Publications

Peer-reviewed Publications

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., & 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://dx.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).

Other Publications

2021

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

2020

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).
Born, B., Dovern, J., & Enders, Z. (2020). Expectation Dispersion, Uncertainty, and the Reaction to News. München: Munich Society for the Promotion of Economic Research - CESifo GmbH.
Buchheim, L., Dovern, J., Krolage, C., & Link, S. (2020). Zur Reaktion von Unternehmen auf die Coronakrise: Welche Rolle spielen die erwartete Krisendauer und die Geschäftslage vor der Krise?
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., Buchheim, L., Krolage, C., & Link, S. (2020). Firm-Level Expectations and Behavior in Response to the Covid-19 Crisis.
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., Müller, L.S., & Wohlrabe, K. (2020). How Do Firms Form Expectations of Aggregate Growth? New Evidence from a Large-Scale Business Survey. Munich.
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
Knoll, J., & Stübinger, J. (2020). Machine-Learning-Based Statistical Arbitrage Football Betting. Künstliche Intelligenz, 34(1), 69-80. https://doi.org/10.1007/s13218-019-00610-4
Kölbl, L., & Grottke, M. (2020). Obtaining More Specific Topics and Detecting Weak Signals by Topic Word Selection. In Hoang Pham (Eds.), Reliability and Statistical Computing. (pp. 193-206). Springer.
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., & Adler, K. (2020). How to identify varying lead-lag effects in time series data: Implementation, validation, and application of the generalized causality algorithm. Algorithms, 13(4). https://doi.org/10.3390/A13040095
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

2019

Bazhenov, O., Oykher, A., & Baev, D. (2019). The Impact of Financial and Economic Crises on the Performance Indicators of Copper Mining Enterprises. In PROCEEDINGS OF THE 2ND INTERNATIONAL SCIENTIFIC CONFERENCE ON NEW INDUSTRIALIZATION: GLOBAL, NATIONAL, REGIONAL DIMENSION (SICNI 2018) (pp. 133-140). Ekaterinburg, RUSSIA: PARIS: ATLANTIS PRESS.
Endres, S. (2019). Review of stochastic differential equations in statistical arbitrage pairs trading. Managerial and Decision Economics, 20(2), 71-118. https://doi.org/DOI: https://doi.org/10.7494/manage.2019.20.2.71
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
Rende, J. (2019). Pairs trading with the persistence-based decomposition model. Managerial and Decision Economics, 2, 151-180. https://doi.org/10.7494/manage.2019.20.2.151
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

2018

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://dx.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

2017

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
Clegg, M., Krauss, C., & Rende, J. (2017). partialCI: An R package for the analysis of partially cointegrated time series.
Doll, M., Seebauer, M., & Tonn, M. (2017). Bargaining over waiting time in gain and loss framed ultimatum games.
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
Dovern, J., & Kenny, G. (2017). The Long-term Distribution of Expected Inflation in the Euro Area: What Has Changed since the Great Recession?
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).
Mangold, B., & Konopik, J. (2017). A general class of entropy based control charts. Erlangen: Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, Nürnberg.
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.

2016

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

2015

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.

2014

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

2013

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
Dovern, J., Fritsche, U., Tamirisa, N., & Loungani, P. (2013). Information Rigidities in Economic Growth Forecasts: Evidence from a Large International Panel.
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

2012

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
Grottke, M. (2012). Prognose von Softwarezuverlässigkeit, Softwareversagensfällen und Softwarefehlern. In Mertens Peter, Susanne Rässler (Eds.), Prognoserechnung. (pp. 585-619). Berlin: Springer/Physica.
Grottke, M., & Schleich, B. (2012). Cost Optimality in Testing and Rejuvenation. In 2012 IEEE 23rd International Symposium on Software Reliability Engineering Workshops (ISSREW) (pp. 259-264). Dallas, TX, US: IEEE.

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., Rickels, W., & Klepper, G. (2011). Large-Scale Intentional Interventions into the Climate System? Assessing the Climate Engineering Debate. In Kiel Earth Institute (Eds.), Report for the German Federal Ministry of Education and Research, Kiel, December 2011..
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
Klein, I. (2011). Möglichkeiten der empirischen Erfassung von Automietpreisen. In Holzer-Thieser A., Roth S. (Hrg.), Versicherungswissenschaft - im Wandel wirtschaftsrechtlicher und rechtsökonomischer Analysen mit Finanzmarkt- und Freiberufsrecht. FS für Harald Herrmann. (S. 323-331). Nürnberg: IF Verlag.
Klein, I. (2011). Van Zwet Ordering and the Ferreira-Steel Family of Skewed Distributions.
Klein, I. (2011). Van Zwet Ordering for Fechner Asymmetry. Nürnberg.
Klein, I., & Christa, F. (2011). Families of Copulas closed under the Construction of Generalized Linear Means.
Trivedi, K., Grottke, M., & Andrade, E. (2011). Software fault mitigation and availability assurance techniques. International Journal of Systems Assurance Engineering and Management, 1(4), 340-350. https://doi.org/10.1007/s13198-011-0038-9
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.
Dovern, J., Fritsche, U., & Slacalek, J. (2009). Disagreement among Forecasters In G7 Countries.
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.
Dovern, J., Fritsche, U., Slacalek, J., & Döpke, J. (2008). Sticky Information Phillips Curves: European Evidence.
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
Klein, I., & Grottke, M. (2008). On J.M. Keynes' "The Principal Averages and the Laws of Error which Lead to Them" - Refinement and Generalisation.
Trivedi, K., Ciardo, G., Dasarathy, B., Grottke, M., Matias, R., Rindos, A., & Vashaw, B. (2008). Achieving and assuring high availability. In Proceedings of the 5th International Service Availability Symposium (pp. 20-25). Miami, FL.

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., Bissantz, N., & Manns, C. (2006). Competitive Intelligence bei einem Pharma-Hersteller. HMD : Praxis der Wirtschaftsinformatik, 249, 91-98.
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
Grottke, M., & Trivedi, K. (2005). Truncated non-homogeneous Poisson process models - Properties and performance. OPSEARCH, 42(4), 310-321.
Klein, I. (2005). Bivariate Abhängigkeitsmessung für ordinalskalierte Merkmale im Falle fixierter Randverteilungen. In Brachinger H.W., Hamerle A., Münnich R., Schweitzer W. (Hrg.), Wirtschaftsstatistik - Festschrift zum 65. Geburtstag von Prof. Dr. E. Schaich. (S. 81-109). München: Vahlen.
Klein, I. (2005). Streuungsmessung ordinalskalierter Merkmale mittels Rangordnungsstatistiken. In Dobbener R, Kolb U, Wan Hussin D (Hrg.), Arbeiten aus der Statistik - Festschrift für F. Vogel. (S. 1-18). Bamberg.

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
Klein, I., & Fischer, M. (2004). Tailabhängigkeit und Asymmetrie in multivariaten Finanzmarktdaten. In M. Bank, B. Schiller (Hrg.), Finanzintermediation: Theoretische, wirtschaftspolitische und praktische Aspekte aktueller Entwicklungen im Bank- und Börsenwesen. Festschrift für Wolfgang Gehrke zum 60. Geburtstag. (S. 69-102). Stuttgart: Schäffer-Pöschel Verlag.

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).
Klein, I., & Fischer, M. (2002). gh-transformed of symmetrical distributions. In Klein I., Mittnik S. (Eds.), Contributions to modern econometrics. (pp. 119-134). Boston: Kluwer Academic Publishers.
Klein, I., & Fischer, M. (2002). Symmetrical gh-transformed distributions. In Klein Ingo, Mittnik Stefan (Eds.), Contributions to Modern Econometrics. (pp. 119-134). Boston: Kluwer Academic Publishers.
Klein, I., & Mittnik, S. (2002). Contributions to modern econometrics - From data analysis to economic policy. Boston: Kluwer Academic Publishers.

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