Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/284759 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 41 [Issue:] 1 [Year:] 2021 [Pages:] 181-200
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
Motivated by the application to German interest rates, we propose a time‐varying autoregressive model for short‐term and long‐term prediction of time series that exhibit a temporary nonstationary behavior but are assumed to mean revert in the long run. We use a Bayesian formulation to incorporate prior assumptions on the mean reverting process in the model and thereby regularize predictions in the far future. We use MCMC‐based inference by deriving relevant full conditional distributions and employ a Metropolis‐Hastings within Gibbs sampler approach to sample from the posterior (predictive) distribution. In combining data‐driven short‐term predictions with long‐term distribution assumptions our model is competitive to the existing methods in the short horizon while yielding reasonable predictions in the long run. We apply our model to interest rate data and contrast the forecasting performance to that of a 2‐Additive‐Factor Gaussian model as well as to the predictions of a dynamic Nelson‐Siegel model.
Subjects: 
Bayesian time‐varying autoregressive models
Gibbs sampler
interest rate models
long run regularization
MCMC metropolis‐Hastings
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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