빅데이터 기반의 국제거시경제 전망모형 개발 연구(Developing an International Macroeconomic Forecasting Model Based on Big Data)

본 연구에서는 빅데이터를 활용하여 경제성장률을 단기 전망하고, 전통적 통계모형 및 구조적 거시모형의 전망과 비교하여 예측 성과를 분석한다. 미국과 한국의 경제성장률을 예측하기 위해 대량의 거시·금융 지표와 머신러닝을 사용하며, 네이버 검색 데이터와 동적모형 평균화 및 선택을 활용하여 한국의 경제성장률을 전망한다. 이를 통해 빅데이터가 경제성장률 예측 성과에 미치는 영향을 살펴본다. 마지막으로 빅데이터 기반의 전망과 소규모 개방경제 동태확률일반균형 모형의 전망을 종합하여 시사점과 향후 경제전망 연구 방향을 제안한다. The economic uncertainties arising from recent global inflation and the Covid-19 pandemic have significantly amplified the importance of accuracy and timeliness in macroeconomic forecasts. To enhance the predictive abilities of models, harnessing all potentially relevant information is crucial. The advent of big data has spurred active exploration in economic forecasting research, leveraging additional data dimensions. Notably, text data such as online searches and news articles are widely employed to extract sentiments of economic agents, thereby monitoring economic and financial conditions. Additionally, machine learning has emerged as a pivotal tool in macroeconomic forecasting because it efficiently processes and analyzes big data. Given the potential benefits of big data for forecasting and the ongoing development of new methodologies, a collective analysis of forecasts based on big data and traditional macroeconomic models is essential. In this study, we analyze the predictive ability of short-term GDP growth rate forecasts based on big data against those generated by traditional statistical and structural macroeconomic models. Given the contrasting characteristics between big data-based forecasting models and structural models, we comprehensively analyze the results of each model and discuss implications for future economic forecasting research. This study largely consists of four parts. In Chapter 2, we utilize a small open economy dynamic stochastic general equilibrium model (SOE-DSGE) to forecast Korea’s GDP growth. This theoretical model serves as a benchmark for comparing against big data-based forecasts. Using a Bayesian framework, the model examines the impacts of various shocks, such as those related to total factor productivity, government spending, monetary policy, foreign demand, and foreign monetary policy. The findings reveal that the response of model variables to external shocks align with real-world outcomes. One of the strengths of the SOE-DSGE model is that it explicitly includes structural shocks, allowing us to analyze not only forecasts but also the effects of economic policies. However, a limitation is its inability to fully leverage available data due to inherent model constraints.(the rest omitted)