Journal of Statistics: Advances in Theory and ApplicationsAuthor's: Dennis K. Muriithi, A. N. Ngeretha, R. G. Muriungi and E. W. Njoroge
Pages: [31] - [43]
Received Date: February 10, 2014
Submitted by:
Modelling and forecasting the Kenyan economy is a vital concern. In this paper, the annual gross domestic product (GDP) is forecasted using autoregressive integrated moving average models (ARIMA) so as to determine the most efficient and adequate model for analyzing the Kenyan GDP. The study employed the Box-Jenkins (1976) methodology that involves stages of identification, estimation, diagnostic checking, and forecasting of a univariate time series. An exploratory research design was adopted for a sample of 51 observations. The annual data was obtained from the World Bank national accounts data, and OECD National Accounts data files for the period of 1960 to 2011. Analysis was done using Gretl-package. The results indicate that autoregressive integrated moving average models (ARIMA) (2, 1, 2) is the most adequate and efficient model. This was ascertained by comparing the various model selection criterion and the diagnostic tests for various models. A better understanding of a country’s GDP situation and future economic growth will facilitate the government in making appropriate policy measures to maintain high and stable economic growth.
Akaike information criteria, gross domestic product, auto-regressive, moving average, economic growth.
