Course description: The goal of the course is to provide a solid understanding of the probability theory and statistics required for graduate study and research in economics. The course material underpins econometric methods learned in future courses as well as data analysis in all fields. The course will cover the following topics:

1. Probability theory

2. Random variables, distributions, integration

3. Joint, marginal, and conditional distributions

4. Introductory asymptotic theory

5. Point estimation and properties of estimators

6. Distribution theory and statistical inference

Course description: This course covers the most commonly used tools in empirical research. We will consider both the classical setup with constant coefficients and more recent advances in the analysis of models with heterogeneous coefficients, with a focus on treatment effects. Topics covered include: OLS estimation/inference, robust variance estimation, potential outcomes and causality under unconfoundedness, IV and GMM, Local Average Treatment Effects, fixed and random effects models, DID, consistency and asymptotic normality of M- and Z-estimators, and non-linear models.

Course description: The first part of the course covers: introduction to dynamic programming, Arrow-Debreu equilibria, sequences of market equilibria, recursive competitive equilibria, first and second welfare theorems, real business cycles. The second part of the course covers the theory of the discrete and continuous time Job search model and its application to other context such as marriage, crime, etc. 

Python code demo for collecting and plotting data from FRED:

• Code 

Problem sets:

• Problem set 1 (Due: 01/20/2023) - Solution

• Problem set 2 (Due: 02/03/2023) - Solution

• Problem set 3 (Due: 03/01/2023) - Solution

• Problem set 4 (Due: 03/15/2023) - Solution

• Midterm solution.

Section slides:

• Week 1 (01/13/2023) 

• Week 2 (01/20/2023) 

• Week 3 (01/27/2023) 

• Week 4 (02/03/2023) - Problem solutions - Extra practice problem

• Week 5 (02/10/2023)

• Week 6 (02/17/2023)

• Week 7 (02/24/2023)

• Week 8 (03//03/2023)

• Week 9 (03/10/2023)

• Week 10 (03/17/2023)

Course description: An introduction to probability modeling and statistical inference applied to the analysis of economic data for students with basic knowledge of calculus. Topics covered include: probability, discrete and continuous random variables, probability distributions, mean, variance, correlation, sampling, parameter estimation, unbiasedness and efficiency, confidence intervals, and hypothesis testing. Computing labs with Excel.

Course description: Students develop skills in organizing economic data, learning how to summarize and display data to answer substantive economic questions. Emphasis is placed on the communication of results and coding with R.

• Led a team of four undergraduate students to code an automated grading platform for more than 250 students using R.

• Developed R coding prompts and answers for more than 20 data analysis assignments.

Course description: Estimation and hypothesis testing in classical linear regression models as well as violations of each classical assumption. Discrete dependent variable models and systems of simultaneous equations are also covered.

• Sampling random variables

• Inference and hypothesis testing  

• Linear regression 

• Binary independent variable in linear regression.