Working Paper: NBER ID: w19034
Authors: Yongyang Cai; Kenneth L. Judd; Thomas S. Lontzek; Valentina Michelangeli; Chelin Su
Abstract: 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.
Keywords: Nonlinear programming; Dynamic programming; Economic analysis; Approximation theory
JEL Codes: C61; C63
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| dpnlp method (C45) | efficiency of solving infinite horizon DP problems (C61) |
| dpnlp method (C45) | accuracy in approximating decision rules (C52) |
| dpnlp method (C45) | avoidance of discretization inefficiencies (C69) |
| dpnlp method (C45) | solving problems with continuous state variables (C61) |
| dpnlp method (C45) | solving problems with several continuous control variables (C61) |
| dpnlp method (C45) | performance in optimal accumulation problems (C61) |
| dpnlp method (C45) | solving deterministic DP problems (C61) |
| dpnlp method (C45) | solving stochastic DP problems (C61) |