299 citations · 844 across the 14 of their papers we have counts for
14 papers
Bayesian modeling and clustering for spatio-temporal areal data: An application to Italian unemployment
Alexander Mozdzen, Andrea Cremaschi, Annalisa Cadonna +2
Spatio-temporal areal data can be seen as a collection of time series which are spatially correlated according to a specific neighboring structure. Incorporating the temporal and s…
Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!
Luis Gruber, Gregor Kastner
Vector autogressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesi…
Bayesian Machine Learning meets Formal Methods: An application to spatio-temporal data
Laura Vana, Ennio Visconti, Laura Nenzi +2
We propose an interdisciplinary framework that combines Bayesian predictive inference, a well-established tool in Machine Learning, with Formal Methods rooted in the computer scien…
On the joint volatility dynamics in dairy markets
Anthony N. Rezitis, Gregor Kastner
The present study investigates the price (co)volatility of four dairy commodities -- skim milk powder, whole milk powder, butter and cheddar cheese -- in three major dairy markets.…
Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
Gregor Kastner
The R package stochvol provides a fully Bayesian implementation of heteroskedasticity modeling within the framework of stochastic volatility. It utilizes Markov chain Monte Carlo (…
Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol
Darjus Hosszejni, Gregor Kastner
Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the larg…
Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage
Darjus Hosszejni, Gregor Kastner
The sampling efficiency of MCMC methods in Bayesian inference for stochastic volatility (SV) models is known to highly depend on the actual parameter values, and the effectiveness…
Introducing shrinkage in heavy-tailed state space models to predict equity excess returns
Florian Huber, Gregor Kastner, Michael Pfarrhofer
We forecast S&P 500 excess returns using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparamete…
Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?
Martin Feldkircher, Luis Gruber, Florian Huber +1
We assess the relationship between model size and complexity in the time-varying parameter VAR framework via thorough predictive exercises for the Euro Area, the United Kingdom and…
Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models
Gregor Kastner, Sylvia Frühwirth-Schnatter
Bayesian inference for stochastic volatility models using MCMC methods highly depends on actual parameter values in terms of sampling efficiency. While draws from the posterior uti…
Sparse Bayesian vector autoregressions in huge dimensions
Gregor Kastner, Florian Huber
We develop a Bayesian vector autoregressive (VAR) model with multivariate stochastic volatility that is capable of handling vast dimensional information sets. Three features are in…
Sparse Bayesian time-varying covariance estimation in many dimensions
Gregor Kastner
We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stocha…
Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models
Florian Huber, Gregor Kastner, Martin Feldkircher
This paper proposes a straightforward algorithm to carry out inference in large time-varying parameter vector autoregressions (TVP-VARs) with mixture innovation components for each…
Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models
Gregor Kastner, Sylvia Frühwirth-Schnatter, Hedibert Freitas Lopes
We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two inte…