• KDI Journal of Economic Policy
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• v.36 no.3
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• pp.95-120
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• 2014

This paper attempts to demonstrate the critical role of expectation horizons in economic agents building their expectations for the future. It starts with the analysis of what constraints the economics-based assumption related to information efficiency could impose in the stochastic process, and then suggests a new concept, random revision of expectation, to refer to the case when the adjustment process of expected variables employs newly generated information only. According to the inflation dynamics formula drawn under this condition, the demand pressure measured by output gap is found to cause different impacts on inflation according to different expectation horizons. The empirical analysis of this model using the data on Korea reveals that a short expectation horizon causes coefficient estimates to become small and statistically less significant.

• KDI Journal of Economic Policy
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• v.34 no.4
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• pp.117-156
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• 2012

This paper decomposes and estimates trend/cyclical components of some key macro variables-GDP, inflation, and interest rate, using a simple DSGE model along with flexible trend specification. The extracted cyclical components of output and interest rate are similar to HP-filtered counterparts, despite some differences in persistence and volatility, while inflation resembles that from BK filtering. This implies that the usual practice of applying a single filtering method to the data of interest may be problematic. When the baseline model is extended to incorporate consumption habit and price indexation, habit turns out to be important in explaining the persistence of business cycles. Comparison of several alternative models shows that the usual practice of estimation of DSGE model using filtered data leads to biased results. Finally, various sensitivity analyses illustrate that (1) allowing for correlation between structural cyclical shocks and trend shocks and (2) including irregular components (in inflation rate) may deliver interesting/important implication for gap estimates.