Statistical Methods for Social Science (2015, 2016, 2017)
- Bresson (2009)
- Jorda & Alonso (2017)
- Jorda et al. (2018)
- Lakner & Milanovic (2016)
- Niño-Zarazua et al. (2017)
Quantitative methods in Finance (2014, 2015, 2017, 2018)
Lectures R code
Statistical Methods in Economics and Business (2013, 2014, 2015, 2016, 2017, 2018)
This course covers the fundamental concepts of statistical inference in a practical approach using R. Broad techniques of statistical inference will be introduced and students will use this information for making solid choices when analyzing data. We will start with punctual estimation via maximum likelihood and the method of moments, using both complete and limited information. We will introduce confidence intervals construction from pivot functions, Monte Carlo simulation and bootstrap. The last bloc of contents covers goodness-of-fit analysis and model selection.
Mathematics for Economists (2015, 2016, 2017)
In this course, we introduce broad concepts of calculus and linear algebra that are important to the study of macroeconomics and microeconomics. Topics covered include derivatives of functions of one and several variables to investigate maximum and minimum values of those functions with examples and motivation inspired in economics and business problems; interpretations of the derivatives; convexity; constrained and unconstrained optimization; and financial mathematics.
Statistics I (2014, 2015)
A whirlwind tour of descriptive statistics used in economics. This course explains how they are used and misused, and how to generate descriptive statistics yourself, using Excel. More specifically, the students will learn how to compute measures of position (mean, mode, median and quantiles), dispersion (standard deviation and variance), and form (skewness and kurtosis). We will also discuss how to assess relationships between variables by introducing the concepts of correlation and regression. Finally, some concepts of probability theory are introduced. Other topics include inequality measures, categorical variables and index numbers.
Statistics II (2013, 2015, 2016, 2017, 2018)
This course introduces the theoretical foundations of probability and random variables, with special emphasis on its practical implementation on freely available statistical software. This course covers topics such as univariate and bivariate distributions with special emphasis on discrete models as binomial, geometric, hypergeometric, and Poisson; and continuous models, including the normal, the gamma, the beta and the exponential distributions. The other topics cover sums of independent random variables, law of large numbers, central limit theorem, point estimation and confidence intervals.
Multivariate Analysis (2015, 2016, 2017, 2018)
This course provides hands-on experience using R for the fundamental multivariate statistical techniques in economics, also covering some theoretical foundations of the topic. We first discuss some common statistical techniques used to visualize high-dimensional data. Next, we will describe basic concepts of multivariate distributions including, multivariate normal distribution and multivariate inference for its parameters. The course also covers concepts of principal component analysis, factor analysis, multivariate analysis of variance (MANOVA), cluster analysis, discriminant analysis and canonical correlations.