The Bank of Korea Watch

37 Pages Posted: 22 Jul 2021

See all articles by Hyerim Kim

Hyerim Kim

affiliation not provided to SSRN

Kyu H. Kang

Korea University

Date Written: July 21, 2021


Traders closely watch the Bank of Korea (BOK) base rate decisions since the short rate is the primary factor in bond and currency valuations. The survey of professional forecasters (SPF) has been widely used as the most reliable BOK base rate decision forecaster. In this paper, we investigate whether the SPF's prediction ability can be improved further. To this end, we use a dynamic multinomial ordered probit prediction model of the BOK base rate with a large number of predictors, and apply a Bayesian variable selection algorithm. Through an empirical exercise, we show that our approach substantially outperforms the SPF in terms of out-of-sample prediction. The key predictors are found to be the SPF, short-term bond yields, lagged base rate, federal funds rate, and inflation expectation survey data. Further, allowing for the prediction abilities to change over time is essential for improving predictive accuracy.

Keywords: policy rate, variable selection, Bayesian machine learning, out-of-sample prediction

JEL Classification: C52, E58, G17

Suggested Citation

Kim, Hyerim and Kang, Kyu H., The Bank of Korea Watch (July 21, 2021). Available at SSRN: or

Hyerim Kim

affiliation not provided to SSRN

Kyu H. Kang (Contact Author)

Korea University ( email )

1 Anam-dong 5 ka
Seoul, 136-701

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