Economics

This course equips students with the basic facts about the Japanese economic developments and economic policies, as well as the basic economics theories required to analyze these facts.
 

The course first reviews basic concepts such as GDP, business cycle, economic growth, inflation/deflation, as well as fiscal, monetary, and structural policies. Then, the course provides an overview of the economic history of the postwar period, analyzing special features of the Japanese economy, as seen from the business cycle and economic growth aspects. The problems that economic policies at that time tried to address, and whether they were successful in dealing with the problems, will also be discussed. Finally, the course addresses the future of Japanese economy: the outdatedness of the current Japanese economic system; stagnated economic productivity; the deterioration of fiscal situation, and the negative impacts of aging and declining population. 

The course explains the major steps that have shaped the world economy to its present configuration. The topics covered include the diversity of pre-modern economies, the impact of colonialism, the birth of the modern economy in Europe, the varieties of forms of enterprise and of national approaches to the governance of the economy and the role of international crises. At the end of the course, the student has a better knowledge of the major economic challenges to be faced today.

The course content includes:

1. The pre-industrial economy and the preparation of the "great divergence" of Europe. The role of institutions.
2. The British Industrial Revolution and the process of imitation
3. The second Industrial Revolution, the rise of USA and the creation of an international economy
4. World War I and its effects
5. The first major world crisis starting in 1929 and its economic and political impact to WWII
6. The birth of a new international economic order, the golden age and the process of European economic integration
7. The third industrial revolution and the return of instability: globalization, financialization, the demise of Soviet Union and its legacy
8. New protagonists of the "great convergence": the developing world, the rise of Asia
9. A polycrisis world: the 2008 financial crisis, the Covid19 pandemic, wars.
10. The present day challenges: the fourth industrial revolution, AI, the environment. How not to destroy humanity

This course provides an introduction to the application of various forecasting techniques. It introduces quantitative methods and techniques for time series modeling, analysis, and forecasting. Emphasis is also put on the applications in economic and business related areas. Computing is an integral part of this course, therefore all students are expected to do data analysis, modeling, and forecasting with computer programming software. The focus of the course is to use past data to predict the future. The key concept is that there is an underlying process that gives rise to the data. Using statistical properties of that process, we can develop forecasts. Forecasting methodology is presented in a lecture format in the first part of each class meeting. The application, however, is the centerpiece of the presentation. In the second part of the class meeting, students work on in-class applications. The course starts start with simple models that are widely used in business and progresses to modern methods that are used by professional forecasters and economists. It studies basic components of time-series data, such as trend, seasonal, and cyclical components, as well as basic model specification techniques, such as moving average and auto regressions, used in the forecasting of time-series. All forecasting methods are illustrated with detailed real world applications designed to mimic typical forecasting situations. The course uses applications not simply to illustrate the methods but also drive home an important lesson, the limitations of forecasting, by presenting truly realistic examples in which not everything works perfectly. Examples of the applications include, but are not limited to, forecasting retail sales, hotel occupancy, fishery output, consumer loan requests, predicting expansion of fast food chain stores, modeling and forecasting macroeconomic activity indices such as Gross Domestic Product, unemployment and inflation, modeling development of a small open economy, forecasting New York Stock Exchange index and currency exchange rates and many other applications.

This course introduces fundamental concepts of econometrics and data analysis that form the basis for data driven decision making, empirical analysis of causal relationships, and forecasting. It covers matrices and their use in linear regression analysis, probability distributions and their role in carrying out valid data approximations, and estimation methods and their importance in producing credible results of any data analysis. The course also introduces programming with R, which is the main programming language of statistical computing. It starts out with basic R operations and then, with time, students learn about ways to write their own functions in R.

The course analyses accounting practices and processes from the point of view of investors. The course examines revenue recognition, tangible and intangible assets, the reporting of financial instruments, off-balance-sheet accounting, stock-based compensation, as well as, issues related to the differential approaches to measurement including historical cost and fair values. However, the exact composition of the topics may vary from year to year driven by the latest developments in financial reporting, standard-setting and related debates. The course enhances students’ understanding of contemporary issues in financial accounting. Throughout the course, taken-for-granted “wisdoms” are evaluated and challenged. 

This course covers estimating relationships between economic variables associated with agricultural situations. Students are enabled to understand the general concepts about model identification, estimation, forecasting, and policy analysis. Students learn simple regression, multiple regression, and time series analysis. Prerequisite: Principles of Economy, Statistics, Mathematics for Economic Analysis. 

The course explores the long-term socio-economic consequences of exposure to natural disasters, focusing on the level of the individual. It consists of two complementary classes that have to be taken together. The first part of the course provides students with a theoretical foundation for understanding how natural disasters can shape economic and social outcomes over time. It focuses on discussing channels and mechanisms through which the natural environment and disasters or upheaval, in particular, affect individuals. Topics covered include the impact of such disasters on health, education, household income, labor markets, and migration. Students familiarize themselves with underlying microeconomic models, discuss research methods like causal inference strategies, and analyze empirical findings from academic research. The second part of the course is designed to deepen students’ understanding of the concepts covered in class through active engagement with empirical studies. Students are required to present and critically discuss academic papers that investigate natural disaster effects using micro-level data. The seminar emphasizes methodological approaches, data sources, and empirical strategies, encouraging students to evaluate the presented research critically and develop their analytical skills.

The course teaches quantitative techniques that guide evidence-based managerial decision-making. Students examine whether the predictions of managerial, social, or economic theory are supported by empirical evidence. Particular emphasis is on (a) the many ways in which evidence is abused in the academic or managerial debate, and (b) the causality in the relationship between variables. The approach is both formal, as the course makes extensive use of econometric theorems and techniques, and solidly grounded in intuition, as it provides numerous examples of tests of real-life relations. Many of these examples are illustrated using the STATA software package, and students learn the basics of data manipulation and regression techniques.

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. 

The course content includes the following: Elements of noncooperative game theory, and solution concepts; The basics of oligopoly theory: price and quantity competition and product differentiation; Supply function competition; Mergers: private and social incentives; Cartels: implicit collusion and the theorem; Discrete choice theory: horizontal and vertical differentiation; R&D, process and product innovation and the indirect debate between Schumpeter and Arrow; Network externalities, technological standards and switching costs; Sketch of the environmental implications.

At the end of the course, the student is expected to be acquainted with: basic game theory instruments; the evolution of the theory of industrial organization, including the basic oligopoly models of Cournot, Bertrand and Stackelberg; and the manifold issues connected with the impact of firms' unregulated strategic behavior on the environment and natural resources.