Publications

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

Peer-reviewed

2024

  1. Clark, T. E., Huber, F., Koop, G., & Marcellino, M. (2024). Forecasting US inflation using Bayesian nonparametric models. The Annals of Applied Statistics, 18(2), 1421–1444. https://doi.org/10.1214/23-AOAS1841
  2. Clark, T. E., Huber, F., Koop, G., Marcellino, M., & Pfarrhofer, M. (2024). Investigating growth-at-risk using a multicountry nonparametric quantile factor model. Journal of Business & Economic Statistics, 1–16. https://doi.org/10.1080/07350015.2024.2310020
  3. Feldkircher, M., Gruber, L., Huber, F., & Kastner, G. (2024). Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions? Journal of Forecasting. https://doi.org/10.1002/for.3121
  4. Griller, S., Huber, F., & Pfarrhofer, M. (2024). Financial markets and legal challenges to unconventional monetary policy. European Economic Review, 163, 104680. https://doi.org/10.1016/j.euroecorev.2024.104680
  5. Hauzenberger, N., Huber, F., & Koop, G. (2024). Dynamic shrinkage priors for large time-varying parameter regressions using scalable Markov chain Monte Carlo methods. Studies in Nonlinear Dynamics & Econometrics, 28(2), 201–225. https://doi.org/10.1515/snde-2022-0077
  6. Hauzenberger, N., Huber, F., Marcellino, M., & Petz, N. (2024). Gaussian process vector autoregressions and macroeconomic uncertainty. Journal of Business & Economic Statistics, 1–17. https://doi.org/10.1080/07350015.2024.2322089
  7. Huber, F., Onorante, L., & Pfarrhofer, M. (2024). Forecasting euro area inflation using a huge panel of survey expectations. International Journal of Forecasting, 40(3), 1042–1054. https://doi.org/10.1016/j.ijforecast.2023.09.003
  8. Pfarrhofer, M. (2024). Forecasts with Bayesian vector autoregressions under real time conditions. Journal of Forecasting, 43(3), 771–801. https://doi.org/10.1002/for.3055
  9. Pigozzi, F., Nenzi, L., & Medvet, E. (2024). BUSTLE: a Versatile Tool for the Evolutionary Learning of STL Specifications from Data. Evolutionary Computation, 1–24. https://doi.org/10.1162/evco_a_00347
  10. Prüser, J., & Huber, F. (2024). Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions. Journal of Applied Econometrics, 39(2), 269–291. https://doi.org/10.1002/jae.3018
  11. Schwendinger, B., Schwendinger, F., & Vana, L. (2024). Holistic Generalized Linear Models. Journal of Statistical Software, 108(7), 1–49. https://doi.org/10.18637/jss.v108.i07
  12. Schwendinger, F., Vana, L., & Hornik, K. (2024). Readability prediction: How many features are necessary? Annals of Applied Statistics, 18(2), 1010–1034. https://doi.org/10.1214/23-AOAS1820

2023

  1. Clark, T. E., Huber, F., Koop, G., Marcellino, M., & Pfarrhofer, M. (2023). Tail forecasting with multivariate Bayesian additive regression trees. International Economic Review, 64(3), 979–1022. https://doi.org/10.1111/iere.12619
  2. Dobe, O., Schupp, S., Bartocci, E., Bonakdarpour, B., Legay, A., Pajic, M., & Wang, Y. (2023). Lightweight Verification of Hyperproperties. International Symposium on Automated Technology for Verification and Analysis, 3–25. https://doi.org/10.1007/978-3-031-45332-8_1
  3. Fischer, M. M., Hauzenberger, N., Huber, F., & Pfarrhofer, M. (2023). General Bayesian time-varying parameter vector autoregressions for modeling government bond yields. Journal of Applied Econometrics, 38(1), 69–87. https://doi.org/https://doi.org/10.1002/jae.2936
  4. Hauzenberger, N., Huber, F., & Klieber, K. (2023). Real-time inflation forecasting using non-linear dimension reduction techniques. International Journal of Forecasting, 39(2), 901–921. https://doi.org/10.1016/j.ijforecast.2022.03.002
  5. Huber, F., Kastner, G., & Pfarrhofer, M. (2023). Introducing shrinkage in heavy-tailed state space models to predict equity excess returns. Empirical Economics, 1–19. https://doi.org/10.1007/s00181-023-02437-3
  6. Huber, F., & Koop, G. (2023). Subspace shrinkage in conjugate Bayesian vector autoregressions. Journal of Applied Econometrics, 38(4), 556–576. https://doi.org/10.1002/jae.2966
  7. Huber, F., Krisztin, T., & Pfarrhofer, M. (2023). A Bayesian panel vector autoregression to analyze the impact of climate shocks on high-income economies. The Annals of Applied Statistics, 17(2), 1543–1573. https://doi.org/10.1214/22-AOAS1681
  8. Krisztin, T., & Piribauer, P. (2023). A joint spatial econometric model for regional FDI and output growth. Papers in Regional Science, 102(1), 87–107. https://doi.org/10.1111/pirs.12714
  9. Nenzi, L., Bartocci, E., Bortolussi, L., Silvetti, S., & Loreti, M. (2023). MoonLight: a lightweight tool for monitoring spatio-temporal properties. International Journal on Software Tools for Technology Transfer, 25(4), 503–517. https://doi.org/10.1007/s10009-023-00710-5
  10. Pfarrhofer, M. (2023). Measuring international uncertainty using global vector autoregressions with drifting parameters. Macroeconomic Dynamics, 27(3), 770–793. https://doi.org/10.1017/S1365100521000663
  11. Piribauer, P., Glocker, C., & Krisztin, T. (2023). Beyond distance: The spatial relationships of European regional economic growth. Journal of Economic Dynamics and Control, 155, 104735. https://doi.org/10.1016/j.jedc.2023.104735

2022

  1. Feldkircher, M., Huber, F., Koop, G., & Pfarrhofer, M. (2022). Approximate Bayesian Inference and Forecasting in Huge-Dimensional Multicountry VarsityRs. International Economic Review, 63(4), 1625–1658. https://doi.org/https://doi.org/10.1111/iere.12577
  2. Hauzenberger, N., Huber, F., Koop, G., & Onorante, L. (2022). Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models. Journal of Business & Economic Statistics, Volume 40(4), 1904–1918. https://doi.org/10.1080/07350015.2021.1990772
  3. Hauzenberger, N., Huber, F., Koop, G., & Onorante, L. (2022). Fast and flexible Bayesian inference in time-varying parameter regression models. Journal of Business & Economic Statistics, 40(4), 1904–1918. https://doi.org/10.1080/07350015.2021.1990772
  4. Krisztin, T., & Piribauer, P. (2022). A Bayesian approach for the estimation of weight matrices in spatial autoregressive models. Spatial Economic Analysis, 1–20. https://doi.org/10.1080/17421772.2022.2095426
  5. Mozdzen, A., Cremaschi, A., Cadonna, A., Guglielmi, A., & Kastner, G. (2022). Bayesian modeling and clustering for spatio-temporal areal data: an application to Italian unemployment. Spatial Statistics, forthcoming.
  6. Visconti, E., Tsigkanos, C., & Nenzi, L. (2022). WebMonitor: Verification of Web User Interfaces. Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 1–4. https://doi.org/10.1145/3551349.3559538

2021

  1. Glocker, C., & Piribauer, P. (2021). Digitalization, retail trade and monetary policy. Journal of International Money and Finance, 112, 102340. https://doi.org/10.1016/j.jimonfin.2020.102340
  2. Glocker, C., & Piribauer, P. (2021). The determinants of output losses during the Covid-19 pandemic. Economics Letters, 204, 109923. https://doi.org/10.1016/j.econlet.2021.109923
  3. Hauzenberger, N. (2021). Flexible Mixture Priors for Large Time-varying Parameter Models. Econometrics and Statistics, 20, 87–108. https://doi.org/10.1016/j.ecosta.2021.06.001
  4. Hauzenberger, N., Huber, F., & Onorante, L. (2021). Combining shrinkage and sparsity in conjugate vector autoregressive models. Journal of Applied Econometrics, 36(3), 304–327. https://doi.org/10.1002/jae.2807
  5. 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, 191, 822–845. https://doi.org/10.1016/j.jebo.2021.09.041
  6. Hosszejni, D., & Kastner, G. (2021). Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol. Journal of Statistical Software, 100, 1–34. https://doi.org/10.18637/jss.v100.i12
  7. Huber, F., & Pfarrhofer, M. (2021). Dynamic shrinkage in time-varying parameter stochastic volatility in mean models. Journal of Applied Econometrics, 36(2), 262–270. https://doi.org/10.1002/jae.2804
  8. Krisztin, T., & Piribauer, P. (2021). A Bayesian spatial autoregressive logit model with an empirical application to European regional FDI flows. Empirical Economics, 61(1), 231–257. https://doi.org/10.1007/s00181-020-01856-w
  9. Krisztin, T., & Piribauer, P. (2021). Modelling European regional FDI flows using a Bayesian spatial Poisson interaction model. The Annals of Regional Science, 67(3), 593–616. https://doi.org/10.1007/s00181-020-01856-w
  10. Krisztin, T., Piribauer, P., & Wögerer, M. (2021). A spatial multinomial logit model for analysing urban expansion. Spatial Economic Analysis, 1–22. https://doi.org/10.1080/17421772.2021.1933579
  11. 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
  12. Vana, L., & Hornik, K. (2021). Dynamic Modeling of Corporate Credit Ratings and Defaults. Statistical Modelling, 23(4), 357–375. https://doi.org/10.1177/1471082X211057610

2020

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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, 39(6), 911–926. https://doi.org/10.1002/for.2667
  11. Kastner, G., & Huber, F. (2020). Sparse Bayesian Vector Autoregressions in Huge Dimensions. Journal of Forecasting. https://doi.org/10.1002/for.2680
  12. Krisztin, T., Piribauer, P., & Wögerer, M. (2020). The spatial econometrics of the coronavirus pandemic. Letters in Spatial and Resource Sciences, 13(3), 209–218. https://doi.org/10.1007/s12076-020-00254-1
  13. 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
  14. 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

2019

  1. 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
  2. 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

Working papers

  1. Parzer, R., Filzmoser, P., & Vana-Gür, L. (2024). Data-Driven Random Projection and Screening for High-Dimensional Generalized Linear Models (No.2410.00971; Number 2410.00971). arXiv.org E-Print Archive. https://doi.org/10.48550/arXiv.2410.00971
  2. Parzer, R., Filzmoser, P., & Vana-Gür, L. (2024). Sparse Data-Driven Random Projection in Regression for High-Dimensional Data (No.2312.00130; Number 2312.00130). arXiv.org E-Print Archive. https://doi.org/10.48550/arXiv.2312.00130
  3. Parzer, R., Vana-Gür, L., & Filzmoser, P. (2024). spar: Sparse Projected Averaged Regression in R (No.2411.17808; Number 2411.17808). https://doi.org/10.48550/arXiv.2411.17808
  4. Schwendinger, B., Schwendinger, F., & Vana-Gür, L. (2024). Automated Model Selection for Generalized Linear Models (No.2404.16560; Number 2404.16560). https://doi.org/10.48550/arXiv.2404.16560
  5. Vana, L., Visconti, E., Nenzi, L., Cadonna, A., & Kastner, G. (2024). Bayesian Machine Learning meets Formal Methods: An application to spatio-temporal data. https://arxiv.org/abs/2110.01360
  6. Huber, F., Koop, G., & Pfarrhofer, M. (2020). Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations.
  7. Pfarrhofer, M., & Stelzer, A. (2019). The international effects of central bank information shocks.

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.