Working papers

Freshly baked research – straight from the oven!

  1. Vana, L., Visconti, E., Nenzi, L., Cadonna, A., & Kastner, G. (2021). Posterior predictive model checking using formal methods in a spatio-temporal model.
  2. Huber, F., Koop, G., & Pfarrhofer, M. (2020). Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations.
  3. Pfarrhofer, M. (2020). Forecasts with Bayesian vector autoregressions under real time conditions.
  4. Vana, L., & Hornik, K. (2020). Dynamic modeling of corporate credit ratings and defaults.
  5. Hauzenberger, N., Huber, F., Koop, G., & Onorante, L. (2019). Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models.
  6. Pfarrhofer, M. (2019). Measuring international uncertainty using global vector autoregressions with drifting parameters.
  7. Pfarrhofer, M., & Stelzer, A. (2019). The international effects of central bank information shocks.

Publications

The following contains a list of all academic papers published in international, peer-reviewed journals originating from research within the ZK35 project.

  1. Hosszejni, D., & Kastner, G. (forthcoming). Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol. Journal of Statistical Software. https://arxiv.org/abs/1906.12123
  2. Hauzenberger, N. (2021). Flexible Mixture Priors for Large Time-varying Parameter Models. Econometrics and Statistics, 20, 87–108.
  3. Hauzenberger, N., Huber, F., Koop, G., & Onorante, L. (2021). Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models. Journal of Business & Economic Statistics, forthcoming.
  4. Hauzenberger, N., Pfarrhofer, M., & Stelzer, A. (2021). On the Effectiveness of the European Central Bank’s Conventional and Unconventional Policies under Uncertainty. Journal of Economic Behavior & Organization, forthcoming.
  5. Rezitis, A. N., & Kastner, G. (2021). On the joint volatility dynamics in international dairy commodity markets. Australian Journal of Agricultural and Resource Economics, 65(3), 704–728. https://doi.org/10.1111/1467-8489.12433
  6. Bartocci, E., Bortolussi, L., Loreti, M., Nenzi, L., & Silvetti, S. (2020). MoonLight: A Lightweight Tool for Monitoring Spatio-Temporal Properties. In J. Deshmukh & D. Nickovic (Eds.), Runtime Verification - 20th International Conference, RV 2020, Los Angeles, CA, USA, October 6-9, 2020, Proceedings (Vol. 12399, pp. 417–428). Springer. https://doi.org/10.1007/978-3-030-60508-7_23
  7. Feldkircher, M., Gruber, T., & Huber, F. (2020). International effects of a compression of euro area yield curves. Journal of Banking & Finance, 113(4), 26–43. https://doi.org/10.1016/j.jbankfin.2019.03.017
  8. Fischer, M. M., Huber, F., & Pfarrhofer, M. (2020). The regional transmission of uncertainty shocks on income inequality in the United States. Journal of Economic Behavior & Organization. https://doi.org/10.1016/j.jebo.2019.03.004
  9. Hauzenberger, N., Huber, F., & Onorante, L. (2020). Combining Shrinkage and Sparsity in Conjugate Vector Autoregressive Models. Journal of Applied Econometrics, (forthcoming). https://arxiv.org/abs/2002.08760
  10. Hauzenberger, N., Huber, F., Pfarrhofer, M., & Zörner, T. O. (2020). Stochastic model specification in Markov switching vector error correction models. Studies in Nonlinear Dynamics and Econometrics. https://doi.org/10.1515/snde-2018-0069
  11. Hauzenberger, N., & Huber, F. (2020). Model instability in predictive exchange rate regressions. Journal of Forecasting, 39(2), 168–186. https://doi.org/10.1002/for.2620
  12. Hauzenberger, N., & Pfarrhofer, M. (2020). Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy. Scandinavian Journal of Economics, (forthcoming). https://arxiv.org/abs/1911.06206
  13. Hirk, R., Kastner, G., & Vana, L. (2020). Investigating the dark figure of COVID-19 cases in Austria: Borrowing from the deCODE genetics study in Iceland. Austrian Journal of Statistics, 49(5), 1–17. https://doi.org/10.17713/ajs.v49i4.1142
  14. Huber, F., Koop, G., Pfarrhofer, M., Onorante, L., & Schreiner, J. (2020). Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs. Journal of Econometrics, (forthcoming). https://arxiv.org/pdf/2008.12706.pdf
  15. Huber, F., & Pfarrhofer, M. (2020). Dynamic shrinkage in time-varying parameter stochastic volatility in mean models. Journal of Applied Econometrics, (forthcoming). https://arxiv.org/abs/2005.06851
  16. Huber, F., Koop, G., & Onorante, L. (2020). Inducing Sparsity and Shrinkage in Time-Varying Parameter Models. Journal of Business & Economic Statistics, 1–15. https://doi.org/10.1080/07350015.2020.1713796
  17. Huber, F., Pfarrhofer, M., & Piribauer, P. (2020). A multi-country dynamic factor model with stochastic volatility for euro area business cycle analysis. Journal of Forecasting. https://doi.org/10.1002/for.2667
  18. Kastner, G., & Huber, F. (2020). Sparse Bayesian Vector Autoregressions in Huge Dimensions. Journal of Forecasting. https://doi.org/10.1002/for.2680
  19. Krisztin, T., & Piribauer, P. (2020). A Bayesian spatial autoregressive logit model with an empirical application to European regional FDI flows. Empirical Economics. https://doi.org/10.1007/s00181-020-01856-w
  20. Krisztin, T., Piribauer, P., & Wögerer, M. (2020). The spatial econometrics of the coronavirus pandemic. Letters in Spatial and Resource Sciences. https://doi.org/10.1007/s12076-020-00254-1
  21. Nenzi, L., Bartocci, E., Bortolussi, L., Loreti, M., & Visconti, E. (2020). Monitoring Spatio-Temporal Properties (Invited Tutorial). In J. Deshmukh & D. Nickovic (Eds.), Runtime Verification - 20th International Conference, RV 2020, Los Angeles, CA, USA, October 6-9, 2020, Proceedings (Vol. 12399, pp. 21–46). Springer. https://doi.org/10.1007/978-3-030-60508-7_2
  22. Nikravech, M., Kwan, V., Dobernig, K., Wilhelm-Rechmann, A., & Langen, N. (2020). Limiting food waste via grassroots initiatives as a potential for climate change mitigation: a systematic review. Environmental Research Letters. https://doi.org/10.1088/1748-9326/aba2fe
  23. Visconti, E., Tsigkanos, C., Hu, Z., & Ghezzi, C. (2019). Model-driven design of city spaces via bidirectional transformations. 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS), 45–55. https://doi.org/10.1007/s10270-020-00851-0
  24. Vissat, L. L., Loreti, M., Nenzi, L., Hillston, J., & Marion, G. (2019). Analysis of Spatio-Temporal Properties of Stochastic Systems Using TSTL. ACM Trans. Model. Comput. Simul., 29(4). https://doi.org/10.1145/3326168

Book chapters

  1. Feldkircher, M., Huber, F., & Pfarrhofer, M. (2020). Factor Augmented Vector Autoregressions, Panel VARs, and Global VARs. In Macroeconomic Forecasting in the Era of Big Data (pp. 65–93). Springer.