In this course, we study topics in economic dynamics,
mainly (but not fully) following the textbook given below.
We focus on mathematical aspects, but occasionally discuss computational methods as well.
The programming language used is Python.
J. P. Jarvis and D. R. Shier,
``Graph-Theoretic Analysis of Finite Markov Chains.'' |
``Strongly Connected Components,'' in timl.blog. |
W. J. Stewart,
Probability, Markov Chains, Queues, and Simulation, Princeton University Press, 2009.
[Amazon]
[Slides] |
J. Rust,
``Numerical Dynamic Programming in Economics,''
Handbook of Computational Economics, Volume 1, 619-729, 1996.
[Working paper version] |
D. P. Bertsekas,
Dynamic Programming: Deterministic and Stochastic Models, Prentice Hall, 1987. |
M. L. Puterman,
Markov Decision Processes:
Discrete Stochastic Dynamic Programming, Wiley-Interscience, 2005. |
L. Kallenberg,
``Markov Decision Processes.'' |
M. J. Miranda and P. L. Fackler,
Applied Computational Economics and Finance, MIT Press, 2002. |
J. Rust,
``Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher,'' Econometrica 55 (1987), 999-1033. |
J. Rust,
Nested Fixed Point Maximum Likelihood Algorithm. |