**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.

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.

Tutorial 1, solutions

Tutorial 2

Tutorial 3

Scraps of code

Value function iteration (discrete state approximation)

Collocation example