We propose a novel variational Bayes approach to estimate high-dimensional Vector Autoregressive (VAR) models with hierarchical shrinkage priors. Our approach does not rely on a conventional structural representation of the parameter space for posterior inference. Instead, we elicit hierarchical shrinkage priors directly on the matrix of regression coefficients so that (a) the prior structure maps into posterior inference on the reduced-form transition matrix and (b) posterior estimates are more robust to variables permutation. An extensive simulation study provides evidence that our approach compares favorably against existing linear and nonlinear Markov chain Monte Carlo and variational Bayes methods. We investigate the statistical and ec...
This paper shows that vector auto regression (VAR) with Bayesian shrinkage is an appropriate tool fo...
This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarch...
Variational Inference (VI) has become a popular technique to approximate difficult-to-compute poster...
We propose a novel variational Bayes approach to estimate high-dimensional Vector Autoregressive (VA...
Many recent papers in macroeconomics have used large vector autoregressions (VARs) involving 100 or ...
This paper proposes a simulation-free estimation algorithm for vector autoregressions (VARs) that al...
<p>Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among multipl...
This paper proposes a simulation-free estimation algorithm for vector autoregressions (VARs) that al...
Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis...
Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency es...
We develop a new variational Bayes estimation method for large-dimensional sparse multivariate predi...
Macroeconomists using large datasets often face the choice of working with either a large Vector Aut...
This paper proposes a mean field variational Bayes algorithm for efficient posterior and predictive ...
Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. Th...
We develop a novel, highly scalable estimation method for large Bayesian Vector Autoregressive model...
This paper shows that vector auto regression (VAR) with Bayesian shrinkage is an appropriate tool fo...
This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarch...
Variational Inference (VI) has become a popular technique to approximate difficult-to-compute poster...
We propose a novel variational Bayes approach to estimate high-dimensional Vector Autoregressive (VA...
Many recent papers in macroeconomics have used large vector autoregressions (VARs) involving 100 or ...
This paper proposes a simulation-free estimation algorithm for vector autoregressions (VARs) that al...
<p>Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among multipl...
This paper proposes a simulation-free estimation algorithm for vector autoregressions (VARs) that al...
Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis...
Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency es...
We develop a new variational Bayes estimation method for large-dimensional sparse multivariate predi...
Macroeconomists using large datasets often face the choice of working with either a large Vector Aut...
This paper proposes a mean field variational Bayes algorithm for efficient posterior and predictive ...
Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. Th...
We develop a novel, highly scalable estimation method for large Bayesian Vector Autoregressive model...
This paper shows that vector auto regression (VAR) with Bayesian shrinkage is an appropriate tool fo...
This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarch...
Variational Inference (VI) has become a popular technique to approximate difficult-to-compute poster...