**Macroeconomics**(PhD core), 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

*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