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Zwetsloot, I. M., Mahmood, T., Taiwo, F. M., & Wang, Z. (2023). A real-time monitoring approach for bivariate event data. Applied Stochastic Models in Business and Industry, 39(6), 789-817. https://doi.org/10.1002/asmb.2800 -
Bouman, T., Bolderdijk, J. W., Renes, R. J., van der Wal, A. J., Paradies, G., Roeser, S., van de Grift, L., van Uffelen, N., de Vries, G., Onwezen, M. C., Wals, A. E. J., & Aarts, M. N. C. (2023). Een verbod op fossiele reclame: Essentieel, maar niet voldoende. Tweede Kamer. https://www.tweedekamer.nl/downloads/document?id=2023D44350 -
Kleibergen, F., Kong, L., & Zhan, Z. (2023). Identification Robust Testing of Risk Premia in Finite Samples. Journal of Financial Econometrics, 21(2), 263–297. https://doi.org/10.1093/jjfinec/nbac010 -
Linde, J., Gietl, D., Sonnemans, J., & Tuinstra, J. (2023). The effect of quantity and quality of information in strategy tournaments. Journal of Economic Behavior & Organization, 211, 305-323. https://doi.org/10.1016/j.jebo.2023.04.024 -
Antao, J., de Mast, J., Marques, A., Franssen, F. M. E., Spruit, M. A., & Deng, Q. (2023). Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases. Expert Review of Respiratory Medicine, 17(12), 1207-1219. https://doi.org/10.1080/17476348.2024.2302940 -
Georgallis, P. (2023, March 1). Data and Stata code [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.18551108.v1
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Lengyel, A., & Giuliodori, M. (2023, October 7). Data of the demand shock paper [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.24042822.v4
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Ziegler, A. G. B., Offerman, T. J. S., & Romagnoli, G. (2023, June 22). Dataset for: "Why are open ascending auctions popular? The role of information aggregation and behavioral biases" [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.23552004.v2
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Hanaki, N., Hommes, C. H., Kopányi, D., Kopányi-Peuker, A., & Tuinstra, J. (2023, October 26). Data Package Forecasting Returns instead of Prices Exacerbates Financial Bubbles [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.24441913.v1
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Ensing, S., & Amrit, C. (2023). Agent-based modelling and simulation of public transport to identify effects of network changes on passenger flows. In H. Zaynidinov, M. Singh, U. Shanker Tiwary, & D. Singh (Eds.), Intelligent Human Computer Interaction: 14th International Conference, IHCI 2022, Tashkent, Uzbekistan, October 20–22, 2022 : revised selected papers (pp. 373-385). (Lecutre Notes in Computer Science; Vol. 13741). Springer. https://doi.org/10.1007/978-3-031-27199-1_37
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