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A computationally efficient method for vector autoregression with mixed frequency data

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  • Qian, Hang

Abstract

A linear transformation method is proposed to handle the vector autoregression with mixed frequency time series data. Temporally aggregated observations impose linear constraints on the distribution of latent variables, which are converted such that each observation replaces a latent variable. Full-sample transformation yields a closed-form simulation smoother, while partial-sample transformation leads to a computationally efficient sampler suitable for parallel computing.

Suggested Citation

  • Qian, Hang, 2016. "A computationally efficient method for vector autoregression with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 433-437.
    • Handle: RePEc:eee:econom:v:193:y:2016:i:2:p:433-437
    DOI: 10.1016/j.jeconom.2016.04.016
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    References listed on IDEAS

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    Cited by:

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