Department of Economics and Business Economics

A mixed-frequency Bayesian vector autoregression with a steady-state prior

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  • rp18_32

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    Sebastian Ankargren, Uppsala University, SwedenMåns Unosson, University of Warwick, United KingdomYukay Yang, Uppsala University, Sweden
We consider a Bayesian vector autoregressive (VAR) model allowing for an explicit prior specification for the included variables' "steady states" (unconditional means) for data measured at different frequencies. We propose a Gibbs sampler to sample from the posterior distribution derived from a normal prior for the steady state and a normal-inverse-Wishart prior for the dynamics and error covariance. Moreover, we suggest a numerical algorithm for computing the marginal data density that is useful for finding appropriate values for the necessary hyperparameters. We evaluate the proposed model by applying it to a real-time data set where we forecast Swedish GDP growth. The results indicate that the inclusion of high-frequency data improves the accuracy of low-frequency forecasts, in particular for shorter time horizons. The proposed model thus facilitates a simple and helpful way of incorporating information about the long run through the steady-state prior as well as about the near future through its ability to cope with mixed frequencies of the data.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages42
Publication statusPublished - 5 Dec 2018
SeriesCREATES Research Papers
Number2018-32

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