EM and the Bayesian algorithm Gibbs sampler. Our results have shown that the classification accuracy of the Bayesian approach is comparable to that of frequentist approaches, while Bayesian approach also has the advantage of providing estimates of uncertainty in predictions.
Abstract: The support vector machine (SVM) has become very popular within the machine learning literature. Recently, SVM has received much attention from statisticians. It is well known that for multicategory classification problem, the commonly used multicategory SVM is based on the frequentist framework. In this paper, we develop a multi-class support vect...
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