Econometrics II

Theory and Economic application f the linear multiple regression model, identification and structural estimation in simultaneous models, analyzing of economic policy and forecasting.

This econometrics course is designed to provide students with the essential skills and knowledge necessary for conducting rigorous research in economics and finance. By delving into advanced econometric practices and models, students will gain the expertise needed to analyze complex economic phenomena using empirical data. Through the utilization of the STATA and R econometric packages, students will engage in hands-on learning experiences, mastering techniques for data manipulation, estimation, and interpretation of results. Moreover, laboratory sessions will offer students practical opportunities to apply theoretical concepts to real-world datasets, fostering a deeper understanding of econometric methodologies. By the course's conclusion, students will emerge equipped with the ability to conduct independent research, critically evaluate empirical findings, and contribute meaningfully to the advancement of economic and financial knowledge.

1. Binary Dependent Variable: A variable that takes on only two possible outcomes (e.g., yes/no, employed/unemployed).
2. Proxy Variables and Measurement Error: Proxy variables substitute for unobserved or imperfectly measured variables, while measurement error refers to inaccuracies in data that can bias estimates.
3. Instrumental Variables (IV): Variables used to obtain consistent parameter estimates when explanatory variables are endogenous, often due to measurement error or omitted factors.
4. Panel Data: Data that follows the same subjects over multiple time periods, allowing for analysis of both cross-sectional and time-series variations.
5. Difference-in-Differences (DiD): A quasi-experimental design that compares changes in outcomes over time between treatment and control groups to estimate causal effects.
6. Generalized Method of Moments (GMM): An estimation technique that uses moment conditions derived from theoretical models to efficiently and consistently estimate parameters.
7. Simultaneous Equations: A system where multiple interdependent relationships are modeled together, requiring specialized methods to address the mutual determination of variables.
8. Limited Dependent Variable Models: Models designed for outcomes that are restricted in range (such as binary or censored variables), often implemented using techniques like Logit or Probit.
9. Multinomial Variable Models: Models used when the dependent variable has more than two unordered categories, such as choices among several alternatives.
10. Regression Discontinuity Design (RDD): A quasi-experimental approach that identifies causal effects by exploiting a cutoff or threshold in the assignment of treatment.

1. Develop proficiency in advanced econometric techniques and methodologies, spanning topics such as binary dependent variables, measurement error, instrumental variables, panel data analysis, differencein-differences methods, simultaneous equations, limited dependent variable models, multinomial variable models, regression discontinuity design, and resampling methods.
2. Cultivate the ability to critically analyze econometric models and methodologies, identify potential sources of bias or error, and implement appropriate strategies to address them.
3. Gain hands-on experience with econometric software and tools, such as STATA and R, in conducting empirical analysis, interpreting results, and drawing meaningful conclusions from economic and financial data.
4. Develop the skills necessary to design and execute empirical research studies in economics and related fields, including formulating research questions, selecting appropriate econometric methods, collecting and analyzing data, and communicating findings effectively.
5. Deepen understanding of causal inference methods, including quasi-experimental designs and identification strategies, to estimate causal effects in observational data settings and mitigate biases inherent in econometric analysis.
6. Recognize the interdisciplinary nature of econometrics and its applications across various domains, including economics, finance, public policy, and social sciences, and appreciate the broader implications of econometric analysis for decision-making and policy formulation.

Wooldridge J.M.(2015). Introduction to Econometrics. Cengage Learning Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: methods and applications. Cambridge university press. Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton university press. Cunningham, S. (2021). Causal inference: The mixtape. Yale university press. Gaillac, C., & L’Hour, J. (2019). Machine learning for econometrics, lecture notes ensae paris.