Random Quote: Our culture is obsessed with urgency, but winning doesn’t always mean being fastest to first. The deeper reward comes from staying in the race long enough to become who you were meant to be. – Mark Robichaux

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Teaching

This page collects lecture slides for some courses I have taught.

Graduate Macroeconomics

Lecture slides from the grad macro class I taught at NYU in 2018. The objective of this half semester course was to introduce students to Markov dynamics, dynamic programming in discrete time and other foundations.

• Lecture 1 – Introduction: why recursive methods
• Lecture 2 – Dynamics and analytical preliminaries
• Lecture 3 – Contraction mappings and asset pricing
• Lecture 4 – Dynamical systems: stability and order
• Lecture 5 – Markov chains and stationary distributions
• Lecture 6 – Markov stability and the LLN
• Lecture 7 – Distribution dynamics and heavy tails
• Lecture 8 – Wealth distributions and job search
• Lecture 9 – Job search: dominance and learning
• Lecture 10 – Linear quadratic control and MDPs
• Lecture 11 – given by Thomas J. Sargent (slides not available)
• Lecture 12 – Optimal savings: theory and computation
• Lecture 13 – Euler equations and firm dynamics
• Lecture 14 – Dynamic programming: general theory

Undergraduate Mathematical Economics

Taught to second and third year undergraduates. The lectures contain a mix of optimization theory and dynamics.

• Lecture 1 – Introduction and course overview
• Lecture 2 – Computing and unconstrained optimization
• Lecture 3 – Constrained optimization: substitution and tangency
• Lecture 4 – Computers, sets and logic
• Lecture 5 – Tuples, functions, counting and cardinality
• Lecture 6 – Vector spaces, norms and span
• Lecture 7 – Linear independence and bases
• Lecture 8 – Linear maps, matrices and rank
• Lecture 9 – Linear equations and determinants
• Lecture 10 – Eigenvalues and Neumann series
• Lecture 11 – Quadratic forms and probability
• Lecture 12 – Random variables and expectations
• Lecture 13 – Densities and random vectors
• Lecture 14 – Independence and the LLN
• Lecture 15 – Central limit theorem, conditional expectation
• Lecture 16 – Real numbers, sequences and limits
• Lecture 17 – Open and closed sets, continuity
• Lecture 18 – Suprema, infima and optima
• Lecture 19 – Convexity and uniqueness of optimizers
• Lecture 20 – Fixed points and contractions
• Lecture 21 – Dynamics and graphical analysis
• Lecture 22 – Stability and complex dynamics
• Lecture 23 – Linear models and stochastic dynamics
• Lecture 24 – Asset pricing and ergodicity
• Lecture 25 – Review: optimization and existence
• Lecture 26 – Review: probability, analysis, dynamics