Working papers

Freshly baked research – straight from the oven!

  1. 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. https://doi.org/10.13140/RG.2.2.18427.05928
  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. Hauzenberger, N., Huber, F., Koop, G., & Onorante, L. (2019). Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models.
  5. Hauzenberger, N., & Pfarrhofer, M. (2019). Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy.
  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. 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
  2. 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
  3. 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
  4. 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
  5. 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(4), 1–17. https://doi.org/10.13140/RG.2.2.18427.05928
  6. 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
  7. 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
  8. Kastner, G., & Huber, F. (2020). Sparse Bayesian Vector Autoregressions in Huge Dimensions. Journal of Forecasting. https://doi.org/10.1002/for.2680
  9. 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
  10. 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.