Simon Scheidegger

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Felix KublerUniversity of Zurich and Swiss Finance InstituteVerified email at bf.uzh.ch
Roger KäppeliETH ZurichVerified email at sam.math.ethz.ch
Laurence KotlikoffBoston UniversityVerified email at bu.edu
Olaf SchenkProfessor, Institute of Computing, Universita della Svizzera italiana, SIAM FellowVerified email at usi.ch
Polbin AndreyRussian Presidential Academy of National Economy and Public AdministrationVerified email at ranepa.ru
Tobias FischerUniversity of WroclawVerified email at uwr.edu.pl
Aryan EftekhariUniversity of LausanneVerified email at unil.ch
Johannes BrummKarlsruhe Insitute of TechnologyVerified email at kit.edu
Matthias BollhöferProfessor for Numerical Analysis, TU BraunschweigVerified email at tu-bs.de
Friedrich-Karl ThielemannProf. of theor. physics em., Univ. of Basel and guest scientist GSI DarmstadtVerified email at unibas.ch
Dmitry MikushinUniversity of LausanneVerified email at kernelgen.org
Ilias BilionisAssociate Professor, Mechanical Engineering, Purdue UniversityVerified email at purdue.edu
Philipp RennerSenior Lecturer, Department of Economics, Lancaster UniversityVerified email at lancaster.ac.uk
Ue-Li PenUniversity of Toronto, Canadian Institute of Theoretical AstrophysicsVerified email at cita.utoronto.ca
Anthony MezzacappaNewton W. and Wilma C. Thomas Endowed Chair and College of Arts and Sciences Excellence ProfessorVerified email at utk.edu
Hui ChenProfessor of Finance, MITVerified email at mit.edu
Irina SagertLos Alamos National LaboratoryVerified email at lanl.gov
Dimosthenis PasadakisPostdoctoral fellow, Università della Svizzera italianaVerified email at usi.ch
Christopher KrauseKarlsruhe Institute of TechnologyVerified email at kit.edu
Andreas SchaabAssistant Professor, Toulouse School of EconomicsVerified email at tse-fr.eu

Simon Scheidegger

Department of Economics, Faculty of Business and Economics (HEC Lausanne), University of Lausanne

Verified email at unil.ch - Homepage

Climate-change economics computational economics machine learning deep learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Using adaptive sparse grids to solve high‐dimensional dynamic models J Brumm, S Scheidegger Econometrica 85 (5), 1575-1612, 2017	161	2017
The influence of model parameters on the prediction of gravitational wave signals from stellar core collapse S Scheidegger, R Käppeli, SC Whitehouse, T Fischer, M Liebendörfer arXiv preprint arXiv:1001.1570, 2010	121	2010
Large-scale sparse inverse covariance matrix estimation M Bollhöfer, A Eftekhari, S Scheidegger, O Schenk SIAM Journal on Scientific Computing 41 (1), A380-A401, 2019	86	2019
Gravitational waves from 3D MHD core collapse simulations S Scheidegger, T Fischer, S Whitehouse, L Matthias Astronomy & Astrophysics, 0	84*
Deep Equilibrium Nets M Azinovic, L Gaegauf, S Scheidegger https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3393482, 0	75*
Machine learning for high-dimensional dynamic stochastic economies S Scheidegger, I Bilionis Journal of Computational Science 33, 68-82, 2019	67	2019
Making Carbon Taxation a Generational Win Win LJ Kotlikoff, F Kubler, A Polbin, JD Sachs, S Scheidegger NBER Working Paper No. 25760, 2019	55	2019
FISH: a three-dimensional parallel magnetohydrodynamics code for astrophysical applications R Käppeli, SC Whitehouse, S Scheidegger, UL Pen, M Liebendörfer The Astrophysical Journal Supplement Series 195 (2), 20, 2011	40	2011
Gravitational waves from supernova matter S Scheidegger, SC Whitehouse, R Käppeli, M Liebendörfer Classical and Quantum Gravity 27 (11), 114101, 2010	37	2010
Can today's and tomorrow's world uniformly gain from carbon taxation? LJ Kotlikoff, F Kubler, A Polbin, S Scheidegger National Bureau of Economic Research, 2021	20	2021
Computing equilibria in dynamic stochastic macro-models with heterogeneous agents J Brumm, F Kubler, S Scheidegger Advances in economics and econometrics 2, 185, 2017	19	2017
Scalable high-dimensional dynamic stochastic economic modeling J Brumm, D Mikushin, S Scheidegger, O Schenk Journal of Computational Science 11, 12-25, 2015	19	2015
Pareto-improving carbon-risk taxation L Kotlikoff, F Kubler, A Polbin, S Scheidegger Economic Policy 36 (107), 551-589, 2021	15	2021
Machine Learning for Dynamic Incentive Problems P Renner, S Scheidegger https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3282487, 2018	14	2018
The climate in climate economics D Folini, F Kübler, A Malova, S Scheidegger arXiv preprint arXiv:2107.06162, 2021	12	2021
Rethinking large-scale economic modeling for efficiency: Optimizations for gpu and xeon phi clusters S Scheidegger, D Mikushin, F Kubler, O Schenk 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2018	12	2018
Deep structural estimation: With an application to option pricing H Chen, A Didisheim, S Scheidegger arXiv preprint arXiv:2102.09209, 2021	11	2021
Self-justified equilibria: Existence and computation F Kubler, S Scheidegger Available at SSRN 3494876, 2019	11	2019
Pricing American Options under High-Dimensional Models with Recursive Adaptive Sparse Expectations S Scheidegger, A Treccani Journal of Financial Econometrics, 2018	11	2018
Sparse grids for dynamic economic models J Brumm, C Krause, A Schaab, S Scheidegger Available at SSRN 3979412, 2021	10	2021

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