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Adaptive Minnesota Prior for High-Dimensional Vector Autoregressions


  • Korobilis, D
  • Pettenuzzo, D


We develop a novel, highly scalable estimation method for large Bayesian Vector Autoregressive models (BVARs) and employ it to introduce an "adaptive" version of the Minnesota prior. This flexible prior structure allows each coeffcient of the VAR to have its own shrinkage intensity, which is treated as an additional parameter and estimated from the data. Most importantly, our estimation procedure does not rely on computationally intensive Markov Chain Monte Carlo (MCMC) methods, making it suitable for high-dimensional VARs with more predictors that observations. We use a Monte Carlo study to demonstrate the accuracy and computational gains of our approach. We further illustrate the forecasting performance of our new approach by applying it to a quarterly macroeconomic dataset, and find that it forecasts better than both factor models and other existing BVAR methods.

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  • Korobilis, D & Pettenuzzo, D, 2016. "Adaptive Minnesota Prior for High-Dimensional Vector Autoregressions," Essex Finance Centre Working Papers 18626, University of Essex, Essex Business School.
  • Handle: RePEc:esy:uefcwp:18626

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    Cited by:

    1. Martin Feldkircher & Luis Gruber & Florian Huber & Gregor Kastner, 2017. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?," Papers 1711.00564,, revised Mar 2024.
    2. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
    3. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1658-1668.

    More about this item


    Bayesian VARs; Minnesota prior; Large datasets; Macroeconomic forecasting;
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