This course covers practical numerical techniques to solve single-agent and industry-level quantitative dynamic models. We will cover important topics in both discrete time and continuous time setups. We will touch upon approximate dynamic programming methods in order to overcome the “curse of dimensionality” problems in realistic applications.

The applications of these techniques center around three broad areas: (1) Solving a dynamic discrete choice model: we look at individual learning, firm innovation, and entry/exit. (2) Solving a dynamic continuous choice model: we look at individual consumption/saving and firm investment/borrowing. (3) Solving a basic continuous time dynamic programming problem: consumer search and product innovation. (4) The application to industry evolution bridges models of competitive industry dynamics w/ models where granular firms are important for aggregate outcomes

Textbook: Applied Computational Economics and Finance, Miranda and Fackler (Chapters 1-6)  
                 Numerical Methods in Economics, Judd (optional, good reference)
                 Various papers

Homework: This is a class of learning-by-doing.  We will cover key concepts and some sample codes in class, but you are also expected to work on the 5-6 problem sets that require you to solve various numerical problems.

Lecture 1: Basics of Solving a Linear/Nonlinear Equation (Lecture 1)

Homework 1 (due 10/27) (Homework 1)

Lecture 2: Optimization and Numerical Integral (Lecture 2)
In case you are interested in more recent development (http://www.cs.cmu.edu/~aarti/Class/10701_Spring14/slides/MCMC.pdf)

Lecture 3: Dynamic Discrete Choice Model: Setup and Computation Basics  (Lecture 3)

Homework 2 (due 11/3) (Homework 2) (Data Homework 2)

Lecture 4: Going over the application of Rust (1997) -- Aw et al (2011)  (Lecture 4 code)

Homework 3 (due 11/10) (Homework 3)  

Lecture 5: Keane and Wolpin (1994) with application in Crawford and Shum (2005) (Lecture 5)

Lecture 6: Functional Approximation (Lecture 6 (revising))

Lecture 7: Dynamic Continuous Choice Model: Collocation Method (Lecture 7) (Lecture 7 code)

Homework 4 (due 11/29) (Homework 4)  

Lecture 8: Basics of Continuous Time DP (Lecture 8)

Lecture 9: Applications of Continuous Time DP (Lecture 9)

Lecture 10: Finite Difference Method for Continuous Time DP -- (Going over the basics of http://www.princeton.edu/~moll/HACTproject.htm)