Tel.: +43 (0) 662 8044 3770
Bayesian methods, big data, text mining, statistical and machine learning, economic growth
Paul Hofmarcher is an Assistant Professor of economics at the Department of Economics at the University of Salzburg. He studied economics and mathematics at the University of Vienna and received his doctoral degree from WU Vienna. His research interests include text mining, economic growth, Bayesian econometrics, and model uncertainty.
Assmus, Josephine, Michael Blauberger, Anita Heindlmaier, Paul Hofmarcher, and Birgit Mitter. “The differentiated politicization of free movement of people in the EU. A topic modeling analysis of press coverage in Austria, Germany, Poland and the UK.” Journal of European Public Policy, 2021, forthcoming. doi
Grün, Bettina and Paul Hofmarcher. “Identifying groups of determinants in Bayesian model averaging using Dirichlet process clustering.” Scandinavian Journal of Statistics, 2021, 48, 3, 1018-1045. doi
Cuaresma, Jesus Crespo, Bettina Grün, Paul Hofmarcher, Stefan Humer, and Mathias Moser. “Unveiling covariate inclusion structures in economic growth regressions using latent class analysis.” European Economic Review, 2016, 81, 189-202. doi
Hofmarcher, Paul and Mathias Moser. “Model priors revisited: Interaction terms in BMA growth applications.” Journal of Applied Econometrics, 2013, 29, 2, 344-347. doi
Hatzinger, Reinhold, Paul Hofmarcher, Kurt Hornik, and Thomas Rusch. “Model trees with topic model pre-processing: An approach for data journalism illustrated with the Wikileaks Afghanistan war logs.” Annals of Applied Statistics, 2013, 7, 2, 613-639. doi