Eunkyu Seong

Room 228, Bldg 16

1 Gwanak-ro, Gwanak-gu

Seoul 08826, Republic of Korea

My first name is Eunkyu, pronounced as “Ng-Q”, and my surname is Seong (“Sung”) (성은규). I am a Ph.D. candidate in Economics at Seoul National University, supervised by Associate Professor Seojeong (Jay) Lee.

You can find my full CV here: Web CV · PDF.

I study econometric methods for dynamic causal inference with time series data, with particular interest in instrumental variable estimation.

Research Interests

Time series econometrics
Dynamic causal inference
Instrumental variables

News

Jun 13, 2026	The 20th International Symposium on Econometric Theory and Applications (SETA 2026) [website], University of Tokyo, Tokyo (Scheduled in June)
Jun 09, 2026	SNU Econometrics Brown Bag [website], Seoul National University, Seoul (Scheduled in June)
Feb 06, 2026	2026 Korea’s Allied Economic Associations Annual Meeting [program], Chungang University, Korea
Dec 09, 2025	Australasian Econometric Conference of the Association of Econometricians (AE2) [website], Macquarie University, Australia
Aug 23, 2025	Workshop on Recent Advances in Econometrics, Seoul National University, Korea [program]

Publications

JoF

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

Eunkyu Seong, and Seojeong Lee

Journal of Forecasting, 45(3), Apr 2026

Abs DOI PDF Blog 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 state dependency, 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.