Mathematical Approach for Predicting the Gross Domestic Product of Malaysia

  • Maryam Rukayyah Al-Munirah Ayob School of Quantitative Sciences, Universiti Utara Malaysia (UUM)
  • Azizah Mohd Rohni School of Quantitative Sciences, Universiti Utara Malaysia (UUM)
Keywords: gross domestic product, linear regression, exponential regression, parabolic regression

Abstract

Gross domestic product (GDP) is a monetary measure of the market value of overall final goods and services produced in a given year, and serves as a gauge of the economy’s overall health and size. The GDP prediction is significant, as it can capture and understand the future developments of a country’s economy. In this paper, three different mathematical models have been used to predict Malaysia’s gross domestic product using regressions. The models discussed in this paper are linear, exponential and parabolic regressions. In developing the models, data from year 1970 to 2014 has been employed and data from year 2015 to 2019 has been used to examine the models' accuracy. The models are then observed to identify the most appropriate to express the relationship between the years and Malaysia’s gross domestic product. In this study, it is found that the parabolic regression model is more accurate compared to the linear and exponential regression models. The parabolic regression model is also the most appropriate since it is adjusted to the real conditions of Malaysia's gross domestic product which is the main subject of this paper. Finally, it is obtained that the prediction values of GDP in Malaysia will increase for the next ten years (2020 - 2029).

 

Keywords: Gross domestic product, Linear regression, Exponential regression, Parabolic regression

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Published
2020-10-02
How to Cite
Al-Munirah Ayob, M. R. and Mohd Rohni, A. (2020) “Mathematical Approach for Predicting the Gross Domestic Product of Malaysia”, Malaysian Journal of Social Sciences and Humanities (MJSSH), 5(10), pp. 247 - 257. doi: https://doi.org/10.47405/mjssh.v5i10.491.
Section
Articles