University of Zurich
Moussonstrasse 15, 8044 Zurich
E-Mail: no email available
Institutional Affiliation: University of Zurich
Information about this author at RePEc
NBER Working Papers and Publications
|May 2013||Nonlinear Programming Method for Dynamic Programming|
with Yongyang Cai, Kenneth L. Judd, Valentina Michelangeli, Che-Lin Su: w19034
A nonlinear programming formulation is introduced to solve infinite horizon dynamic programming problems. This extends the linear approach to dynamic programming by using ideas from approximation theory to avoid inefficient discretization. Our numerical results show that this nonlinear programming method is efficient and accurate.
Published: Cai, Yongyang & Judd, Kenneth L. & Lontzek, Thomas S. & Michelangeli, Valentina & Su, Che-Lin, 2017. "A Nonlinear Programming Method For Dynamic Programming," Macroeconomic Dynamics, Cambridge University Press, vol. 21(02), pages 336-361, March. citation courtesy of
|January 2013||The Social Cost of Stochastic and Irreversible Climate Change|
with Yongyang Cai, Kenneth L. Judd: w18704
There is great uncertainty about the impact of anthropogenic carbon on future economic wellbeing. We use DSICE, a DSGE extension of the DICE2007 model of William Nordhaus, which incorporates beliefs about the uncertain economic impact of possible climate tipping events and uses empirically plausible parameterizations of Epstein-Zin preferences to represent attitudes towards risk. We find that the uncertainty associated with anthropogenic climate change imply carbon taxes much higher than implied by deterministic models. This analysis indicates that the absence of uncertainty in DICE2007 and similar models may result in substantial understatement of the potential benefits of policies to reduce GHG emissions.
|September 2012||Continuous-Time Methods for Integrated Assessment Models|
with Yongyang Cai, Kenneth L. Judd: w18365
Continuous time is a superior representation of both the economic and climate systems that Integrated Assessment Models (IAM) aim to study. Moreover, continuous-time representations are simple to express. Continuous-time models are usually solved by discretizing time, but the quality of a solution is significantly affected by the details of the discretization. The numerical analysis literature offers many reliable methods, and should be used because alternatives derived from "intuition" may be significantly inferior. We take the well-known DICE model as an example. DICE uses 10-year time steps. We first identify the underlying continuous-time model of DICE. Second, we present mathematical and computational methods for transforming continuous-time deterministic perfect foresight models into...