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About
My research lies at the intersection of applied mathematics and economics, focusing on developing and implementing advanced computational solutions for complex economic problems. For example I use artificial neural networks via TensorFlow to solve a macro-prudential policy problem.
Beyond research, I have a strong passion for quantitative pedagogy. I have innovated in online learning environments, notably using Moodle and STACK to create partially randomized, auto-graded assessments, enhancing student learning and assessment integrity. Link to sample questions
Publications
Renner, P. and K. Schmedders (2020). “Discrete-Time Dynamic Principal--Agent Models: Contraction Mapping Theorem and Computational Treatment”. Quantitative Economics 83(2), 729–769.
Renner, P. and K. Schmedders (2015). “A Polynomial Optimization Approach to Principal-Agent Problems”. Econometrica 83(2), 729–769.
Kubler, F., P. Renner, and K. Schmedders (2014). “Chapter 11 - Computing All Solutions to PolynomialEquations in Economics”. Schmedders, K. and K. L. Judd, eds. Handbook of Computational Economics Vol. 3. Vol. 3. Handbook of Computational Economics. Elsevier, pp.599–652.
Couzoudis, E. and P. Renner (2013). “Computing Generalized Nash Equilibria by Polynomial Programming”. Mathematical Methods of Operations Research 77(3), 459–472.
Judd, K., P. Renner, and K. Schmedders (2012). “Finding All Pure-Strategy Equilibria in Static and Dynamic Games with Continuous Strategies”. Quantitative Economics 3 (2).
Teaching
Advanced Microeconomics
Mathematical Economics
Research Skills for Economists
Quantitative Methods for Economics
Current Position
Senior Lecturer (Associate Professor with tenure), Lancaster School of Management
Education
PhD in Mathematics, ETH Zurich, Switzerland, 2013
Master in Mathematics, Technical University of Kaiserslautern, Germany, 2008
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