Global Economics - Senior

COURSE #: ECON 3031

Course description

This course builds on an understanding of entrepreneurship and the vital role played by entrepreneurs and entrepreneurship in the development of economy.  In addition, the course provides a platform for undertaking research in the field of economics of entrepreneurship and implementing economic analysis of entrepreneurial ventures. Students are introduced to the concept and theory of entrepreneurship. Students use methods of applied entrepreneurial research, assess performance of entrepreneurial ventures, analyze public policy, market regulation and taxation and their impact on entrepreneurial activity.

Course learning outcomes

Upon successfully completing this course, students should be able to:

• Explain the role of entrepreneurship in economies
• Analyze entrepreneurial markets and behavior
• Demonstrate an understanding of entrepreneurial financing
• Present an analysis of public policy

Course Assessments and Grading

COURSE #: ECON 4001

Course Description

In this course we look at key developments and tendencies in post-Soviet economies of Central Asia. We introduce students to logic and instruments of analysis of national economies of the region. It starts with historical background of Central Asia and highlights region’s transition from planned to a market-based economy. The role of natural resources, migration, and remittances in the context of specific Central Asian republics is discussed in detail. Economic integration in the form of free trade, customs union and other types of regional cooperation is explored as well. In the end of the course differences in growth rates of output, human and physical capital and total factor productivity in all Central Asian countries will be analyzed based on the growth accounting technique.

Course Learning Outcomes

• Understand the nature of centrally planned economic system and its failure in the former Soviet Union, particularly, in the context of all Central Asian republics.
• Analyze costs associated with transition to a market-based economy in the region.
• Discuss the role of natural resources in a country’s post-communist development and presence of any type of resource curse in Central Asia.
• Explain the role of international migration from the region and remittances on a country’s development for the last two decades.
• Analyze regional cooperation projects among Central Asian republics and their possible integration into the world economy.
• Apply the Solow growth model to identify differences in economic performance among all countries of the region.
• Understand existing and prospective policies for bilateral and multilateral cooperation of Central Asian republics with other countries and international, and regional agencies.

Course Assessments and Grading

COURSE #: ECON 4037

Course Description

Electricity plays a crucial role in our life, allowing us to perform our daily routines and to undertake economic, social and developmental activities. Economics of Electricity is an interdisciplinary economics course which covers both a detailed description of the technologies and institutions of the consumption, production and transportation sides of electricity markets.  A variety of core microeconomic concepts are introduced for non-economists, in order to better understand the behavior of firms, consumers and regulators in electricity markets. The course emphasizes issues related to both the local and the global electricity sectors, with an eye towards pricing, regulation and access. In particular, the tools learned in the course will set the foundations to conduct energy research, making use of quantitative data, write policy case studies, produce market forecasts and manage energy-related firms and investments. 

Course Learning Outcomes

At the end of the course in energy economics students are expected to be able to:

• Apply microeconomic modeling techniques to understand the functioning of a variety of energy markets and the impact of government actions in them;
• Discuss the special characteristics of wholesale and retail electricity markets, electricity pricing and electric grids;
• Model investment and other financial decisions for a variety of energy firms;
• Assess the effects of energy price volatility on energy importers and other sources of systemic risk;
• To explain the determinants of energy access and link between energy access and economic development;
• To evaluate programs for improving energy efficiency;

Course Assessment and Grading

COURSE #: DMNS 4180 

Course Description 

Linear programming is one of the most versatile and powerful mathematical programming techniques that can be employed for efficiently solving a stylized class of decision problems. 

Linear programming has been used with marked success to solve optimization problems in areas such as economics (including banking and finance), business administration and management, agriculture and energy, contract bidding, nutrition (diet) planning, health care, public decision making, facility location, transportation, strategic planning, and so on.  

In this course, students will study mathematics that deals with various real-world situations which leads to mathematical models involving linear optimization problems, geometrical solution of two-variable problem, simplex algorithm, dual problems and transportation problem. 

Course Learning Outcomes 

Upon successful completion of this course, students should be able to: 

• Formulate a mathematical model of real life problem. 
• Solve the formulated mathematical problem with the use of different 
• Translate the results back into the context of the original problem.  

Course Assessments and Grading 

COURSE #: ECON 4109

Course Description

Experiments are a fundamental tool for research in a wide array of disciplines, including all social sciences. Experiment is an attractive research method because it enables the researcher to ground statistical and causal inferences in features of the research design rather than assumptions about the world. This course is an introduction to the Experimental Economics and its methods. Its objective is to review the main results obtained in experimental economics. It covers the basic theoretical principles of experimental design, discusses some practical issues, and introduces statistical techniques used to analyze experimental data. The focus will be on field and laboratory experiments. Strong emphasis is placed on developing practical skills for real research scenarios, in particular O-tree software is explored.

Course Learning Outcomes

At the end of my course students will be able to:

• Understand what the experiment in social science (both lab and field) is; when, how and why it should be used
• Explain the strong and weak points of the experimental approach compared to other empirical methods in social sciences
• Explain basic experiments in economics (dictator game, ultimatum game; public good game, gift-exchange game)
• Design their own experiments
• Explain what availability bias is, sunk costs, base rate neglect, confirmation bias and give examples of these cognitive biases

Course Assessments and Grading

COURSE #: ECON 4308

Course description

Data Science III: Automated statistical analysis of the terabytes of data that are produced every minute in text form is one of the frontiers of data analysis. It allows the integration of new data types in standard statistical analysis as well as new insights into the properties and significance of texts. Text Analysis is an advanced quantitative methods course which builds on the skills and knowledge acquired in Econometrics I & II. Students learn the tools required for statistical analysis of text, including working with character encodings, regular expressions and string functions.  The course progresses to construction of datasets from text, analysis of texts based on their similarity, sophistication and sentiment as well as classification of texts, descriptive statistics and data visualization for text data and description of texts based on topic.

Learning outcomes

At the end of this course students are expected to be able to:

• Classify texts based on characteristics using unsupervised learning algorithms
• Utilize regular expressions to write code for pattern matching in text strings
• Create variables from text strings and apply these in categorization of texts and analysis of text sentiment
• Extract sentiment information and other variables from text for application in traditional regression analysis
• Analyze the similarity and sophistication of texts for use in comparison between and identification of authors
• Perform Latent Dirichlet Analysis to determine the topics found in bodies of text

Course Assessment and Grading