The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Forecasting Korea's GDP growth rate based on the dynamic factor model

동적요인모형에 기반한 한국의 GDP 성장률 예측

  • Kyoungseo Lee (Department of Applied Statistics, Chung-Ang University) ;
  • Yaeji Lim (Department of Applied Statistics, Chung-Ang University)
  • 이경서 (중앙대학교 응용통계학과) ;
  • 임예지 (중앙대학교 응용통계학과)
  • Received : 2023.11.20
  • Accepted : 2023.12.11
  • Published : 2024.04.30
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Abstract

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.

GDP는 한 나라의 가계, 기업, 정부 등 모든 경제 주체가 일정 기간 동안 창출한 재화와 서비스의 시장 가치의 합을 나타낸다. GDP를 통하여 국가의 경제 규모를 파악할 수 있으며, 정부의 정책 방향에 영향을 미치는 대표적인 경제 지표이므로 이에 대한 연구가 다양하게 이루어지고 있다. 본 논문에서는 G20 국가들의 주요 거시경제 지표를 활용하여 dynamic factor model 기반의 GDP 성장률 예측 모델을 제시하였다. 추출된 factor를 다양한 회귀분석 방법론과 결합하여 그 결과들을 비교하였으며, 기존의 전통적인 시계열 예측방법인 ARIMA 모델, common component를 이용한 예측 등도 함께 비교하였다. COVID 이후 지표의 변동성이 큰 점을 고려하여 예측 시기를 COVID 전후로 나누었으며, 그 결과 factor에 대해 ridge regression과 lasso regression을 적용하여 예측한 경우 가장 좋은 성능을 나타내었다.

Keywords

Acknowledgement

이 논문은 한국연구재단 기초연구사업의 지원을 받아 수행된 연구임 (No. NRF-2022R1F1A1074134).

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