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Выбор дисциплин в 2017-2018 учебном году (2 курс)

 

Академический трек (4 из 4)
Название дисциплиныОписание дисциплиныПреподаватель
Международная торговля (преподается на английском языке) The main purpose of the International trade course for students is to study the evolution of trade ideas. First, we discuss stylized facts, importance of international trade, and forces that drive international trade. Second, we study the classical theory of international trade based on technological differences or comparative advantages, including Ricardo and Heckscher-Ohlin models. The theory explains inter-industry trade. Third, starting from the growing tendency of intra-industry trade, we discuss why inter-industry trade is profitable for firms (increasing returns to scale at the firm level) and enjoyable for consumers (love for variety). We focus on the baseline monopolistic competition model and its applications in international trade in detail. The next step is to discuss modern interpretations of classical trade models and consequences of trade under combining classical and new trade theory. Therefore, we are going to discuss the “new new” trade theory where firms are heterogeneous. The main focus of this part of the course is on firm-level changes under trade liberalization. To conclude, new effects will be discovered in the new trade models by allowing variable markups. Кичко С.И.
Прикладная теория отраслевых рынков (преподается на английском языке) The course covers the standard array of topics studied in industrial organization at the graduate level. The purpose of the course is that students get the idea of how imperfect markets operate, how such markets are modeled and what kind of policy issues emerge in this regard. In order to implement this, the key concepts of the discipline (market power, product differentiation, strategic behavior) will be discussed. The students will also learn how to apply these concepts to better understand the problems industrial economists study: determination of price and quality patterns, the sources and measurement of monopoly power, competition policy issues, R&D behavior of firms, etc. Finally, the course will explain how the basic models of industrial organization are developed and put to data in order to work out recommendations for competition policy.

The course appeals to the economic intuition rather than formal models. However, it requires from the students some knowledge in microeconomics. Knowledge of basic calculus and basic optimization is also strongly appreciated, though not absolutely necessary.

i-font-family:Calibri;mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin;color:black; mso-ansi-language:EN-US;mso-fareast-language:EN-GB'>algorithms, ant algorithms, immune algorithms), the forecasting methods, the time series analysis, the econometrics.
 Ущев Ф.А.
Исследование операций (преподается на английском языке)

The base of Operations Research course is the developing and applying the optimal solution methods with mathematical modelling and different heuristic approaches. The major object of this course is to generate the system approach for magister to solve the economical tasks or problems. At the same time the course has application function. Using different models and economical and math methods the students could practise all such tools to solve complex economical problems, estimate the efficiency of applied methods and use all these skills in IT or finance companies.

The course is based on mathematical models such as linear programming, traveling salesman problem, integer programming, algorithms on the graphs (for searching the routing chains in transport scheduling), meta-heuristic algorithms (genetic algorithms, ant algorithms, immune algorithms), the forecasting methods, the time series analysis, the econometrics.

The task solution of acyclic directed graphs arrangement is based on the linear programming, integer programming, the fundamentals of graphs theory, the graphs algorithms (depth-first search, breadth first search), the sorting algorithms, heuristic approaches.

The program tools are Microsoft Excel, Wolfram Mathematica.
 
Дизайн механизмов (преподается на английском языке) Mechanism design is a science of how to construct economic mechanisms (rules, environments, institutions) with desirable properties. While the usual microeconomic approach aims at understanding how agents behave in certain environments given certain rules, Mechanism design aims at finding "good" rules, that lead to desirable outcomes. At the same time the rules themselves have to be simple and non-manipulable, i.e. provide incentives to participate sincerely.

Mechanism design uses game theory tools and can be considered as its most applied part. The range of applications is very broad: from auctions and internet marketplaces to admission of young students to colleges, voting mechanisms, online dating services, and many others.

The course will provide an overview of general methods used to design mechanisms in different areas of life.
Mechanism Design

Нестеров А.С.

Сандомирский Ф.А.


Прикладной трек (4 из 7)

Финансовая эконометрика (преподается на английском языке)The course "Financial econometrics" is one of the advanced courses of financial economics track of the Bch in Ec educational programme. The focus of the course is the application of econometric tools and methods to financial markets data. The financial econometrics tools are then to be used by the graduates working in the finance industry or researching the finance sector, for example, in support of portfolio management and in the valuation of assets.

The main topics of the course are:

1) analysis of high-frequency price observations,

2) arbitrage pricing theory,

3) asset price dynamics modeling,

4) nonlinear financial models (ARCH class, nonlinear AR models etc.),

5) realized variance modeling and analysis,

6) markets effecience and tests of the random walk hypothesis,

7) the capital asset pricing model,

8) the term structure of interest rates.

Муравьев А.А.
Прикладная эконометрика панельных и пространственных данных (преподается на английском языке)

The course considers the topics related to the choice of specification and estimation of the regressions using panel and/or qualitative data. The first part is dedicated to the panel data. This includes fixed effects and random effects models, first differences, dynamic panel regressions, the issues of efficient estimation when dealing with heteroscedasticity and autocorrelation, consistent estimation when simultaneously using time-varying and time-constant regressors. The second part is devoted to the qualitative data. This part includes general discussion of maximum likelihood estimation, probit and logit estimators, censured sample models, as well as panel data probit and logit estimators.

One of the features of the course is the combination of the econometric theory with its applied use. Every topic includes the discussion of the specification choice and detailed description of the respective estimators. The statistical features of the estimators are discussed with the use of simulated data and specific examples with the use of real data.

The purpose of the course is twofold. One the one hand, this should give the students background in the respective econometric theory, on the other hand, due to this course the students should develop their skills in applied empirical analysis using panel and qualitative data. Theoretical background is to be used in the applied work for the proper choice of specification, estimation technics, and interpretation of the results. At the same time, the theory covered by the course may encourage someone to devote their future research to econometrics.
Скоробогатов А.С.
Эконометрика временных рядов (преподается на английском языке)

This is a graduate level course in time series econometrics. It will cover classical techniques for ARMA models, frequency domain techniques, unit roots, spurious regressions, and cointegration and structural VAR estimation. The prerequisite for the course is one graduate econometric theory course. Fundamentally, the students should have basic knowledge of matrix algebra and statistics and should have foundations of linear regression analysis of single- and multiple-equation models.

Слободян С.А.
Байесовская эконометрика и модели биостатистикиЦелями освоения дисциплины «Байесовская эконометрика и модели биостатистики» являют- ся построение и исследование методов выбора вероятностных моделей, наилучшим образом отра- жающих существенные особенности биомедицинских данных, а также методов сбора, систематиза- ции и обработки данных. Цель изучения дисциплины - ознакомление с теорией и методами байесовского подхода в приложении к прикладным задачам биологии и медицины. В основе курса лежит концепция байе- совского использования априорной информации в сочетании с накапливаемыми результатами на- блюдений для выработки рациональных решений.Мясникова Е.М.
Международная торговля (преподается на английском языке)The main purpose of the International trade course for students is to study the evolution of trade ideas. First, we discuss stylized facts, importance of international trade, and forces that drive international trade. Second, we study the classical theory of international trade based on technological differences or comparative advantages, including Ricardo and Heckscher-Ohlin models. The theory explains inter-industry trade. Third, starting from the growing tendency of intra-industry trade, we discuss why inter-industry trade is profitable for firms (increasing returns to scale at the firm level) and enjoyable for consumers (love for variety). We focus on the baseline monopolistic competition model and its applications in international trade in detail. The next step is to discuss modern interpretations of classical trade models and consequences of trade under combining classical and new trade theory. Therefore, we are going to discuss the “new new” trade theory where firms are heterogeneous. The main focus of this part of the course is on firm-level changes under trade liberalization. To conclude, new effects will be discovered in the new trade models by allowing variable markups.
Кичко С.И.
Корпоративный анализ данных

Целями освоения дисциплины «Корпоративный анализ данных» является формирование навыков работы с анализом данных как процессом. Дисциплина сочетает в себе как непосредственно работу с данными, изучение предиктивных моделей, так и изучение процессов внедрения решений, на основе анализа в бизнес-процессы.

Часть лекций в данном курсе проводится приглашенными аналитиками имеющими богатый опыт работы в реальной индустрии.

Раздел 1. Введение в корпоративную аналитику данных

Аналитическое мышление. Бизнес-проблемы и наука о данных. Введение в предиктивное моделирование. Дата-продукты.

Раздел 2. Инфраструктура анализа данных.

Базы данных и хранилища данных. Функциональные классы аналитических систем. Системы оптимизации. Экспертные системы. Системы машинного обучения. Операционная бизнес-аналитика. Аналитическая отчетность. ERP-системы. Облачные решения анализ данных.

Раздел 3. Организация аналитики в компании.

Проектная и процессная организация аналитики. Business Intelligence. Business Analytics. Enterprise Decision Management. Data Science и Big Data.

Мусабиров И.Л.
Распределенная обработка и анализ больших данных (преподается на английском языке)

The course deals with fundamentals of big data handling, how big data algorithms have to be different from “regular” learning algorithms, and how the MapReduce approach to parallelization works. The purpose of this course is to study modern approaches to representation and handling of Big Data. As a result, a student will have understanding of Big Data issues, knowledge of common ways to deal with such issues and practical skills in implementation of machine learning algorithms in a number of popular Big Data frameworks. This course contributes to understanding which methods of machine learning are used in practice.

The course consists of the following main topics:

Topic 1. Intro to big data: what is big data? How should algorithms change to handle big data?

Topic 2. Databases and Databases Management Systems. SQL and NoSQL, The Relational Model and The Model-less Approach

Topic 3. The MapReduce framework. Data Streams.

Topic 4. Basics of Hadoop, Spark and Microsoft Azure Machine Learning.

Topic 5. Big Data Algorithms I. Clustering, Dimensionality Reduction, and Frequent Itemsets

Topic 6. Big Data Algorithms II. Recommendation Systems and Web-Advertising

Topic 7. Big Data Algorithms III. Mining Social Network Graphs

Topic 8. Integrating Big Data algorithms in Decision Making. Architecture of Big Data Systems