Artificial neural network application in gross domestic product forecasting: an Indonesia case

By: Liliana and Togar Alam Napitupulu

Abstract: Gross Domestic Product (GDP) is a benchmark for
economic production conditions of a country. Estimates of
economic growth in the coming year in a country has important
roles, among others as a benchmark in determining business
plans for business entities, and the basis for devising government
fiscal policy. Artificial Neural Network (ANN) has been
increasingly recognized as a good forecasting tool in various
fields. Its nature that can mimic the workings of the human brain
makes it flexible for non-linear and non-parametric data. GDP
growth forecasting techniques using ANN has been widely used in
various countries, such as the United States, Canada, Germany,
Austria, Iran, China, Japan and others. In Indonesia, forecasting
of GDP is only done by government institutions, namely National
Planning Board, using macroeconomic model. In this study, ANN
is used as a tool for forecasting GDP growth in Indonesia, using
some variables, such as GDP growth in the two previous periods,
population growth rate, inflation, exchange rate and political
stability and security conditions in Indonesia. Results from this
study indicate that ANN forecasts GDP relatively better than the
one issued by the government. Further study would be to use
ANN to predict other economic indicators.