ENSAE Paris - École d'ingénieurs pour l'économie, la data science, la finance et l'actuariat

Advanced Methods in Computational Economics (CREST)


  • Marlon Azinovic, Luca Gaegauf, and Simon Scheidegger. Deep equilibrium nets. International Economic Review, 2022. doi: https://doi.org/10.1111/iere.12575. URL  ttps://onlinelibrary.wiley.com/doi/abs/10.1111/iere.12575.
  • Johannes Brumm and Simon Scheidegger. Using adaptive sparse grids to solve high-dimensional dynamic models. Econometrica, 85(5):1575- 1612, 2017. ISSN 1468-0262. doi: 10.3982/ECTA12216. URL http://dx.doi.org/10.3982/ECTA12216.
  • Johannes Brumm, Christopher Krause, Andreas Schaab, and Simon Scheidegger. Sparse grids for dynamic economic models. Available at SSRN 3979412, 2021.
  • Hans-Joachim Bungartz and Michael Griebel. Sparse grids. Acta numerica, 13:147–269, 2004.
  • Hui Chen, Antoine Didisheim, and Simon Scheidegger. Deep structural estimation: With an application to option pricing. Available at SSRN 3782722, 2021.
  • Paul G. Constantine. Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2015. ISBN 1611973856, 9781611973853.
  • Aryan Eftekhari and Simon Scheidegger. High-dimensional dynamic stochastic model representation. Available at SSRN 3603294 - Forthcoming in SIAM SISC, 2020.
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.
  • Felix Kubler and Simon Scheidegger. Self-justified equilibria: Existence and computation. 2018.
  • L. Ljungqvist and T.J. Sargent. Recursive macroeconomic theory. Mit Press, 2000. ISBN 9780262194518. URL http://books.google.ch/books?id=3LmXQgAACAAJ.
  • Carl Edward Rasmussen. Gaussian processes in machine learning. In Advanced lectures on machine learning, pages 63–71. Springer, 2004.
  • Philipp Renner and Simon Scheidegger. Machine learning for dynamic incentive problems. Available at SSRN 3462011, 2018. Working paper.
  • Simon Scheidegger and Ilias Bilionis. Machine learning for high-dimensional dynamic stochastic economies. Journal of Computational Science, 33:68 – 82, 2019. ISSN 1877-7503. doi: https://doi.org/10.1016/j.jocs.2019.03.004. URL http://www.sciencedirect.com/science/article/pii/S1877750318306161.
  • Nancy L. Stokey, Robert E. Lucas, and Edward C. Prescott. Recursive Methods in Economic Dynamics. Harvard University Press, 1989. ISBN 9780674750968. URL http://www.jstor.org/stable/j.ctvjnrt76.
  • Xiu Yang, Minseok Choi, Guang Lin, and George Em Karniadakis. Adaptive anova decomposition of stochastic incompressible and compressible flows. Journal of Computational Physics, 231(4):1587 – 1614, 2012. ISSN 0021-9991. doi: https://doi.org/10.1016/j.jcp.2011.10.028. URL http://www.sciencedirect.com/science/article/pii/S0021999111006280.