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Adaptive Minnesota Prior for High-Dimensional Vector Autoregressions

Korobilis, D and Pettenuzzo, D (2016) Adaptive Minnesota Prior for High-Dimensional Vector Autoregressions. Working Paper. Essex Finance Centre Working Papers, Colchester.

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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.

Item Type: Monograph (Working Paper)
Uncontrolled Keywords: Bayesian VARs, Minnesota prior, Large datasets, Macroeconomic forecasting
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Jim Jamieson
Date Deposited: 19 Dec 2016 10:36
Last Modified: 07 Aug 2019 21:15

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