Eunkyu Seong

Room 319, Bldg 223

1 Gwanak-ro, Gwanak-gu

Seoul 08826, Republic of Korea

I am Eunkyu Seong (“Un-kyoo Sung”; 성은규, 成恩圭), a Ph.D. candidate in Economics at Seoul National University. I study econometric methods for causal inference with instrumental variables (IV). My research focuses on IV-based approaches in time-series and macroeconometric setting, with particular emphasis on estimating for impulse response function via local projections.

Research Interests

Instrumental Variable
Local projection

Presentations and Talks

2026
- The Korean Economic Association Joint Conference 2026 (Scheduled), Chungang University, Korea
2025
- Australasian Econometric Conference of the Association of Econometricians (AE2) [website], Macquarie University, Australia
- Workshop on Recent Advances in Econometrics, Seoul National University, Korea [program]
- Invited Seminar, University of Sydney, Australia [website]
- The 19th International Symposium on Econometric Theory and Applications, University of Macau, Macau [website]
- The Korean Economic Association Joint Conference 2025, Hannam University, Korea
2024
- Econometrics Workshop, Seoul National University, Korea

Research

JoF

Are the Bank of Korea’s Inflation Forecasts Biased Toward the Target?

Eunkyu Seong, and Seojeong Lee

Journal of Forecasting, Dec 2025

Abs DOI PDF Website

The Bank of Korea (BoK) regularly publishes the Economic Outlook, offering forecasts for key macroeconomic variables such as GDP growth, inflation, and unemployment rates. This study examines whether the BoK’s inflation forecasts exhibit bias, specifically a tendency to align with its inflation target. We extend the Holden and Peel (1990) test to incorporate statedependency, defining the state of the economy based on whether realized inflation falls below the target at the time of the forecast. Our analysis reveals that the BoK’s inflation forecasts are biased under this state-dependent framework. Furthermore, we examine a range of bias correction strategies based on AR(1) and mean error models, including their state-dependent variants. These strategies generally improve forecast accuracy. Among them, the AR(1)-based correction exhibits relatively stable performance, consistently reducing the root mean square error.