Bayesian Semiparametric Forecasts of Real Interest Rate Data

Philippe Deschamps

No 2016050, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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.

Keywords: Dirichlet process mixture; Bayesian nonparametrics; structural change; real interest rate (search for similar items in EconPapers)
JEL-codes: C11 C14 C22 C53 (search for similar items in EconPapers)
Date: 2016-11-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2016050

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