|Official course title||PROBABILITY THEORY|
|Degree level||Second Cycle Degree M.D.270|
|Educational sector code||SECS-S/03|
Degree Programmes and curricula
The first part of the course is intended to be an introduction to fundamentals of probability theory in order to devote the second part to nonparametric curve estimation (kernel methods). The use of these tools allows the analysis of macroeconomic as well as financial data and therefore the interpretation of a variety of economic phenomena through a set of instruments characterized by high degree of flexibility.
Introduction to probability theory
Events, probability, axioms of probability, conditional probability, independence
Distributions, expectations, moments,
Joint and marginal distributions, dependence, conditional distributions,
Nonparametric methods for data analysis
Nonparametric estimate of probability distributions (kernel density estimator)
Practical use of the kernel density estimator to study some data set
Nonparametric estimate of conditional density (stochastic kernels) for economic convergence analysis.
Recommended Reading List
Material available on the lecturer's website