Simulation smoothing for nowcasting with large mixed-frequency VARs

Sebastian Ankargren () and Paulina Jonéus

Econometrics and Statistics, 2021, vol. 19, issue C, 97-113

Abstract: Mixed-frequency VAR models deal with data sampled at different frequencies while remaining within the realms of VARs. Estimation of mixed-frequency VARs makes use of simulation smoothing, but as the size of the model grows, these models quickly become prohibitive in nowcasting situations using the standard procedure. Two algorithms that alleviate the computational efficiency of the simulation smoothing algorithm are therefore proposed. The preferred choice is an adaptive algorithm, which augments the state vector as necessary to sample the monthly variables that are missing at the end of the sample. For large VARs, considerable improvements in speed can be shown by using the proposed adaptive algorithm. The algorithm therefore provides a crucial building block for bringing the mixed-frequency VAR model to the high-dimensional regime.

Keywords: Ragged edges; Forecasting; Bayesian; Stochastic volatility; MCMC (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Working Paper: Simulation smoothing for nowcasting with large mixed-frequency VARs (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:19:y:2021:i:c:p:97-113

DOI: 10.1016/j.ecosta.2020.05.007

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