Teaching

Econometrics (Spanish). 

This course introduces students to empirical methods used in modern applied research, with an emphasis on causal inference and the econometric foundations behind regression analysis.

Key topics include:

• Probability review, estimators & asymptotics

• Simple & multiple regression

• OLS derivation and properties (Gauss-Markov)

• Functional forms, binary regressors & interaction terms

• Hypothesis testing & prediction

• Heteroskedasticity & robust inference

• Introduction to endogeneity

Students work with real policy datasets and implement all methods in Stata and Matlab, building analytical and coding skills for empirical research.

• Probem sets:

  • Problem Set 1: Bias–variance, hypothesis testing, confidence intervals, basic inference. [PDF]

  • Problem Set 2: Simple regression, OLS derivation, interpretation, model fit, prediction. [PDF]

  • Problem Set 3: Multiple regression, omitted variable bias, functional forms, elasticity, scaling of variables. [PDF]

  • Problem Set 4: Heteroskedasticity, autocorrelation, robust inference, GLS intuition. [PDF]

• Codes:

  • Comparing Unemployment Across Regions (Matlab): This hands-on example uses an Excel dataset to test whether unemployment rates differ between two Chilean regions. Students load data, explore distributions, compute descriptive statistics, and run a t-test. The script automatically saves figures and summary tables. [Code] [Data]

  • Simple OLS — crime & drugs (Matlab): This hands-on example walks students through computing simple linear regression “by hand” using real data on drug-related deaths and homicide rates. They load the dataset, calculate OLS coefficients step-by-step, generate predictions and residuals, plot the fitted line, and run a two-sided t-test for the slope. The script automatically saves the figure and a summary file. [Code][Data]

  • Multiple Regression with Dummies (Matlab): This applied example estimates a multiple regression model using matrix algebra and real income data. Students work with continuous and dummy variables (age, education, race/ethnicity, marital status, university completion). The script performs matrix-based OLS estimation, computes standard goodness-of-fit measures, conducts individual and joint significance tests (t-test and F-test), and analyzes residuals graphically. Outputs include diagnostic plots and summary tables.[Code][Data]

  • Central Limit Theorem — Repeated Sampling (Stata). Students repeatedly sample from an income variable to build the sampling distribution of the mean at n=20, 100, 500. The script generates histograms with a normal overlay and Q–Q plots, illustrating how the distribution of sample means becomes approximately normal as n grows. It works with your dataset (if present) or with synthetic data.[Code][Data]

  • CASEN (Stata). Step-by-step lab using CASEN 2015 to build an entrepreneurship indicator, create income quintiles, explore descriptive stats, visualize distributions, run t-tests, and estimate wage models with interactions and diagnostics.[Code][Data]

Macroeconomics I (Spanish).

This course provides a rigorous and intuitive introduction to macroeconomic analysis. We study how economies grow in the long run, fluctuate over the business cycle, and respond to monetary and fiscal policy. The emphasis is on understanding key mechanisms, interpreting economic data, and connecting theory to real-world applications and policy debates.

Key topics include:

• What macroeconomics studies: growth vs. business cycles

• Measuring the economy: national accounts, inflation, unemployment & external sector

• Flexible–price macroeconomics: aggregate supply & demand, money, labor markets

• Short–run fluctuations: Keynesian model, rigidities, IS–LM framework

• Economic growth fundamentals: Solow model, productivity, technology & human capital

Students will work with real macroeconomic data (Banco Central de Chile, World Bank, IMF, FRED), develop economic intuition through theory, and simulate key models such as the Solow model and the IS–LM framework.

• Problem sets:

  • Problem Set 1: National accounts, inflation & price indices, labor market measurement, neoclassical model with flexible prices. [PDF]

  • Problem Set 2:  Unemployment dynamics, search & matching model, Beveridge curve, money demand & inflation. [PDF]

  • Problem Set 3: Classical dichotomy, monetary neutrality, long- vs short-run policy, Mundell–Fleming in open economies, fixed vs flexible exchange rates, PPP, Solow growth and convergence, crowding-out, and fiscal & monetary transmission. [PDF]

• Codes:

  • Consumption & GDP (Stata): Students import quarterly data (GDP, total consumption, policy rate), construct a quarterly date, apply an HP(1600) filter, and analyze levels, trends, and cycles. They run long/short-sample regressions, include monetary policy and seasonal dummies, and compare results in levels vs growth rates. [Code][Data]

  • HDI-style Index (Stata). Students fetch life expectancy, PPP GDP per capita, and expected schooling from the World Bank, normalize each dimension, and build an HDI-style composite index. They explore time trends for a specific country, and compare averages by income group and region.[Code]

  • World Inequality Database — Income Levels & Bottom 50% (Stata). Students fetch WID data on average net national income and on the bottom 50% of the distribution, convert series to 2017 PPP USD, and compare France, the U.S., China, and Chile on a log scale. Clean labeling and reproducible workflow make the policy discussion more concrete. [Code]

  • Investment & GDP (Stata). Students import quarterly data (GDP, investment, government spending, trade, consumption, FX), build a quarterly index, apply an HP(1600) filter, and compute growth rates. A simple recession rule is used to shade downturns. The lab visualizes levels, trends, and cycles, compares investment decomposition, and explores correlations (including the stock market index when available). [Code][Data]

  • Pissarides Model (Matlab). This module explores how unemployment, wages, and vacancy creation respond to changes in key labor-market fundamentals such as productivity, job-separation rates, unemployment benefits, and hiring costs. It includes a core function implementing the Pissarides matching framework and a demo script with simulations, comparative statics, and transition dynamics. The code is structured for teaching and student replication, with clear figures and commented steps for experimentation. [Function][Main]

  • Chile Regional Heatmap (Python). Students load a regional shapefile, map a dictionary of region-level values (e.g., GDP share) to region names, and produce a choropleth with a colorbar. The lab normalizes names (lowercase/trim), highlights missing matches, and exports a high-resolution PNG to a chosen folder; students can customize the region column and colormap. [Code][Shapefile]

  • BCCh API (Python). Students authenticate via environment variables (BCCH_EMAIL, BCCH_KEY), choose series from the provided spreadsheet, and fetch monthly/quarterly time series through bcchapi. The script returns a clean long-format table (Fecha, Variable, Valor) and saves a UTF-8 CSV. Frequency and aggregation are configurable. [Code][Series]

Macroeconomics II (Spanish). 

This course covers open-economy macroeconomics, consumption theory, macroeconomic indicators, and the foundations of long-run growth. We bridge theoretical models with real-world evidence, with special emphasis on Chile and emerging markets. Students develop analytical intuition and data skills to interpret macroeconomic policy debates and global economic conditions.

Key topics include:

• Open-economy macroeconomics: flexible vs. sticky prices, nominal and real exchange rates, purchasing power parity (PPP), and the Mundell–Fleming model under fixed and floating regimes.

• Consumption theory: Keynes-Hicks framework, permanent and transitory income, intertemporal models, life-cycle hypothesis, and risk aversion.

• Applied macroeconomic analysis: interpreting GDP, IMACEC, unemployment, inflation, interest rates, and exchange rate movements.

• Economic growth: stylized facts, conditional convergence, Solow model, human capital, endogenous growth, and the role of institutions.

Students will use real macroeconomic data from the Banco Central de Chile, the World Bank, and the IMF to test theories empirically, estimate consumption and growth models, and simulate open-economy dynamics

• Problem sets:

  • Problem Set 1: National accounts, open-economy long-run model, saving–investment balance, real exchange rate, PPP & exchange rate dynamics, Mundell–Fleming small open economy, and international capital flows. [PDF]

  • Problem Set 2: Intertemporal consumption, Permanent Income & Life-Cycle theories, liquidity constraints, Euler equation, intertemporal elasticity of substitution. [PDF]

  • Problem Set 3: Macroeconomic indicators, business-cycle interpretation, inflation dynamics & monetary policy response [PDF]

  • Problem Set 4: Human capital & growth, Solow model extensions, golden rule capital, institutional change & development. [PDF]

Quantitative Methods in Public Policy II (Spanish). 

This course provides a rigorous and applied introduction to modern empirical tools for analyzing public policies using micro-data. The focus is on models for categorical and limited dependent variables, longitudinal data, and methods commonly used in contemporary applied research in the social sciences.

Students will deepen their understanding of econometric theory while developing hands-on skills to implement models in statistical software. Each class blends conceptual discussion, mathematical intuition, and practical exercises using real-world datasets from diverse policy areas.

Key topics include:

• Review of linear regression foundations

• Binary outcome models: Linear Probability Model, Logit, Probit

• Conditional and nested logit models

• Multinomial and ordered choice models

• Count data models

• Dimensionality reduction techniques

• Limited dependent variable models (Tobit, etc.)

• Introduction to survival analysis

• Quantile regression

• Introduction to panel data and multilevel structures

Students will complete empirical tasks using real datasets, present methodological solutions, and produce a final research paper applying course methods to their own policy-relevant research question. By the end of the course, students will be equipped to independently design, implement, and interpret advanced quantitative analyses for public policy research.

• Probem sets:

  • Problem Set 1:Binary outcomes, linear probability model, probit & logit, marginal effects, hypothesis testing, clustered SEs . [PDF]

  • Problem Set 2. Building socioeconomic status indices (PCA), health-care utilization analysis, OLS vs. Poisson vs. negative binomial models, and quantile regression using CASEN 2022 microdata. Students clean, structure, and analyze national survey data, implement multiple regression frameworks, compare model assumptions, and interpret results for public-policy design.  [PDF]

Quantitative Methods in Public Policy III (Spanish).

This course provides a rigorous and applied introduction to modern causal inference for public policy evaluation. We study the main identification strategies used in contemporary empirical research, with a focus on quasi-experimental designs and micro-data applications. The course bridges theory, methodological intuition, and hands-on implementation.

Key Topics:

• Potential outcomes, counterfactuals & causal diagrams

• Randomized controlled trials and experimental design

• Quasi-experimental methods:

  • Difference-in-differences

  • Regression discontinuity

  • Instrumental variables

  • Matching methods

  • Interrupted time series

• Panel data tools for causal inference

• Design, estimation, and interpretation challenges

• Internal vs. external validity & policy relevance