Publikationen

• Beck, N., Dovern, J., & Vogl, S. (2025). Mind the Naive Forecast! A Rigorous Evaluation of Forecasting Models for Time Series with Low Predictability. Applied Intelligence, 55. https://doi.org/10.1007/s10489-025-06268-w
• Böck, M., & Glocker, C. (2025). The Role of Labor Market Institutions in Shaping Euro Area Monetary Policy Transmission. Oxford Bulletin of Economics and Statistics. https://doi.org/10.1111/obes.70029
• Böck, M., & Mori, L. (2025). Has globalization changed the international transmission of U.S. monetary policy? Journal of International Economics, 157, 1-17. https://doi.org/10.1016/j.jinteco.2025.104139
• Böck, M., & Pfarrhofer, M. (2025). Belief Shocks and Implications of Expectations About Growth-at-Risk. Journal of Applied Econometrics, 40(3), 341-348. https://doi.org/10.1002/jae.3117
• Böck, M., & Zörner, T.O. (2025). Natural Gas Prices, Inflation Expectations, and the Pass-Through to Euro Area Inflation. Energy Economics, 141(108061), 1-14. https://doi.org/10.1016/j.eneco.2024.108061
• Perico Ortiz, D., Schnaubelt, M., & Seifert, O. (2025). A Narrative Perspective on Investor Uncertainty: Evidence from Earnings Conference Calls. Journal of Behavioral Finance. https://doi.org/10.1080/15427560.2025.2528025

• Bjerkander, L., & Glas, A. (2024). Talking in a language that everyone can understand? Clarity of speeches by the ECB Executive Board. Journal of International Money and Finance. https://doi.org/10.1016/j.jimonfin.2024.103200
• Böck, M., Feldkircher, M., & Raunig, B. (2024). A view from outside: sovereign CDS volatility as an indicator of economic uncertainty. Macroeconomic Dynamics, 28(7), 1423-1450. https://doi.org/10.1017/S1365100523000524
• Böck, M., Marcellino, M., Pfarrhofer, M., & Tornese, T. (2024). Predicting Tail-Risks for the Italian Economy. Journal of Business Cycle Research, 20, 339–366. https://doi.org/10.1007/s41549-025-00106-1
• Böck, M., & Zörner, T.O. (2024). The Impact of Credit Market Sentiment Shocks. Journal of Money, Credit and Banking, 56(7), 1645-1673. https://doi.org/10.1111/jmcb.13109
• Dovern, J. (2024). Eliciting Expectation Uncertainty from Private Households. International Journal of Forecasting, 40(1), 113-123. https://doi.org/10.1016/j.ijforecast.2023.01.002
• Dovern, J., Glas, A., & Kenny, G. (2024). Testing for Differences in Survey-Based Density Expectations: a Compositional Data Approach. Journal of Applied Econometrics, 39(6), 1104-1122. https://doi.org/10.1002/jae.3080
• Möstel, L., Fischer, M., & Pfeuffer, M. (2024). Composite Tukey-type distributions with application to operational risk management. Journal of Operational Risk, 19(1), 1-27. https://doi.org/10.21314/JOP.2023.010

• Born, B., Dovern, J., & Enders, Z. (2023). Expectation dispersion, uncertainty, and the reaction to news. European Economic Review, 154, 104440. https://doi.org/10.1016/j.euroecorev.2023.104440
• Dovern, J., Frank, J., Glas, A., Müller, L.S., & Perico Ortiz, H. (2023). Estimating pass-through rates for the 2022 tax reduction on fuel prices in Germany. Energy Economics, 126, 106948. https://doi.org/10.1016/j.eneco.2023.106948
• Dovern, J., Müller, L., & Wohlrabe, K. (2023). Local Information and Firm Expectations about Aggregates. Journal of Monetary Economics, 138, 1-13. https://doi.org/10.1016/j.jmoneco.2023.03.005
• Glas, A., & Heinisch, K. (2023). Conditional macroeconomic survey forecasts: Revisions and errors. Journal of International Money and Finance, 138. https://doi.org/10.1016/j.jimonfin.2023.102927
• Mühlroth, C., Kölbl, L., & Grottke, M. (2023). Innovation signals: leveraging machine learning to separate noise from news. Scientometrics, 128(5), 2649-2676. https://doi.org/10.1007/s11192-023-04672-y

• Buchheim, L., Dovern, J., Krolage, C., & Link, S. (2022). Sentiment and Firm Behavior During the COVID-19 Pandemic. Journal of Economic Behavior & Organization, 195, 186 - 198. https://doi.org/10.1016/j.jebo.2022.01.011
• Böck, M., Feldkircher, M., & Huber, F. (2022). BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R. Journal of Statistical Software, 104 (9), 1-28. https://doi.org/10.18637/jss.v104.i09
• Glas, A., Conrad, C., & Enders, Z. (2022). The role of information and experience for households' inflation expectations. European Economic Review, 143, 104015. https://doi.org/10.1016/j.euroecorev.2021.104015
• Glas, A., & Hartmann, M. (2022). Uncertainty Measures from Partially Rounded Probabilistic Forecast Surveys. Quantitative Economics, 13(3), 979-1022. https://doi.org/10.3982/QE1703
• Klein, I., & Doll, M. (2022). Sample size analysis for two-sample linear rank tests. Communications in Statistics-Theory and Methods. https://dx.doi.org/10.1080/03610926.2022.2068029
• Perico Ortiz, H. (2022). Economic Policy Statements, Social Media, and Stock Market Uncertainty: an analysis of Donald Trump’s tweets. Journal of Economics and Finance, 47. https://doi.org/10.1007/s12197-022-09608-5
• Stübinger, J., & Walter, D. (2022). Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships. Sensors, 22(18). https://doi.org/10.3390/s22186884

• Böck, M., & Feldkircher, M. (2021). The Impact of Monetary Policy on Yield Curve Expectations. Journal of Economic Behavior & Organization, 119, 887-901. https://doi.org/10.1016/j.jebo.2021.09.044
• Böck, M., Feldkircher, M., & Siklos, P.L. (2021). International Effects of Euro Area Forward Guidance. Oxford Bulletin of Economics and Statistics, 83(5), 1066 - 1110. https://doi.org/10.1111/obes.12438
• Schnaubelt, M. (2021). Deep reinforcement learning for the optimal placement of cryptocurrency limit orders. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2021.04.050
• Waldow, F., Schnaubelt, M., Krauss, C., & Fischer, T. (2021). Machine Learning in Futures Markets. Journal of Risk and Financial Management, 14(3). https://doi.org/10.3390/jrfm14030119

• Avritzer, A., Grottke, M., & Menasché, D.S. (2020). Using Software Aging Monitoring and Rejuvenation for the Assessment of High-Availability Systems. In Handbook of Software Aging and Rejuvenation. (pp. 197-228).
• Böck, M., Zörner, T.O., & Zens, G. (2020). The heterogeneous impact of monetary policy on the US labor market. Journal of Economic Dynamics & Control, 119.
• Dovern, J., & Kenny, G. (2020). Anchoring Inflation Expectations in Unconventional Times: Micro Evidence for the Euro Area. International Journal of Central Banking, 64, 309 - 347.
• Dovern, J., & Manner, H. (2020). Order-Invariant Tests for Proper Calibration of Multivariate Density Forecasts. Journal of Applied Econometrics, 35, 440-456. https://doi.org/10.1002/jae.2755
• Dovern, J., & Zuber, C. (2020). How Economic Crises Damage Potential Output – Evidence from the Great Recession. Journal of Macroeconomics, 65. https://doi.org/10.1016/j.jmacro.2020.103239
• Dovern, J., & Zuber, C. (2020). Recessions and Potential Output: Disentangling Measurement Errors, Supply Shocks, and Hysteresis Effects. Scandinavian Journal of Economics, 122(4), 1431-1466. https://doi.org/10.1111/sjoe.12385
• Glas, A. (2020). Five Dimensions of the Uncertainty-Disagreement Linkage. International Journal of Forecasting, 36(2), 607-627. https://doi.org/10.1016/j.ijforecast.2019.07.010
• Klein, I., & Doll, M. (2020). (Generalized) Maximum Cumulative Direct, Residual, and Paired Φ Entropy Approach. Entropy. https://dx.doi.org/10.3390
• Klein, I., & Doll, M. (2020). Tests on asymmetry for ordered categorical variables. Journal of Applied Statistics. https://dx.doi.org/10.1080/02664763.2020.1757045
• Mühlroth, C., & Grottke, M. (2020). Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2020.2989214
• Pfeuffer, M., Nagl, M., Fischer, M., & Rösch, D. (2020). Parameter estimation, bias correction and uncertainty quantification in the Vasicek credit portfolio model. The Journal of Risk, 22(4), 1-30. https://doi.org/10.21314/JOR.2020.429
• Rufino, V.Q., De Aguiar, L.P., Menasche, D.S., Lima, C., Cunha, I., Altman, E.,... Grottke, M. (2020). Beyond Herd Immunity Against Strategic Attackers. IEEE Access, 8, 66365-66399. https://doi.org/10.1109/ACCESS.2020.2983652
• Schnaubelt, M., Fischer, T., & Krauß, C. (2020). Separating the signal from the noise - financial machine learning for Twitter. Journal of Economic Dynamics & Control, 114, 103895. https://doi.org/10.1016/j.jedc.2020.103895
• Schneider, L., & Stübinger, J. (2020). Dispersion trading based on the explanatory power of S&P 500 stock returns. Mathematics, 8(9). https://doi.org/10.3390/math8091627
• Stübinger, J., Mangold, B., & Knoll, J. (2020). Machine learning in football betting: Prediction of match results based on player characteristics. Applied Sciences, 10(1). https://doi.org/10.3390/app10010046
• Stübinger, J., & Schneider, L. (2020). Epidemiology of Coronavirus COVID-19: Forecasting the Future Incidence in Different Countries. Healthcare, 8(2). https://doi.org/10.3390/healthcare8020099
• Stübinger, J., & Schneider, L. (2020). Understanding smart city—a data-driven literature review. Sustainability, 12(20), 1-23. https://doi.org/10.3390/su12208460
• dos Reis, G., Pfeuffer, M., & Smith, G. (2020). Capturing model risk and rating momentum in the estimation of probabilities of default and credit rating migrations. Quantitative Finance. https://doi.org/10.1080/14697688.2020.1726439

• Endres, S., & Stübinger, J. (2019). A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns. Quantitative Finance. https://doi.org/10.1080/14697688.2019.1585562
• Endres, S., & Stübinger, J. (2019). Optimal trading strategies for Levy-driven Ornstein-Uhlenbeck processes. Applied economics, 51(29), 3153-3169. https://doi.org/10.1080/00036846.2019.1566688
• Fischer, T., Krauss, C., & Deinert, A. (2019). Statistical Arbitrage in Cryptocurrency Markets. Journal of Risk and Financial Management, 12(1). https://doi.org/10.3390/jrfm12010031
• Knoll, J., Stübinger, J., & Grottke, M. (2019). Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500. Quantitative Finance, 19(4), 571-585. https://doi.org/10.1080/14697688.2018.1521002
• Kölbl, L., Mühlroth, C., Wiser, F., Grottke, M., & Durst, C. (2019). Big Data im Innovationsmanagement: Wie Machine Learning die Suche nach Trends und Technologien revolutioniert. HMD : Praxis der Wirtschaftsinformatik, 56(5), 900-913. https://doi.org/10.1365/s40702-019-00528-3
• Möstel, L., Fischer, M., Pfaelzner, F., & Pfeuffer, M. (2019). Parameter estimation of Tukey-type distributions: A comparative analysis. Communications in Statistics-Simulation and Computation. https://doi.org/10.1080/03610918.2019.1571604
• Schnaubelt, M., Rende, J., & Krauss, C. (2019). Testing Stylized Facts of Bitcoin Limit Order Books. Journal of Risk and Financial Management, 12(1). https://doi.org/10.3390/jrfm12010025
• Stübinger, J. (2019). Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500. Quantitative Finance, 19(6), 921-935. https://doi.org/10.1080/14697688.2018.1537503
• Stübinger, J., & Schneider, L. (2019). Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500. Journal of Risk and Financial Management, 12(2). https://doi.org/10.3390/jrfm12020051

• Clegg, M., & Krauß, C. (2018). Pairs trading with partial cointegration. Quantitative Finance, 18(1), 121-138.
• Fischer, T., & Krauß, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669. https://doi.org/10.1016/j.ejor.2017.11.054
• Glas, A., Donaubauer, J., Meyer, B., & Nunnenkamp, P. (2018). Disentangling the impact of infrastructure on trade using a new index of infrastructure. Review of World Economics, 154 (4), 745-784.
• Kißlinger, A.-L., & Stummer, W. (2018). A new toolkit for robust distributional change detection. Applied Stochastic Models in Business and Industry, 34(5), 682-699. https://doi.org/10.1002/asmb.2357
• Klein, I., Fischer, M., & Pleier, T. (2018). Weighted Power Mean Copulas: Theory and Application. Model Assisted Statistics and Applications, 13(3), 253-270. https://doi.org/10.3233/MAS-180436
• Mühlroth, C., & Grottke, M. (2018). A systematic literature review of mining weak signals and trends for corporate foresight. Journal of Business Economics, 88(5), 643-687. https://doi.org/10.1007/s11573-018-0898-4
• Pfeuffer, M., Möstel, L., & Fischer, M. (2018). An Extended Likelihood Framework for Modeling Discretely Observed Credit Rating Transitions. Quantitative Finance. https://doi.org/10.1080/14697688.2018.1465196
• Stübinger, J., & Endres, S. (2018). Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. Quantitative Finance. https://doi.org/10.1080/14697688.2017.1417624
• Stübinger, J., Mangold, B., & Krauss, C. (2018). Statistical arbitrage with vine copulas. Quantitative Finance. https://doi.org/10.1080/14697688.2018.1438642

• Agarwal, N., Grottke, M., Mishra, S., & Brem, A. (2017). A Systematic Literature Review of Constraint-Based Innovations: State of the Art and Future Perspectives. IEEE Transactions on Engineering Management, 64(1), 3-15. https://doi.org/10.1109/TEM.2016.2620562
• Dovern, J., & Hartmann, M. (2017). Forecast performance, disagreement, and heterogeneous signal-to-noise ratios. Empirical Economics, 53(1), 63-77. https://doi.org/10.1007/s00181-016-1137-x
• Dovern, J., & Jannsen, N. (2017). Systematic errors in growth expectations over the business cycle. International Journal of Forecasting, 33(4), 760-769. https://doi.org/10.1016/j.ijforecast.2017.03.003
• Fischer, M., Kraus, D., Pfeuffer, M., & Czado, C. (2017). Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression. Risks, 5(3), 38. https://doi.org/10.3390/risks5030038
• Krauss, C., & Stübinger, J. (2017). Nonlinear dependence modeling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100. Applied economics, 49(52), 5352-5369. https://doi.org/10.1080/00036846.2017.1305097
• Krauss, C., & Stübinger, J. (2017). Non-linear dependence modelling with bivariate copulas: statistical arbitrage pairs trading on the S&P 100. Applied economics, 49(52), 5352-5369. https://doi.org/10.1080/00036846.2017.1305097
• Krauß, C. (2017). Statistical arbitrage pairs trading strategies: Review and outlook. Journal of Economic Surveys, 31(2), 513-545. https://doi.org/10.1111/joes.12153
• Krauß, C., Do, X.A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702. https://doi.org/10.1016/j.ejor.2016.10.031
• Krauß, C., & Herrmann, K. (2017). On the power and size properties of cointegration tests in the light of high-frequency stylized facts. Journal of Financial Risk Management, 10(1).
• Pfeuffer, M. (2017). ctmcd: An R Package for Estimating the Parameters of a Continuous-Time Markov Chain from Discrete-Time Data. The R Journal, 9(2), 127-141.
• Stübinger, J., & Bredthauer, J. (2017). Statistical arbitrage pairs trading with high-frequency data. International Journal of Economics and Financial Issues, 7(4), 650-662.

• Dovern, J., Feldkircher, M., & Huber, F. (2016). Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR. Journal of Economic Dynamics & Control, 70, 86-100. https://doi.org/10.1016/j.jedc.2016.06.006
• Fischer, M., & Pfeuffer, M. (2016). IFRS 9 Impairment von Finanzinstrumenten: Parametrische Modellierung von PD-Kurven. Risiko Manager, 11(7), 10-14.
• Glas, A., & Hartmann, M. (2016). Inflation uncertainty, disagreement and monetary policy: Evidence from the ECB Survey of Professional Forecasters. Journal of Empirical Finance, 39 (B), 215-228. https://doi.org/10.1016/j.jempfin.2016.05.001
• Glas, A., Hübler, M., & Nunnenkamp, P. (2016). Catching up of emerging economies: the role of capital goods imports, FDI inflows, domestic investment and absorptive capacity. Applied Economics Letters, 23 (2), 117-120. https://doi.org/10.1080/13504851.2015.1054065
• Glas, A., Hübler, M., & Nunnenkamp, P. (2016). Indicators of Absorptive Capacity and Import‐induced South–North Convergence in Labour Intensities. World Economy, 39 (11), 1756-1791. https://doi.org/10.1111/twec.12300
• Grottke, M., Avritzer, A., Menasché, D.S., & Altman, E. (2016). On the efficiency of sampling and countermeasures to critical-infrastructure-targeted malware campaigns. ACM SIGMETRICS Performance Evaluation Review, 43(4), 33-42. https://doi.org/10.1145/2897356.2897361
• Grottke, M., Kim, D.S., Mansharamani, R., Manoj, N., Natella, R., & Trivedi, K.S. (2016). Recovery from software failures caused by Mandelbugs. IEEE Transactions on Reliability, 65(1), 70-87. https://doi.org/10.1109/TR.2015.2452933
• Kißlinger, A.-L., & Stummer, W. (2016). Robust statistical engineering by means of scaled Bregman distances. In C. Agostinelli, A. Basu, P. Filzmoser, D. Mukherjee (Eds.), Recent Advances in Robust Statistics: Theory and Applications. (pp. 81-113). Springer.
• Klein, I., Mangold, B., & Doll, M. (2016). Cumulative Paired ϕ-Entropy. Entropy, 18(7), 1-45. https://doi.org/10.3390/e18070248
• Rickels, W., Dovern, J., Hoffmann, J., Quaas, M.F., Schmidt, J.O., & Visbeck, M. (2016). Indicators for monitoring sustainable development goals: An application to oceanic development in the European Union. Earth's Future, 4(5), 252-267. https://doi.org/10.1002/2016EF000353
• Rickels, W., Dovern, J., & Quaas, M. (2016). Beyond fisheries: Common-pool resource problems in oceanic resources and services. Global Environmental Change-Human and Policy Dimensions, 40, 37-49. https://doi.org/10.1016/j.gloenvcha.2016.06.013
• Willert, V., & Schnaubelt, M. (2016). Data control: A feedback control design for clustering of networked data. At-Automatisierungstechnik, 64(8), 618-632. https://doi.org/10.1515/auto-2016-0059

• Dovern, J. (2015). A multivariate analysis of forecast disagreement: Confronting models of disagreement with survey data. European Economic Review, 80, 16-35. https://doi.org/10.1016/j.euroecorev.2015.08.009
• Dovern, J., Fritsche, U., Loungani, P., & Tamirisa, N. (2015). Information rigidities: Comparing average and individual forecasts for a large international panel. International Journal of Forecasting, 31(1), 144-154. https://doi.org/10.1016/j.ijforecast.2014.06.002
• Dovern, J., & Huber, F. (2015). Global prediction of recessions. Economics letters, 133, 81-84. https://doi.org/10.1016/j.econlet.2015.05.022
• Grottke, M., Avritzer, A., Menasché, D.S., Alonso, J., de Aguiar, L., & Alvarez, S.G. (2015). Models and metrics for the assessment of critical-infrastructure-targeted malware campaigns. In Proceedings of the 26th IEEE International Symposium on Software Reliability Engineering (ISSRE) (pp. 330-335). Gaithersburg, MD, USA, US.

• Dovern, J., Quaas, M.F., & Rickels, W. (2014). A comprehensive wealth index for cities in Germany. Ecological Indicators, 41, 79-86. https://doi.org/10.1016/j.ecolind.2014.01.009
• Dovern, J., & Van Roye, B. (2014). International transmission and business-cycle effects of financial stress. Journal of Financial Stability, 13, 1-17. https://doi.org/10.1016/j.jfs.2014.02.006
• Glas, A., & Hübler, M. (2014). The Energy-Bias of North–South Technology Spillovers: A Global, Bilateral, Bisectoral Trade Analysis. Environmental & Resource Economics, 58 (1), 59-89. https://doi.org/10.1007/s10640-013-9690-7
• Kilgert, B., Rybizki, L., Grottke, M., Neurath, M., & Neumann, H. (2014). Prospective long-term assessment of sedation-related adverse events and patient satisfaction for upper endoscopy and colonoscopy. Digestion, 90(1), 42-8. https://doi.org/10.1159/000363567

• Dovern, J. (2013). When are GDP forecasts updated? Evidence from a large international panel. Economics letters, 120 (3), 521-523. https://doi.org/10.1016/j.econlet.2013.06.007
• Grottke, M., & Schleich, B. (2013). How does testing affect the availability of aging software systems? Performance Evaluation, 70(3), 179–196. https://doi.org/10.1016/j.peva.2012.05.007
• Grottke, M., & Schleich, B. (2013). How the distribution of the number of items rated per user influences the quality of recommendation. Performance Evaluation, 70, 179-196. https://doi.org/10.1016/j.peva.2012.05.007

• Alonso, J., Grottke, M., Nikora, A., & Trivedi, K. (2012). The nature of the times to flight software failure during space missions. In Proceedings of the 2012 IEEE 23rd International Symposium on Software Reliability Engineering (pp. 331-340). Dallas, US.
• Dovern, J., Fritsche, U., & Slacalek, J. (2012). Disagreement among Forecasters in G7 Countries. Review of Economics and Statistics, 94(4), 1081-1096.
• Fischer, M., & Köck, C. (2012). Constructing and generalizing given multivariate copulas: A unifying approach. Statistics, 46(1), 1-12. https://doi.org/10.1080/02331888.2010.500028

2011

• Beckhaus, A., Karg, L.M., Graf, C., Grottke, M., & Neumann, D. (2011). A decision support scheme for software process improvement prioritization. In J. Cordeiro, A. Ranchordas, B. Shishka (Eds.), Software and Data Technologies. (pp. 85-93). Berlin: Springer Verlag.
• Dovern, J., & Weisser, J. (2011). Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7. International Journal of Forecasting, 27(2), 452-465. https://doi.org/10.1016/j.ijforecast.2010.05.016
• Grottke, M., Apte, V., Trivedi, K., & Woolet, S. (2011). Response time distributions in networks of queues. In R. Boucherie, N. Van Dijk (Eds.), Queueing Networks: A Fundamental Approach. (pp. 587-641). New York: Springer.
• Karg, L.M., Grottke, M., & Beckhaus, A. (2011). A systematic literature review of software quality cost research. Journal of Systems and Software, 84(3), 415-427. https://doi.org/10.1016/j.jss.2010.11.904
• Trivedi, K., Mansharamani, R., Kim, D.S., Grottke, M., & Nambiar, M. (2011). Recovery from Failures Due to Mandelbugs in IT Systems. In Proceedings of the 2011 IEEE 17th Pacific Rim International Symposium on Dependable Computing (pp. 224-233). Pasadena, CA, USA, US.

2010

• Dovern, J., & Blank, S. (2010). What macroeconomic shocks affect the German banking system? Analysis in an integrated micro‐macro model. Journal of Financial Economic Policy, 2(2), 126-148.
• Dovern, J., Meier, C.-P., & Vilsmeier, J. (2010). How resilient is the German banking system to macroeconomic shocks. Journal of Banking & Finance, 34(8), 1839-1848. https://doi.org/10.1016/j.jbankfin.2009.12.001
• Fischer, M., & Herrmann, K. (2010). An Alternative Maximum Entropy Model for Time-Varying Moments with Application to Financial Returns. Studies in Nonlinear Dynamics and Econometrics, 14(3), 2-21. https://doi.org/10.2202/1558-3708.1694
• Grottke, M., Karg, L.M., & Beckhaus, A. (2010). Team factors and failure processing efficiency: An exploratory study of closed and open source software development. In Proceedings of the 34th Annual IEEE International Computer Software and Applications Conference (pp. 188-197). Seoul: Seoul: IEEE Computer Society.
• Grottke, M., Nikora, A., & Trivedi, K. (2010). An empirical investigation of fault types in space mission system software. In Proceedings of the 40th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (pp. 447-456). Chicago, IL: Chicago: IEEE Computer Society.
• Klein, I., Köck, C., & Tinkl, F. (2010). Spatial-serial dependency in multivariate GARCH models and dynamic copulas: A simulation study. Ekonomia Menedżerska, 7, 43-62.
• Klein, I., Stroebel, A., Bergner, M., Reulbach, U., Biermann, T., Groemer, T.W., & Kornhuber, J. (2010). Statistical methods for detecting and comparing periodic data and their application to the circadian rhythm of bodily harm. Journal of Circadian Rhythms, 8(1), 1-10.

2009

• Beckhaus, A., Karg, L.M., Graf, C., Grottke, M., & Neumann, D. (2009). Prioritization of software process improvements: A COQUALMO-based case study and derived decision support scheme. In Proceedings of the 4th International Conference on Software and Data Technologies (pp. 139-144). Sofia.
• Fischer, M., Köck, C., Weigert, F., & Schlüter, S. (2009). An empirical analysis of multivariate copula models. Quantitative Finance, 9(7), 839-854. https://doi.org/10.1080/14697680802595650
• Gartner, H., Blien, U., Stüber, H., & Wolf, K. (2009). Regional price levels and the agglomeration wage differential in western Germany. The annals of regional science, 43(1), 71-88. https://doi.org/10.1007/s00168-007-0205-8
• Grottke, M., & Graf, C. (2009). Modeling and predicting software failure costs. In Proceedings of the 33rd Annual IEEE International Computer Software and Applications Conference (pp. 180-189). Seattle, WA.
• Karg, L.M., Grottke, M., & Beckhaus, A. (2009). Conformance quality and failure costs in the software industry: An empirical analysis of open source software. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1386-1390). Hong Kong.

2008

• Dovern, J., Döpke, J., Fritsche, U., & Slacalek, J. (2008). The Dynamics of European Inflation Expectations. The B.E. Journal of Macroeconomics, 8(1). https://doi.org/10.2202/1935-1690.1540
• Dovern, J., Döpke, J., Fritsche, U., & Slacalek, J. (2008). The Sticky Information Phillips Curve. European Evidence. Journal of Money, Credit and Banking, 40(7), 1513-1519.
• Grottke, M., Matias, R., & Trivedi, K. (2008). The fundamentals of software aging. In 2008 IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp) (Eds.), Proceedings of the 1st International Workshop on Software Aging and Rejuvenation/19th IEEE International Symposium on Software Reliability Engineering. Seattle, WA, US.
• Grottke, M., Sun, H., Fricks, R.M., & Trivedi, K. (2008). Ten fallacies of availability and reliability analysis. In Proceedings of the 5th International Service Availability Symposium (pp. 187-206). Tokyo: Springer-verlag.
• Klein, I., & Fischer, M. (2008). A Note on the Kurtosis Ordering of the Generalized Secant Hyperbolic Distribution. Communications in Statistics-Theory and Methods, 37(1), 1-7. https://doi.org/10.1080/03610920701648839

2007

• Dovern, J., & Nunnenkamp, P. (2007). Aid and Growth Accelerations: An Alternative Approach to Assessing the Effectiveness of Aid. Kyklos, 60(3), 359-383. https://doi.org/10.1111/j.1467-6435.2007.00376.x
• Fischer, M., & Klein, I. (2007). Construction of Symmetric Generalized FGM Copulas by means of certain Univariate Distributions. Metrika, 65(2), 243-260. https://doi.org/10.1007/s00184-006-0075-6
• Fischer, M., Klein, I., & Horn, A. (2007). Tukey-type Distributions in the context of financial data. Communications in Statistics-Theory and Methods, 36(1), 23-35. https://doi.org/10.1080/03610920500476382
• Grottke, M., & Trivedi, K. (2007). Fighting bugs: Remove, retry, replicate, and rejuvenate. Computer, 40(2), 107-109. https://doi.org/10.1109/MC.2007.55
• Okamura, H., Grottke, M., Dohi, T., & Trivedi, K. (2007). Variational Bayesian approach for interval estimation of NHPP-based software reliability models. In Proceedings of the International IEEE Conference on Dependable Systems and Networks 2007 (pp. 698-707). Edinburgh.

2006

• Avritzer, A., Bondi, A.B., Grottke, M., Trivedi, K., & Weyuker, E.J. (2006). Performance assurance via software rejuvenation: Monitoring, statistics and algorithms. In Proceedings of the International Conference on Dependable Systems and Networks 2006 (pp. 435-444). Philadelphia, PA.
• Grottke, M., Li, L., Vaidyanathan, K., & Trivedi, K. (2006). Analysis of software aging in a web server. IEEE Transactions on Reliability, 55(3), 411-420. https://doi.org/10.1109/TR.2006.879609
• Klein, I., & Fischer, M. (2006). Power Kurtosis Transformations: De finitions, Properties and Ordering. Advances in Statistical Analysis, 90(3), 395-401. https://doi.org/10.1007/s10182-006-0241-1
• Klein, I., & Fischer, M. (2006). Skewness by Splitting the scale parameter. Communications in Statistics-Theory and Methods, 35(7), 1159-1171. https://doi.org/10.1080/03610920600692730

2005

• Grottke, M., & Trivedi, K. (2005). On a method for mending time to failure distributions. In Proceedings of the International Conference on Dependable Systems and Networks 2005 (pp. 560-569). Yokohama.
• Grottke, M., & Trivedi, K. (2005). Software faults, software aging and software rejuvenation. Journal of Reliability Engineering Association of Japan, 27(7), 425-438. https://doi.org/10.11348/reajshinrai.27.7_425

2004

• Klein, I., & Fischer, M. (2004). Kurtosis modelling by means of the J-transformation. Allgemeines Statistisches Archiv, 88(1), 35-50. https://doi.org/10.1007/s101820400158

2002

• Grottke, M. (2002). A vector Markov model for structural coverage growth and the number of failure occurrences. In Proceedings of the Thirteenth International IEEE Symposium on Software Reliability Engineering (pp. 304-315).