Abstract
Selecting and estimating parsimonious models is often desired, but hard to achieve. This is particularly true when models can potentially contain a very large number of parameters but data are scarce—as is the case for many macro-economic models in general and interest-rate models in particular. These models need to cater for a large number of potential relationships and dependencies, but are fitted on low-frequency data to focus on the bigger picture and long-term effects. To identify the ideal model and estimating it is then particularly demanding from an optimization perspective. In this paper, we suggest an evolutionary approach that considers model selection and estimation simultaneously. Numerical experiments with artificial data suggest that the approach is well suited for this type of problem. In an empirical application for short-term and long-term interest rates denominated in US dollar, euro and the Japanese yen, respectively, parsimonious model structures are identified that highlight the dependencies as well as spill-overs across maturities and currencies.
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Notes
For general considerations on interest rates and the drivers behind bond prices, see, e.g., [3].
For a more detailed presentation of VEC and related models, see, e.g., [8] and the literature quoted therein.
For a more technical discussion of co-integration and its econometric implications, see [8].
For more details on the BIC and other Information criteria, see, e.g., [8].
Note that all indexed variables are redrawn in every generation; the generation subscript is dropped for the sake of improved readability.
[12] provide numerous experiments for typical benchmark problems.
All implementations where done on state-of-the-art desktop computers using the platform R, version 3.
For more details on the test and the econometric considerations, see, e.g., [8].
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Acknowledgments
We are grateful to the anonymous referees for their valuable comments and suggestions; to seminar and conference audiences in London, Klagenfurt, Geneva, and Lisbon for their feedback; to Peter Winker and Sandra Paterlini for discussions and inputs; and to the editors of this special issue for their encouragement and support.
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Maringer, D., Deininger, S.H.M. Selecting and estimating interest rate models with evolutionary methods. Evol. Intel. 9, 137–151 (2016). https://doi.org/10.1007/s12065-016-0145-2
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DOI: https://doi.org/10.1007/s12065-016-0145-2