Panel data, nonlinear models, Difference-in-Differences, Network Data, Innovation
11. Arellano, M., S. Bonhomme, S. Borodich Suarez, M. Schumann, X. Shi and G. Tripathi (2025). Erratum to "Robust Priors in Nonlinear Panel Data Models"; comment, Econometrica (link)
10. Dette, H. & M. Schumann (2024). "Testing for equivalence of pre-trends in Difference-in-Differences estimation". Journal of Business & Economic Statistics (open access). The tests are implemented in the R-package EquiTrends (together with Ties Bos).
9. 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. Paper + supplementary material
8. Arroyabe, M. F., Schumann, M. and Arranz, C. F. A. (2022). "Mapping the entrepreneurial university literature: a text mining approach", Studies in Higher Education, 47:5, 955-963 .
7. Schumann, M. (2022). "Second order bias reduction for nonlinear panel data models with fixed effects based on expected quantities", Econometric Theory, Paper + code
6. Arroyabe, M.F. and 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.
5. Schumann, M., Severini, T. A. and G. Tripathi (2021). "Integrated likelihood based inference for nonlinear panel data models with unobserved effects", Journal of Econometrics, 223(1), 73-95. Paper + Supplement + code
4. Fernandez de Arroyabe, J.C., N. Arranz, M. Schumann and M.F. Arroyabe (2021). "The development of CE business models in firms: The role of circular economy capabilities", Technovation, 106, 102292.
3. Fernandez de Arroyabe, J. C., M. Schumann, V. Sena and P. Lucas (2020). "Understanding the network structure of agri-food FP7 projects: An approach to the effectiveness of innovation systems", Technological Forecasting and Social Change, Volume 162.
2. Arranz, N., M. F. Arroyabe and M. Schumann (2020). The role of NPOs and international actors in the national innovation system: A network-based approach, Technological Forecasting and Social Change. 159, 120183-120183.
1. Schumann, M. and G. Tripathi (2017). Convexity of probit weights, Statistics & Probability letters, vol. 143, pp. 81–85.
Borodich Suarez, S., M. Schumann and G. Tripathi. "Integrated likelihood based inference for dynamic binary choice panel data models with fixed effects"
Borodich Suarez, S., M. Schumann and G. Tripathi. "Robust priors in nonlinear panel data models for estimating average marginal effects"
Dette, H., T. Kutta and M. Schumann: "Quantifying forecasting performance of pooled estimators in panel data"
Schumann, M.: "Likelihood specification testing in nonlinear panel data models with fixed effects" (presented at conferences)