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Modeling and forecasting short-term interest rates : The benefits of smooth regimes, macroeconomic variables, and bagging
Journal
Journal of Applied Econometrics
ISSN
0883-7252
ISSN-Digital
1099-1255
Type
journal article
Date Issued
2011-09
Author(s)
Medeiros, Marcelo C.
DOI
Abstract
In this paper we propose a smooth transition tree model for both the
conditional mean and variance of the short-term interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Model specification is also discussed. When the model is applied to the US short-term interest rate we find (1) leading indicators for inflation and real activity are the most relevant predictors in characterizing the multiple regimes' structure; (2) the optimal model has three limiting regimes. Moreover, we provide empirical evidence of the power of the model in forecasting the first two conditional moments when it is used in connection with bootstrap aggregation (bagging).
conditional mean and variance of the short-term interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Model specification is also discussed. When the model is applied to the US short-term interest rate we find (1) leading indicators for inflation and real activity are the most relevant predictors in characterizing the multiple regimes' structure; (2) the optimal model has three limiting regimes. Moreover, we provide empirical evidence of the power of the model in forecasting the first two conditional moments when it is used in connection with bootstrap aggregation (bagging).
Language
English
Keywords
short-term interest rate
regression tree
smooth
transition
transition
conditional variance
bagging
asymptotic theory.
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Wiley
Publisher place
Chichester UK
Volume
26
Number
6
Start page
999
End page
1022
Pages
24
Subject(s)
Division(s)
Eprints ID
57802