Why are Bayesian trend-cycle decompositions of US real GDP so different?
- Authors
- Kim, Jaeho; Chon, Sora
- Issue Date
- Mar-2020
- Publisher
- Springer Verlag
- Keywords
- Gibbs sampling; Structural break; Trend-cycle decomposition; Unobserved components model
- Citation
- Empirical Economics, v.58, no.3, pp 1339 - 1354
- Pages
- 16
- Indexed
- SSCI
SCOPUS
- Journal Title
- Empirical Economics
- Volume
- 58
- Number
- 3
- Start Page
- 1339
- End Page
- 1354
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114236
- DOI
- 10.1007/s00181-018-1554-0
- ISSN
- 0377-7332
1435-8921
- Abstract
- This paper provides an underlying reason for why recent Bayesian trend-cycle decompositions of US real GDP differ despite using identical unobserved components models. We stress that a pitfall in estimating unobserved components models accounts for the divergence in the empirical conclusions. Our results also show that the decline in the long-run growth rate of real GDP has been slow and gradual rather than abrupt during the post-World War II period. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
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