Development of a New Model of Gross Domestic Product Forecasting

Abstract

Economic growth is a result of the increase in real Gross Domestic Product (GDP). Countries or international organizations estimate economic growth to predict the future cycle of the economy. Thus, decision-makers will be able to develop early policies against future situations. In this study, factorial designs, one of the experimental design methods, are used to estimate economic growth. Economic growth and growth estimation studies frequently used time series analysis and econometric methods to determine the factors. In this paper, we analyzed the correlation of the factors such as the inflation rate, unemployment rate, industrial production index, foreign trade volume to GDP ratio, and the ratio of gross external debt stock to GDP by using the correlation analysis. Then, we determined a novel regression model. The output of the regression model is the rate of change in GDP. The novel forecasting model emerges when providing a suitable regression model. In this study, we present a novel 2k factorial design methodology to solve the GDP forecasting problem. It is different from conventional forecasting models that require complex statistical evaluations. Furthermore, we propose a general framework of the model from the econometrics perspectives and a numerical solution to illustrate this demonstration.