The course provides an in-depth understanding of the principles and methods of statistical inference, aimed at drawing valid conclusions about a population from sample data. The first part of the course is dedicated to point estimation, examining the properties of estimators (unbiasedness, consistency, efficiency) and the main estimation methods (maximum likelihood, method of moments, ordinary least squares). The second part covers interval estimation and the construction of confidence intervals, also introducing the Bootstrap method. The third part delves into the foundations of hypothesis testing, including the likelihood ratio test and the Neyman-Pearson lemma. A specialized module is dedicated to Causal Inference, presenting Rubin's potential outcomes model and the main methods for identifying causal relationships (randomized controlled trials, matching, difference-in-differences, regression discontinuity design). Upon completion, the student will be able to select, apply, and interpret the most appropriate inferential and causal methods for the analysis of complex economic and social problems, critically evaluating their limitations and underlying assumptions.
- Teacher: NINA DELIU