Forecasting Kenya’s GDP using a hybrid neural network and ARIMA model
Date
2020-03
Authors
Ngige, Isabel Wanjiru
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Background: Gross Domestic Product (GDP) is the market value of goods
and services produced within a selected geographical area usually a coun-
try in a selected interval in time often a year and can be measured and
forecasted in di erent ways for use by governments and other market par-
ticipants.Speci c users of information on GDP analysis include the United
Nations0 Sustainable Development Goal assessment whose key indicator is
economic growth as measured by GDP and the joint International Mon-
etary Fund-World Bank methodology for conducting standardized debt-
sustainability analyses in low-income countries.
Objective:The main objective of this study was to assess the superiority
as suggested by Literature of a Hybrid Autoregressive Integrated Moving
Average(ARIMA) and feed forward Arti cial Neural Network (ANN) model
over a pure ARIMA model in forecasting Kenya0s GDP.
Methods: The ARIMA and the additive ANN-ARIMA Hybrid model is
used to forecast absolute GDP values and the comparative forecast accuracy
is tested using the RMSE and visualization plots.The Box-Jenkins method-
ology is used to t the ARIMA model while the feed-forward Neural Network
Autoregressive(NNAR) structure is used to model the neural network por-
tion of the hybrid model .
Description
A Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Statistical Sciences (MSc. SS) at Strathmore University
Keywords
Autoregressive Integrated Moving Average, Arti cial Neural Network, Gross Domestic Product, Neural Network Autoregressive