Teaching

Long Run Macroeconomics, Econ 110A, Undergraduate (Growth)

I was the Instructor of UCSD’s Summer Session II First Sequence of Intermediate Macroeconomics. The course covers Long Run Macroeconomics (Growth) and follows Chad Jones’ Macroeconomics textbook. My teaching material was somewhat based on Giacomo Rondina’s slides, who was the supervising faculty for this course.

Syllabus, UCSD Economics, SP24

1. Intro & the Neoclassical Growth Model
2. Measuring the Economy
3. Long Run Growth: Facts
4. A Model of Production (i)
5. A Model of Production (ii): Experiments
6. The Solow Model: Analysis
7. The Solow Model: Experiments
8. How do Firms Make Investment Decisions?
9. Midterm Review
10. The Economics of Ideas and Increasing Returns
11. The Romer Model: Ideas and Labor
12. The Combined Romer and Solow Model: Ideas, Labor and Capital
13. A Model of a Competitive Labor Markets
14. A Model of a Monopsonistic Labor Markets; and an Introduction to Human Capital
15. The Lucas Model and Facts About Human Capital
16. Money and Prices
17. Permanent Income Hypothesis, The Marginal Propensity to Consume, Borrowing Constraints
18. Review Ahead of Final

Graduate Macroeconomics, Econ 210A, TA Session Discussion Notes (Growth)

I was the Teaching Assistant of UCSD’s PhD First Year macro sequence for three years. Below I publish the notes I have used for the discussion session with first years. They tend to be very detailed in algebraic derivations, since the objective is to make them easily comprehensible by students who are facing this material for the first time.

1. Mathematical Foundations for Understanding Value Function Iteration
2. Analytical and Numerical Solutions to VFI
3. Inefficient Equilibria - Economy with Externalities
4. Describing and Characterizing Competitive Equilibria
5. Neoclassical Growth with Endogenous Labor Supply
6. Endogenous Growth with Human Capital Externalities
7. The Romer Model
8. Government Consumption and Distortionary Taxation
9. Introduction to Heterogeneous Agents – the Aiyagari Model

• Extra: How to Derive optimal demand under monopolistic competition/C.E.S. Dixit-Stiglitz Aggregator Preferences.

Introduction to Statistics and Probability with Python (in Portuguese)

I taught “Introduction to Stats and Probability with Python” at Institute for Higher Education of Brasília (IESB), a private university in Brasília, Brazil. This class was part of a graduate degree in Data Science and was meant to cover basic principles of statistics and statistical programming.

1. Basic Concepts of Statistics and Statistical Programming
2. Data Types (Time Series, Panel Data, Cross-Section); Descriptive Statistics; Introduction to pandas.
3. Intro do Data Visualization; Intro to matplotlib and seaborn
4. Mean, Median, Mode, Quantiles and Standard Deviation
5. No slides (review before midterm)
6. Functions, Probability, Density Functions, Z-scores
7. Central limit theorem, standard errors and confidence intervals
8. Hypothesis testing, t-statistics, p-value
9. Intro to regression analysis; intro to statsmodels