The Bank of Korea Watch

Abstract

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