Bayesian semiparametric forecasts of real interest rate data
(2016) , 21 pages
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- Abstract
- The non-hierarchical Dirichlet process prior has been mainly used for parameters of innovation distributions. It is, however, easy to apply to all the parameters (coefficients of covariates and innovation variance) of more general regression models. This paper investigates the predictive performance of a simple (non-hierarchical) Dirichlet process mixture of Gaussian autoregressions for forecasting monthly US real interest rate data. The results suggest that the number of mixture components increases sharply over time, and the predictive marginal likelihoods strongly dominate those of a benchmark autoregressive model. Unconditional predictive coverage is vastly improved in the mixture model.
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Citations
Deschamps, P. (2016). Bayesian semiparametric forecasts of real interest rate data (CORE Discussion Papers 2016/50). https://hdl.handle.net/2078.5/182034