Dr Philipp Renner
Senior LecturerResearch Overview
My research interests lie in computational economics and game theory. I am particularly trying to understand the nature of incentive problems in a dynamic context. These kind of problems occur e.g. in taxation, manager remuneration and insurance. They pose a significant mathematical challenge for two reasons. On the one hand they are bilevel optimization problems. And on the other hand it is necessary to solve complicated dynamic programs.
PhD Supervision Interests
I am interested in any project that involves a novel computational challenge. It can be that either the size of the problem is prohibitive for conventional methods or that even writing down the solution conditions is hard. For example finding solutions to a high dimensional dynamic program or equilibria of a dynamic game.
Selected Publications
Monetary Policy with Persistent Supply Shocks
Renner, P., Scheidegger, S., Nuño, G. 2024 In: SSRN.
Journal article
Machine learning for dynamic incentive problems
Renner, P., Scheidegger, S. 11/2017 Lancaster : Lancaster University, Department of Economics
Working paper
Discrete-Time Dynamic Principal-Agent Models: Contraction Mapping Theorem and Computational Treatment
Renner, P., Schmedders, K. 1/11/2020 In: Quantitative Economics. 11, 4, p. 1215-1251. 37 p.
Journal article
A Polynomial Optimization Approach to Principal-Agent Problems
Renner, P., Schmedders, K. 03/2015 In: Econometrica. 83, 2, p. 729-769. 41 p.
Journal article
Computing generalized Nash equilibria by polynomial programming
Couzoudis, E., Renner, P. 06/2013 In: Mathematical Methods of Operational Research. 77, 3, p. 459-472. 14 p.
Journal article
Finding all pure-strategy equilibria in games with continuous strategies
Judd, K., Renner, P., Schmedders, K. 07/2012 In: Quantitative Economics. 3, 2, p. 289-331. 43 p.
Journal article
All Publications
”Machine learning for dynamic incentive problems“ - ESEM
Participation in conference -Mixed Audience
”Machine learning for dynamic incentive problems“ - NBER Conference
Participation in conference -Mixed Audience
”Machine learning for dynamic incentive problems“
Participation in conference -Mixed Audience
”Machine learning for dynamic incentive problems“
Participation in conference -Mixed Audience
”Machine learning for dynamic incentive problems“
Participation in conference -Mixed Audience
Platform for Advanced Scientific Computing 2018
Participation in conference -Mixed Audience
“An augmented first order approach for dynamic principal agent models”
Participation in conference -Mixed Audience
EEA-ESEM Lisbon 2017
Participation in conference -Mixed Audience
“Dynamic Principal Agent Models”
Participation in conference -Mixed Audience
An augmented first order approach
Invited talk
“Dynamic Principal Agent Models”
Participation in conference -Mixed Audience
“Dynamic Principal Agent Models”
Participation in conference -Mixed Audience
“Dynamic Principal Agent Models”
Participation in conference -Mixed Audience
“Polynomial Programming in Economics”
Participation in workshop, seminar, course
“Quantity Pre-Commitment and Bertrand competition”
Participation in conference -Mixed Audience
“Quantity precommitment and Bertrand competition: A dynamic games approach”
Participation in conference -Mixed Audience
“Studying Two StageGames by Polynomial Programming”
Participation in conference -Mixed Audience
- Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy
- Economics Research Group
- Industrial Organisation and Economic Theory
- Macroeconomics and Financial Markets