The course introduces the core computational tools necessary to solve models on the frontier of macroeconomics. Although the models featured in the course are macro-models, the computational methods can be readily applied to structural models in, inter alia, labor economics and finance.
Here is a tentative calendar for the course:
• Lecture 1: Deterministic Growth Model. Basics of Dynamic Programing and Value Function Iteration. Interpolation methods. One-Variable Nonlinear-Equation Solvers. Shooting Algorithms to compute transitions.
• Lecture 2: Stochastic Growth (RBC) Model. Numerical Integration. Discretization of Stochastic Processes.
• Lecture 3: Augmented RBC Model. Perturbation methods. Log-Linearization and Dynare.
• Lecture 4: Standard Incomplete Market (Aiyagari) Model. Value-Function vs. Policy-Function Iteration. The Endogenous-Grid Method. Local and Global Optimization. Calibration.
• Lecture 5: Krusell-Smith Model. Bounded-Rationality Algorithm. Approximate Aggregation.
• Lecture 6: New Approaches to solving the Krusell-Smith Model.
• Lecture 7: Student presentation of frontier papers. Example topics: Adaptive-Sparse-Grid Method, Machine Learning, Continuous-Time Computational Methods, and Parallel Computing.
Students will be provided with lecture slides which are based on selected chapters of the following textbooks and selected papers. The specific chapters and papers are referenced in the lecture slides.
• Dynamic General Equilibrium Modelling: Computational Methods and Applications, by Burkhard Heer and Alfred Maussner (Springer, 2005)
• Economic Dynamics: Theory and Computation by John Stachurski (MIT Press 2009).
• Numerical Methods in Economics by Kenneth L. Judd (MIT Press, 1998).
• Lectures organized on the website https://lectures.quantecon.org/ by Thomas J. Sargent and John Stachurski.