Economics

This course examines fundamental principles of financial accounting for the purposes of external reporting. The course starts with a discussion of the framework of financial accounting: its nature, intents, and purposes, and the context and environment in which it operates. This includes, and eventually entails, the need for, and various sources of, accounting regulation and accounting standards. The course unpacks various core financial accounting concepts and conventions, but the course also looks into the processes used to record, summarize, and present financial accounting information as well as, crucially, its interpretation. This course focuses on the preparation, interpretation, and limitations of company financial statements for external reporting, and the regulatory framework in which financial reports are prepared.

This course develops tools for analyzing the effect of social networks, the way that culture constrains and enables economic outcomes, and the effect that systems of power have the reproduction of economic inequalities. These tools are the foundation on which economic sociology seeks to explain, criticize, influence, and predict economic action. The first part of the course establishes three primary intellectual camps or “theories” of economic action: power, culture, and rational action. The second part of the course applies these theoretical approaches to address a series of contemporary economic questions and concerns.

This course focuses on the real transactions across borders (i.e., those transactions that involve a physical movement of goods or a tangible commitment of economic resources), such as the pattern of trade, gains from trade, and trade volume.

This is a graduate level course that is part of the Laurea Magistrale program. The course is intended for advanced level students only. Enrollment is by consent of the instructor. The course focuses on the main tools used by economists and statisticians in machine learning and statistical learning to analyze large/huge data sets coming from several domains. The course highlights how to apply key aspects of machine and statistical learning, such as out-of-sample cross-validation, regularization, and scalability. Special attention is placed on the concepts of supervised and unsupervised learning, classification, regression, and clustering analysis as well as the detection of association rules. The course also focuses on the main learning tools such as lasso and ridge regression, regression trees, boosting, bagging and random forests, principal components, mixture models and the k-means algorithm. The course places emphasis on the application of the techniques discussed using dedicated open-source software packages on training datasets. Course topics: introduction and overview of statistical learning; linear regression as a prediction tool; binary and multinomial classification: logistic regression, linear discriminant analysis and k-nearest neighbors; resampling methods: cross-validation and the bootstrap; linear model selection and regularization: ridge regression, the lasso, and principal components; moving beyond linearity: regression splines, smoothing splines and general additive models; tree-based methods: CART, bagging, boosting, and random forests; support vector machines and neural networks; unsupervised learning: hierarchical and k-means clustering. The relevant theory will be applied to each topic and subsequently the analysis will move to its empirical application in the R language. Special emphasis is placed on the economic interpretation of the results. The course focuses in several empirical analyses and replicates the results of a few case studies using the statistical software R and several of its packages.

The course uses all the skills that students have developed as economists to try and answer important economic questions. Providing an answer is hard because solving the problem of world poverty is not as simple as reallocating income. The course uses rigorous impact evaluation to find out whether the intervention implied by theory works.