A computationally efficient method for vector autoregression with mixed frequency data

Hang Qian

Journal of Econometrics, 2016, vol. 193, issue 2, 433-437

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.

Keywords: VAR; Kalman filter; Bayesian (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:193:y:2016:i:2:p:433-437

DOI: 10.1016/j.jeconom.2016.04.016

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