Didattica integrativa
Ciclo Di Seminari
Esercitazioni
Modalità d'esame
Prova scritta e orale
Prerequisiti
Elementary probability (classical discrete and continuous distributions, basic rules). Elements of descriptive statistics (like empirical mean and standard deviation).
Programma del corso
Elements of Measure Theory. Introduction to probability measures. Random variables, probability density and distributions. Conditional probability and conditional expectation, definition and properties. Characteristic functions, moments and cumulants. Limit theorems: Laws of Large numbers, Central Limit theorem.
Introduction to information theory. Shannon entropy. Relative entropy. Mutual information. Asymptotic equipartition property. Information theory, codes, data compression and prediction. Kelly criterion. Horse races. Graphs. Random walks on graphs. Perron-Frobenius Theorem. Google's page rank algorithm.
Review of estimation methods. ARMA processes. GARCH and Stochastic Volatility models. Vector processes, VAR (reduced form, structural form and identification issues). Kalman Filter and Smoother. Generalized Autoregressive Score-driven (GAS) models.
Riferimenti bibliografici
J. Jacod and P. Protter, Probability Essentials, Ed Springer 2004
A.N. Shiryaev, Probability, Ed Springer
Cover-Thomas: Elements of Information Theory
Mackay: Information theory, Inference and Learning Algorithms
Shannon, Claude E. (July 1948). "A Mathematical Theory of Communication".
James D. Hamilton, Time Series Analysis, Princeton University Press 1994.
Durbin, James, and Siem Jan Koopman, Time series analysis by state space methods, Oxford university press, 2012.