Bayesian and Frequentist Approach to Time Series Forecasting with Application to Kenya’s GDP per Capita
International Journal of Data Science and Analysis
Volume 5, Issue 3, June 2019, Pages: 27-41
Received: Apr. 22, 2019;
Accepted: May 27, 2019;
Published: Jul. 15, 2019
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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
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.
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.
Pavia, M., Jose, M., & Cabrer, B., Borras. (2007). On estimating contemporaneous quarterly regional GDP. Journal of Forecasting, 26 (3), 155–170.
Larsson, H., & Harrtell, E. (2007). Does choice of transition model affect GDP per capita growth?
Musundi, S. W. (2016). Modeling and forecasting Kenyan GDP using autoregressive integrated moving average (arima) models. Science Journal of Applied Mathematics and Statistics, 4 (2), 64–73.
Wang, Z., & Wang, H. (2011). GDP prediction of china based on arima model. Journal of Foreign Economic and Trade, 210 (12).
Zakai, M. (2014). A time series modeling on GDP of pakistan. Journal of Contemporary Issues in Business Research, 3 (4), 200–210.
Hai, V. T., Tsui, A. K., & Zhang, Z. (2013). Measuring asymmetry and persistence in conditional volatility in real output: Evidence from three east Asian tigers using a multivariate garch approach. Applied Economics, 45, 2909–2914.
Shahini, L., & Haderi, S. (2013). Short term albanian GDP forecast:“one quarter to one year ahead”. European Scientific Journal, ESJ, 9 (34), 198–208.
Durbin, J., & Koopman, S. J. (2012b). Time series analysis by state space methods. Oxford University Press.
Harvey, A. C. (1981). Finite sample prediction and over differencing. Journal of Time Series Analysis, 2 (4), 221–232.
Harvey, A. C., & Todd, P. (1983). Forecasting economic time series with structural and box-jenkins models: A case study. Journal of Business & Economic Statistics, 1 (4), 299–307.
Harvey, A. C. (1989). Forecasting, structural time series analysis, and the kalman filter. Cambridge University Press.
Fernandez, C., Ley, E., & Steel, M. F. (2001). Model uncertainty in cross-country growth regressions. Journal of applied Econometrics, 16 (5), 563–576.
Jacobson, T., & Karlsson, S. (2004). Finding good predictors for inflation: A Bayesian model averaging approach. Journal of Forecasting, 23 (7), 479–496.
De Alba, E., Mendoza, M. et al. (2007). Bayesian forecasting methods for short time series. The International Journal of Applied Forecasting, 8, 41–44.
Spengler, J. J. (1959). Adam smith’s theory of economic growth: Part i. Southern Economic Journal, 25 (4), 397–415.
Solow, R. M. (1956). A contribution to the theory of economic growth. The quarterly journal of economics, 70 (1), 65–94.
Box, G. E., & Jenkins, G. M. (1976). Time series analysis, control, and forecasting. San Francisco, CA: Holden Day, 3226 (3228), 10.
Hamilton, K. (1994). Green adjustments to GDP. Resources Policy, 20 (3), 155–168.
Wei, N., Bian, K., Yuan, Z., et al. (2010). Analysis and forecast of shaanxi GDP based on the arima model. Asian Agricultural Research, 2 (1), 34–41.
Dritsaki, C. (2015). Forecasting real GDP rate through econometric models: An empirical study from greece. Journal of International Business and Economics, 3 (1), 13–19.
Kravis, A. W., Irving B Heston, & Summers, R. (1978). Real GDP per capita for more than one hundred countries. The Economic Journal, 88 (350), 215–242.
Kitov, O. I. (2009). The evolution of real GDP per capita in developed countries. Journal of Applied Economic Sciences, 4 (2), 221–234.
Kitov, I. O., Dolinskaya, S. et al. (2009). Modelling real GDP per capita in the (usa): Cointegration tests. Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, 4 (1), 7.
Kraay, A., & Monokroussos, G. (1999). Growth forecasts using time series and growth models. The World Bank.
Stockton, D. J., & Glassman, J. E. (1987). An evaluation of the forecast performance of alternative models of inflation. The Review of Economics and Statistics, 69 (1), 108–117.
Scott, S. L., & Varian, H. R. (2013). Predicting the present with Bayesian structural time series. Available at SSRN 2304426.
Steel, M. F. J. (2010). Bayesian time series analysis. In S. N. Durlauf & L. E. Blume (Eds.), Macroeconometrics and Time Series Analysis (pp. 35–45). London: Palgrave Macmillan UK.
Koop, G. M., & Potter, S. (2003). Forecasting in Large Macroeconomic Panels Using Bayesian Model Averaging. SSRN Electronic Journal.
Leedy, P. (1997). Practical research: Planning and design. New Jersey: Prentice-Hall.
Kothari, C. R. (2004). Research methodology: Methods and techniques. New Age International.
Sperling, R. A., Gay, L., & Airasian, P. W. (2003). Student study guide to accompany lr gay and peter airasian’s educational research: Competencies for analysis and application. Merrill.
Pole, A., West, M., & Harrison, J. (1994). Applied Bayesian forecasting and time series analysis. Chapman and Hall/CRC.