Adaptive Minnesota Prior for High-Dimensional Vector Autoregressions

Dimitris Korobilis and Davide Pettenuzzo (dpettenu@brandeis.edu)

Essex Finance Centre Working Papers from University of Essex, Essex Business School

Abstract: 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.

Keywords: Bayesian VARs; Minnesota prior; Large datasets; Macroeconomic forecasting (search for similar items in EconPapers)
Date: 2016-12-13
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:esy:uefcwp:18626

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