Corpus ID: 218122173

A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction

@article{Berninger2020ABT,
  title={A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction},
  author={Christoph Berninger and Almond Stocker and David Rugamer},
  journal={arXiv: Methodology},
  year={2020}
}
Motivated by the application to German interest rates, we propose a timevarying autoregressive model for short and long term prediction of time series that exhibit a temporary non-stationary 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… Expand

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References

SHOWING 1-10 OF 30 REFERENCES
Threshold Autoregressions for Strongly Autocorrelated Time Series
  • 28
  • Highly Influential
Forecasting the Term Structure of Government Bond Yields
  • 1,582
  • Highly Influential
  • PDF
Statistical Evidence on the Mean Reversion of Interest Rates
  • 9
  • PDF
Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models
  • 2,205
Testing for Common Trends
  • 2,134
  • PDF
The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes
  • 1,056
Modelling Nonlinear Economic Time Series
  • 251
  • PDF
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
  • 8,232
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