Korea Trade Review (무역학회지)

Korea Trade Research Association (한국무역학회)
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Prediction on Busan's Gross Product and Employment of Major Industry with Logistic Regression and Machine Learning Model

로지스틱 회귀모형과 머신러닝 모형을 활용한 주요산업의 부산 지역총생산 및 고용 효과 예측

  • Chae-Deug Yi (Department of International Trade, Pusan National University)
  • Received : 2022.03.27
  • Accepted : 2022.04.29
  • Published : 2022.04.30
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Abstract

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

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