M. Schumann

Recent publications

• Dette, H., & Schumann, M. (2024). Testing for Equivalence of Pre-Trends in Difference-in-Differences Estimation. Journal of Business & Economic Statistics, 42(4), 1289-1301. https://doi.org/10.1080/07350015.2024.2308121

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• SCHUMANN, M. (2023). Second-Order Bias Reduction For Nonlinear Panel Data Models With Fixed Effects Based On Expected Quantities. Econometric Theory, 39(4), 693-736. Article PII S0266466622000160. https://doi.org/10.1017/S0266466622000160

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• Schumann, M., Severini, T. A., & Tripathi, G. (2023). The role of score and information bias in panel data likelihoods. Journal of Econometrics, 235(2), 1215-1238. https://doi.org/10.1016/j.jeconom.2022.08.011

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• Arroyabe, M. F., & Schumann, M. (2022). On the Estimation of True State Dependence in the Persistence of Innovation*. Oxford Bulletin of Economics and Statistics, 84(4), 850-893. https://doi.org/10.1111/obes.12482

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• Arroyabe, M. F., Schumann, M., & Arranz, C. F. A. (2022). Mapping the entrepreneurial university literature: a text mining approach. Studies in Higher Education, 47(5), 955-963. https://doi.org/10.1080/03075079.2022.2055318

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• de Arroyabe, J. C. F., Arranz, N., Schumann, M., & Arroyabe, M. F. (2021). The development of CE business models in firms: The role of circular economy capabilities. Technovation, 106, Article 102292. https://doi.org/10.1016/j.technovation.2021.102292

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• Schumann, M., Severini, T. A., & Tripathi, G. (2021). Integrated likelihood based inference for nonlinear panel data models with unobserved effects. Journal of Econometrics, 223(1), 73-95. https://doi.org/10.1016/j.jeconom.2020.10.001

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• de Arroyabe, J. C. F., Schumann, M., Sena, V., & Lucas, P. (2021). Understanding the network structure of agri-food FP7 projects: An approach to the effectiveness of innovation systems. Technological Forecasting and Social Change, 162, Article 120372. https://doi.org/10.1016/j.techfore.2020.120372

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• Arranz, N., Arroyabe, M. F., & Schumann, M. (2020). The role of NPOs and international actors in the national innovation system: A network-based approach. Technological Forecasting and Social Change, 159, Article 120183. https://doi.org/10.1016/j.techfore.2020.120183

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• Schumann, M., & Tripathi, G. (2018). Convexity of probit weights. Statistics & Probability Letters, 143, 81-85. https://doi.org/10.1016/j.spl.2018.07.022

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