12 Lectures on Stochastic Decision Processes:
Theory, Computation, and Empirical Applications

John Rust, University of Maryland

Institute for Advanced Studies, Vienna
January 12-19th, 2004

Text: Dynamic Economics By Jerome Adda and Russell Cooper (2003) MIT Press.

Handbook Chapter: Structural Estimation of Markov Decision Processes by John Rust (1994) in R. Engle and D. McFadden Handbook of Econometrics volume 4, Elsevier, North Holland.

Practice Problems: Sample problems on dynamic programming and computation

Lectures 1-2: Theory

Derivation of Bellman's Principle of Optimality for stochastic decision processes involving maximization of expected discounted payoffs over finite and infinite horizons. Relationship between Bellman's equation and contraction fixed points in infinite horizon, stationary Markovian decision problems. Generalizations to recursive utility theory including non-time-separable and non-expected utility preferences.

Readings:

Lectures 3-6: Computation

Numerical Methods for Solving Finite and Continuous State Dynamic Programming Problems. The Curse of Dimensionality and two ways of breaking it: a) randomization and b) exploiting special structure.

Readings:

Further readings:

Lectures 7-8: Empirical Applications: Discrete Decision Processes

Discrete Decision Processes are Problems where the choice variable is restricted to a finite set of alternatives. I describe parametric econometric methods for inferring the unknown parameters of these processes, and survey some of the numerous applications of these models in many different parts of economics.

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Lectures 9-10: Empirical Applications: Continuous Decision Processes

Continuous Decision Processes are Problems where the choice variable can take on a continuum of possible values. I describe parametric econometric methods for inferring the unknown parameters of these processes based on the "Euler Equation" and via parametric maximum likelihood and simulated method of moments approaches.

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Lectures 11-12: Estimation of Dynamic Games

I extend the single agent decision framework to multi-agent dynamic games. This is much harder and is at the current frontier of research in this area. We will discuss several recent papers that make substantial headway on these topics.

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