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

Author

Listed:
  • Sebastian Ankargren

    (Uppsala University)

  • Måns Unosson

    (University of Warwick)

  • Yukai Yang

    (Uppsala University)

Abstract

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.

Suggested Citation

  • Sebastian Ankargren & Måns Unosson & Yukai Yang, 2018. "A mixed-frequency Bayesian vector autoregression with a steady-state prior," CREATES Research Papers 2018-32, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2018-32
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    File URL: ftp://ftp.econ.au.dk/creates/rp/18/rp18_32.pdf
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    References listed on IDEAS

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    Cited by:

    1. Sebastian Ankargren & Paulina Jon'eus, 2019. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Papers 1907.01075, arXiv.org.

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    More about this item

    Keywords

    VAR; state space models; macroeconometrics; marginal data density; forecasting; nowcasting; hyperparameters.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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