Abstract
are trend in‡ation and the non-accelerating in‡ation rate of unemployment (NAIRU). Our model also incorporates a time-varying Phillips curve and time-varying in‡ation persistence. What sets this paper apart from the existing literature is that we do not use unbounded random walks for the unobserved components, but rather use bounded random walks. For instance, NAIRU is assumed to evolve within bounds. Our empirical work shows the importance of bounding. We …nd that our bounded bivariate model forecasts better than many alternatives, including a version of our model with unbounded unobserved components. Our model also yields sensible estimates
of trend in‡ation, NAIRU, in‡ation persistence and the slope of the Phillips curve.
| Language | English |
|---|---|
| Pages | 551-565 |
| Number of pages | 5 |
| Journal | Journal of Applied Econometrics |
| Volume | 31 |
| Issue number | 3 |
| Early online date | 15 Jan 2015 |
| DOIs | |
| Publication status | Published - 1 Apr 2016 |
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Keywords
- trend ination
- non-linear state space model
- natural rate of unemployment
- ination targeting
- Bayesian
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A bounded model of time variation in trend inflation, NAIRU and the Phillips curve. / Chan, Joshua C. C.; Koop, Gary; Potter, Simon M.
In: Journal of Applied Econometrics, Vol. 31, No. 3, 01.04.2016, p. 551-565.Research output: Contribution to journal › Article
TY - JOUR
T1 - A bounded model of time variation in trend inflation, NAIRU and the Phillips curve
AU - Chan, Joshua C. C.
AU - Koop, Gary
AU - Potter, Simon M.
PY - 2016/4/1
Y1 - 2016/4/1
N2 - In this paper, we develop a bivariate unobserved components model for in‡ation and unemployment. The unobserved componentsare trend in‡ation and the non-accelerating in‡ation rate of unemployment (NAIRU). Our model also incorporates a time-varying Phillips curve and time-varying in‡ation persistence. What sets this paper apart from the existing literature is that we do not use unbounded random walks for the unobserved components, but rather use bounded random walks. For instance, NAIRU is assumed to evolve within bounds. Our empirical work shows the importance of bounding. We …nd that our bounded bivariate model forecasts better than many alternatives, including a version of our model with unbounded unobserved components. Our model also yields sensible estimatesof trend in‡ation, NAIRU, in‡ation persistence and the slope of the Phillips curve.
AB - In this paper, we develop a bivariate unobserved components model for in‡ation and unemployment. The unobserved componentsare trend in‡ation and the non-accelerating in‡ation rate of unemployment (NAIRU). Our model also incorporates a time-varying Phillips curve and time-varying in‡ation persistence. What sets this paper apart from the existing literature is that we do not use unbounded random walks for the unobserved components, but rather use bounded random walks. For instance, NAIRU is assumed to evolve within bounds. Our empirical work shows the importance of bounding. We …nd that our bounded bivariate model forecasts better than many alternatives, including a version of our model with unbounded unobserved components. Our model also yields sensible estimatesof trend in‡ation, NAIRU, in‡ation persistence and the slope of the Phillips curve.
KW - trend ination
KW - non-linear state space model
KW - natural rate of unemployment
KW - ination targeting
KW - Bayesian
U2 - 10.1002/jae.2442
DO - 10.1002/jae.2442
M3 - Article
VL - 31
SP - 551
EP - 565
JO - Journal of Applied Econometrics
T2 - Journal of Applied Econometrics
JF - Journal of Applied Econometrics
SN - 0883-7252
IS - 3
ER -