activity
20182023
most citedA Bayesian approach for estimation of weight matrices in spatial autoregressive models

26 citations · 49 across the 5 of their papers we have counts for

collaborators

6 papers

stat.AP2023★ 3 cited

A Bayesian Markov-switching SAR model for time-varying cross-price spillovers

Christian Glocker, Matteo Iacopini, Tamás Krisztin +1

The spatial autoregressive (SAR) model is extended by introducing a Markov switching dynamics for the weight matrix and spatial autoregressive parameter. The framework enables the…

econ.EM2021★ 26 cited

A Bayesian approach for estimation of weight matrices in spatial autoregressive models

Tamás Krisztin, Philipp Piribauer

We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. Datasets in regional economic literature are typically characterized b…

econ.EM2020

Modeling European regional FDI flows using a Bayesian spatial Poisson interaction model

Tamás Krisztin, Philipp Piribauer

This paper presents an empirical study of spatial origin and destination effects of European regional FDI dyads. Recent regional studies primarily focus on locational determinants,…

econ.EM2020

A spatial multinomial logit model for analysing urban expansion

Tamás Krisztin, Philipp Piribauer, Michael Wögerer

The paper proposes a Bayesian multinomial logit model to analyse spatial patterns of urban expansion. The specification assumes that the log-odds of each class follow a spatial aut…

econ.EM2020

A multi-country dynamic factor model with stochastic volatility for euro area business cycle analysis

Florian Huber, Michael Pfarrhofer, Philipp Piribauer

This paper develops a dynamic factor model that uses euro area (EA) country-specific information on output and inflation to estimate an area-wide measure of the output gap. Our mod…

econ.EM2018★ 20 cited

Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models

Michael Pfarrhofer, Philipp Piribauer

This article introduces two absolutely continuous global-local shrinkage priors to enable stochastic variable selection in the context of high-dimensional matrix exponential spatia…