Economics 690: Computational Economics

Courses

This course is directed at Krannert's economics Ph.D. students, aiming to give them the tools they need to combine serious theory with hard data. Note: The course was altered mid-stream to give some necessary background on certain topics, and the current syllabus does not reflect lecture order.

Course Materials

• Syllabus
• Lecture 1 - Introduction (slides)
• Lecture 2 - Matlab Intro
• Cake-eating code
• Growing cake-eating code
• Helen of Troy code
• Lecture 3 - Bellman Theory (slides)
• Lecture 4 - Value Function Iteration (slides)
• Lecture 5 - Newton's Method (slides)
• Lecture 6 - Newton's Method: Applied (slides)
• Dixit-Stiglitz Lite
• Linear regression
• Lecture 7 - Other Methods (slides)
• Nelder Mead Simplex
• Pattern Search
• Conjugate Gradient
• Differential Evolution
• Genetic Algorithm
• Simulated Annealing
• Lecture 8 - Interpolation (slides)
• Chebyshev Interpolation in 1-D
• Chebyshev Interpolation in 2-D
• Lecture 9 - Quadrature (slides)
• chebfull
• Lecture 10 - Sargent & Ljungqvist: The European Unemployment Dilemma (slides)
• Replication Code (note: slightly different)
• Lecture 11 - Neoclassical Growth Model (slides)
• Lecture 12 - Computational General Equilibrium Models (slides)
• Mulligan Gallen (2013)
• Lecture 13 - Generalized Method of Moments/Method of Simulated Moments (slides)
• Lecture 14 - Simulation-Assisted Estimation (slides)
• Discrete choice model (minimum distance)
• Data_Pref
• Main_Pref
• Estimation_Pref
• General equilibrum (minimum distance, market clearing)
• Data_Firm
• Main_Firm
• Estimation_Firm
• Lecture 15 - Dynamic Discrete Choice Estimation (slides)
• Lecture 16 - Markov-Chain Monte Carlo (slides)
• Gibbs
• Metropolis-Hastings
• Lecture 17 - Likelihoods, Hidden States, and the Kalman Filter (slides)
• Markov Chain Probability Estimation
• Hidden State: Regime Filtering
• Kalman Filter
• Lecture 18 - Heterogeneous Agent Models (Krusell-Smith) (slides)