Macroeconomics
PhD core, University of Melbourne, 2019
This is an advanced course in macroeconomic theory intended for first-year PhD students.
The first part covers dynamic programming theory and applications in both deterministic and stochastic environments and develops tools for solving such models on a computer using Matlab (or your preferred language). The second part covers various extensions and further applications, including consumption-savings problems, job search, asset pricing, and models with heterogeneous households and firms.
Lecture 1 Introduction
and course overview. Intertemporal choice in discrete time.
Lecture 2 Review of
neoclassical growth model in discrete time.
Dynamic programming methods
Lecture 3 Introduction to
deterministic dynamic programming.
Lecture 4 Mathematical
background for dynamic programming. Contraction mappings etc.
Lecture 5 Principal of
optimality. Properties of the value function.
Lecture 6 Introduction to
numerical dynamic programming. Discrete state approximation.
Lecture 7 Collocation
methods for solving dynamic programming problems.
Lecture 8 Introduction to
stochastic dynamic programming. Markov chains, etc.
Lecture 9 Solving
stochastic dynamic programming problems.
Dynamic programming applications
Lecture 10
Consumption-based asset pricing. Contingent claims.
Lecture 11 Job search and
matching. Applications to labor markets.
Lecture 12
Consumption-savings problems. Precautionary savings.
Complete markets general equilibrium
Lecture 13 Arrow-Debreu
equilibrium. Pareto problems. Implications for risk sharing.
Lecture 14 Radner
equilibrium. Additional implications when state is Markov.
Incomplete markets
Lecture 15 Introduction to
incomplete markets. Huggett model with idiosyncratic but no aggregate
risk.
Lecture 16 Solving the
Huggett model. Tauchen-Hussey method for approximating continuous state
processes.
Lecture 17 Aiyagari model.
Idiosyncratic risk in neoclassical growth model.
Lecture 18 Krusell-Smith
model. Aggregate risk. Time-varying wealth distribution. Approximate
aggregation.
Firm dynamics
Lecture 19 Hopenhayn
model. Entry, exit and firm size distribution.
Lecture 20
Hopenhayn-Rogerson. Non-convex adjustment costs. Misallocation
See here for more lectures on firm dynamics
Tutorials
Tutorial 1, solutions
Tutorial 2, solutions
Tutorial 3, solutions
Tutorial 4, solutions
Tutorial 5, solutions
Tutorial 6, solutions
Tutorial 7, solutions
Tutorial 8, solutions
Tutorial 9, solutions
Tutorial 10, solutions
Problem sets
Problem set 1, solutions, code
Problem set 2, solutions, code
Problem set 3, solutions, code
Problem set 4
Scraps of code
Optimal growth
model (value function iteration)
Optimal growth model
(collocation)
Stochastic growth model
(collocation)
Huggett model (value function
iteration)
Tauchen-Hussey
example