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Course parts

Part 1

Preliminaries

Aggregation benchmarks, distributions, and the Markov-process foundation of the course.

3 sections Part 2

Dynamic Programming

Bellman equations, recursive methods, and competitive equilibrium in dynamic settings.

3 sections Part 3

Heterogeneous Agent Models

Precautionary savings, incomplete markets, production economies, and aggregate risk.

4 sections Part 4

Firm Heterogeneity

Span of control, monopolistic competition, variable markups, and misallocation.

5 sections Part 5

Input-Output Networks and Economic Activity

Domar aggregation, Hulten's theorem, propagation, and distortions in networks.

6 sections Part 6

Random Variables and Probability

Measure theory, measurable functions, and integration tools for macro theory.

4 sections Part 7

Stochastic Calculus

Ito processes, dynamic programming in continuous time, and applications.

6 sections