Economia e finanza

Statistical Inference (6CFU) Part 0. Aspects of statistics: from design to inference. Part 1. Point estimation. Properties of estimators (unbiasedness, consistency, efficiency), and related concepts (Cramér-Rao bound, Fisher information). Methods of estimation: the likelihood function and maximum likelihood estimation (MLE), sufficiency and invariance in MLE; method of moments, OLS. Part 2. Interval Estimation. Confidence intervals for parameters and construction; Bootstrap.. Part 3. Hypothesis testing. Likelihood ratio. Neyman-Pearson lemma. From simple to composite hypotheses. Difference between two means. Part 4. Multivariate statistics. Causal Inference (3CFU) Rubin’s problem and counterfactuals Recap of OLS fundamentals Randomized-control trials Matching and subclassifcation Difference-in-Differences Synthetic control method Regression Discontinuity Design

Matematica: algebra lineare con particolare attenzione a logaritmi e potenze e loro proprietà; derivate / integrali. Probabilità / Statistica: operatori media, varianza, covarianza, correlazione e loro proprietà; concetto di variabile aleatoria; PDF, CDF, quantiles, conditional densities; distribuzioni statistiche di base; analisi di regressione. Esperienza DI BASE con il software R (verrà ripreso durante il corso).

Statistical Inference: Cicchitelli, Giuseppe, Pierpaolo D'Urso, and Marco Minozzo. Statistics: principles and methods. Pearson, 2021. Causal Inference: Cunningham, S. (2021). Causal inference: The mixtape. Yale university press. R Appendix: https://stat4ds.rwth-aachen.de/pdf/DS_R_webAppendix.pdf Additional resources. Agresti, Alan, and Maria Kateri. Foundations of statistics for data scientists: with R and Python. Chapman and Hall/CRC, 2021. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R (Vol. 103). New York: springer.

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Statistical Inference: Cicchitelli, Giuseppe, Pierpaolo D'Urso, and Marco Minozzo. Statistics: principles and methods. Pearson, 2021. Causal Inference: Cunningham, S. (2021). Causal inference: The mixtape. Yale university press. R Appendix: https://stat4ds.rwth-aachen.de/pdf/DS_R_webAppendix.pdf Additional resources. Agresti, Alan, and Maria Kateri. Foundations of statistics for data scientists: with R and Python. Chapman and Hall/CRC, 2021. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R (Vol. 103). New York: springer.

La didattica verrà svolta in modalità frontale (in presenza). Si alternerà l'uso di presentazioni, software statistico e lavagna.