Prof. Alberto Lanconelli

Argomenti del programma: Spazi di probabilità e assiomi di Kolmogorov Probabilità condizionata, Legge delle Probabilità Totali e formula di Bayes Eventi indipendenti Variabili aleatorie e funzioni di ripartizione, variabili aleatorie discrete e continue, valore atteso, varianza e covarianza Modelli discreti: Bernoulli, Binomiale, Ipergemometrica, Poisson, Geometrica Modelli continui: Uniforme, Gaussiana, Gamma, Student, Fisher Legge dei…

Argomenti del programma: Vettori aleatori, valore atteso e matrice di covarianza Vettori aleatori Gaussiani, Multinomiali e di Dirichlet Valore atteso condizionato Introduzione ai processi stocastici Martingale Catene di Markov Processi stazionari del secondo ordine Limiti per successioni di variabili aleatorie: lemma di Borel-Cantelli, convergenza quasi certa, convergenza in probabilità, convergenza in media quadratica Convergenza in…

Argomenti del programma: Probability spaces and Kolmogorov axioms Conditional probability, Law of Total Probability and Bayes' formula Independent events Random variables and distribution functions, discrete and continuous random variables, expected value, variance and covariance Discrete models: Bernoulli, Binomial, Hypergeometric, Poisson, Geometric Continuous models: Uniform, Gaussian, Gamma, Student, Fisher.

• Lecture notes will be made available throughout the course, accompanied by additional materials, data sets, and a list of exercises. These resources will be posted on Virtuale and will support the understanding of course content and…
• Lecture notes made available at the start of the course together with exercises with solutions delivered at the end of each week of lectures. This material will be posted on Virtuale.

Argomenti del programma: The course aims to provide foundational concepts in econometric methodology and analysis, which form the essential framework for the empirical analysis of financial markets and their stylized facts. The content is structured as follows: 1. Moment-conditions based estimation. Least squares and quasi maximum likelihood 2. Large-sample OLS-based inference in linear models with stochastic regressors 3.