DOI QR코드

DOI QR Code

동적요인모형에 기반한 한국의 GDP 성장률 예측

Forecasting Korea's GDP growth rate based on the dynamic factor model

  • 이경서 (중앙대학교 응용통계학과) ;
  • 임예지 (중앙대학교 응용통계학과)
  • Kyoungseo Lee (Department of Applied Statistics, Chung-Ang University) ;
  • Yaeji Lim (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2023.11.20
  • 심사 : 2023.12.11
  • 발행 : 2024.04.30

초록

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.

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.

키워드

과제정보

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

참고문헌

  1. Angelini E, Camba-Mendez G, Giannone D, Reichlin L, and Runstler G (2011). Short-term forecasts of euro area GDP growth, The Econometrics Journal, 14, C25-C44. https://doi.org/10.1111/j.1368-423X.2010.00328.x
  2. Doz C, Giannone D, and Reichlin L (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering, Journal of Econometrics, 164, 188-205. https://doi.org/10.1016/j.jeconom.2011.02.012
  3. Eichler M, Motta G, and Von Sachs R (2011). Fitting dynamic factor models to non-stationary time series, Journal of Econometrics, 163, 51-70. https://doi.org/10.1016/j.jeconom.2010.11.007
  4. Forni M, Hallin M, Lippi M, and Reichlin L (2000). The generalized dynamic-factor model: Identification and estimation, Review of Economics and Statistics, 82, 540-554. https://doi.org/10.1162/003465300559037
  5. Geweke J (1977). "The Dynamic Factor Analysis of Economic Time Series," in Latent Variables in Socio-Economic Models, ed. by D.J. Aigner and A.S. Goldberger, Amsterdam: North-Holland.
  6. Giannone D, Reichlin L, and Small D (2008). Nowcasting: The real-time informational content of macroeconomic data, Journal of Monetary Economics, 55, 665-676. https://doi.org/10.1016/j.jmoneco.2008.05.010
  7. Hoerl AE and Kennard RW (1970). Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, 12, 55-67. https://doi.org/10.1080/00401706.1970.10488634
  8. Kim C and Kim H (2016). Study of nowcasting for GDP growth, Bank of Korea, 2016.
  9. Lee H, Chio D, and Kim Y (2022). Development of the GDP nowcasting system based on the dynamic factor model and deep learning algorithm, Bank of Korea, 28(2), 1-37.
  10. Li J and Chen W (2014). Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models, International Journal of Forecasting, 30, 996-1015. https://doi.org/10.1016/j.ijforecast.2014.03.016
  11. Sargent TJ and Sims CA (1977). "Business Cycle Modeling Without Pretending to Have Too Much A-Priori Economic Theory," in New Methods in Business Cycle Research, ed. by C. Sims et al., Minneapolis: Federal Reserve Bank of Minneapolis.
  12. Stock JH and Watson MW (2002). Forecasting using principal components from a large number of predictors, Journal of the American Statistical Association, 97, 1167-1179. https://doi.org/10.1198/016214502388618960
  13. Tibshirani R (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society Series B: Statistical Methodology, 58, 267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x