Microeconomics I
This is a masters-level course that is suitable for students who have completed their training in intermediate microeconomics at the undergraduate level and seek to enhance their understanding of the subject. The course aims to provide students an appreciation of some commonly applied mathematical as well as game-theoretic tools and techniques in microeconomics, along with certain important applications to consumer theory and production. It addresses conceptual and applicational issues pertaining inter alia to mathematical functions, static optimization techniques, duality and envelope theorem, and static and dynamic games of complete information. In doing so, the course also provides a sound understanding of the applicability of various techniques in specific contexts. The end-term assessment consists of a research paper presentation on a topic covered during the course.
Microeconomics II
This course is suitable for students who have completed the Microeconomics I course and seek to enhance their ability to rigorously analyze the discipline. The course aims to provide students an understanding of the notion of stability of a single-market equilibrium and methods of comparative statics, optimization under uncertainty, as well as Bayesian games and their applicability in analyzing imperfectly competitive markets. This is achieved by addressing each topic with a minimum level of analytical rigour. The end-term assessment consists of an up to 2,000-word paper critically reviewing the literature on a topic covered during the course.
Macroeconomics I
The course is envisaged to familiarize the students with introductory level concepts of macroeconomic theories, policies, and applications. It primarily entails the theories pertaining to the determination of real income, employment, and interest rate in a macroeconomic framework. The course also includes some theories and concepts of the determination of exchange rates in an open-economy model. It aims to provide a thorough understanding of several contending macroeconomic theories from different (thought) schools, including classical, neoclassical, Keynesian, and monetarist. The course is designed to discuss issues and challenges in macroeconomic policies, particularly at the globalized level. The course also offers an understanding on major mathematical tools used in modern macro analyses. The course also furthers the understanding on short-run and medium-term dynamics as captured by business cycles. More specifically, it will discuss several types of macroeconomic shocks and how an economy responds to these shocks in a dynamic setup.
Macroeconomics II
This course offers a thorough understanding of advanced-level concepts in macroeconomics. The course content is designed to provide students with a comprehensive understanding on general equilibrium models. More specifically, an explicit focus on analysis for real business cycles, new Keynesian and Dynamic Stochastic General Equilibrium (DSGE) framework. The course also offers exposure to some key concepts pertaining to calibration techniques and simulations. The course further entails advance, endogenous growth models. The primary aim of the course is to develop theoretical insights to understand and analyze policies (particularly in the short run). In order to cover the problems of the contemporary macroeconomy, the course pays more attention to the understanding of macroeconomic issues in a globalized framework.
Econometrics I
Econometrics is the application of mathematical and statistical tools used to estimate economic relationships. The objective of this course is to introduce the fundamental ideas of modern statistical analysis and predictive modeling. The course is an introduction to modern econometrics. It will involve statistical, theoretical, and computational aspects in econometrics. Through this course, students will get training in basic econometric methods to analyze economic data, test hypotheses, and forecast future trends. Students will also learn to critically examine applied work and undertake their own applied research projects. Students will get hands-on experience working with large-scale economic data for graduate studies in economics and related fields. Along with lecture sessions, students will have weekly labs or tutorials where they will be introduced to statistical software and taught the analysis of economic data using the software. The course will begin with discussing the linear regression models, finite and large-sample properties of the least-squares estimator, and inferences and predictions. It will then introduce several topics relevant to the analysis of economic data. These include generalized least squares, simultaneous regression methods, instrumental variable estimation, maximum likelihood, binary choice, and nonlinear regression models. This course will help students acquire the background required for research at the Ph.D. level or employment as a professional applied economist.
Econometrics II
This is an advanced course in Econometrics. It is an applied course. The course will provide advanced instruction and rigorous training in econometric theories and the appropriate empirical research methods in economics. It will help students develop the powers of inquiry, critical and logical thinking, and problem-solving. Students will learn to apply theoretical knowledge in econometrics to analyze current issues of policy and practice. It will enable students to use advanced research skills in a relevant research area in economics and/or econometrics through an empirical project. The course content will build on the textbook treatment of the topics through the detailed analysis of recent research papers that apply the methods. Through this course, students will learn to demonstrate an understanding of the shortcomings of the standard linear regression model and learn techniques to overcome some of these critical shortcomings. This course will begin by reviewing the concepts learned in Econometrics I and then introduce newer advanced-level concepts in econometrics. These include bivariate and multivariate models, multinomial choice models, ordered choice models, limited dependent variable models, experiments and quasi-experiments, panel data, time-series, and nonparametric methods. All the applications in this course will be conducted using statistical software. Through this course, students interested in further study at the Ph.D. level will acquire the technical and intuitive understanding necessary to engage in applied econometric research. Students interested in employment as an applied economist across many areas - including government and policy fields - will gain experience in using theoretical and empirical econometric methods to a diverse set of real-world contexts.
Development Economics
Developmental Economics, or the systematic study of economic transformation in developing nations, is a major component within FLAME University's Master's program. Since this branch of economics delves into the different models of economic growth, the role of the market and its failures, and the responsibility of the state and its limited capacity, it provides excellent material for engaging and enriching student discussions and scientific research by faculty. Globally, this is a growing field of research as endorsed by the expanding number of journals and international conferences. This course on Developmental Economics will equip students in understanding the underlying theories of development. Apart from learning about the historical progression of growth and development studies. This course also encourages creative thinking in terms of analyzing the relevance of several of these theories in today's world. The course also introduces empirical research papers to help students get a feel of recent studies that have come out in the field of development economics.
Mathematical Economics
This course is suitable for students who possess an undergraduate major or honors degree in economics and seek extensive exposure to the mathematical tools of economic analysis. The salience of this course lies in the increasing contemporary importance of quantitative treatments of topics within economics. The course seeks to provide mathematical frameworks and techniques for the conceptual treatment of various topics in both microeconomics and macroeconomics. Inter alia, the course theoretically addresses topics from real analysis, duality theory, and envelope theorem, unconstrained and constrained static optimization, as well as dynamic optimization using the Hamiltonian function.
Applied Game Theory
The design and presentation of this course make it suitable for a diverse audience in terms of academic background. However, a semester's duration of undergraduate-level exposure to both game theory and mathematics is desirable. The content provides students with detailed exposure to different categories of games with widespread real-world applicability in fields including economics, psychology, sociology, international relations, political science, etc. The course addresses static and dynamic games of both complete and incomplete information and discusses plenty of applications. The treatment of this course is highly mathematical throughout, and assessments are commensurately challenging.
Contemporary World & Indian Economy
This course offers students a unique perspective on the Indian economy's engagement with the world economy from a political economy and macroeconomic perspective. We begin by contrasting the macroeconomy of India during the interventionist and liberalized phases since independence, followed by how the transition impacted India's relationship with the rest of the world. The course covers fiscal and monetary performance issues, Globalization, the balance of payments, structural change and sectoral performance, and socio-economic aspects of inequality, poverty, and employment. The emphasis is on understanding the roots of underdevelopment in the Indian economy and the underlying post-colonial structures and institutions, as well as the dynamics of uneven or 'dependent' development in the world economy.
Financial Economics
This course will be consisting of two parts. The first part will provide an introduction to theoretical financial economics with a focus on asset pricing in discrete time, complete and incomplete markets, agency theory, and financial intermediation. The second part will provide an understanding of the advanced financial, economic theory and an introduction to financial econometrics. Topics include dynamic portfolio choice, consumption-based asset pricing, and linear factor models. Additional topics may include option pricing and the term structure of interest rates.
Economic Modelling and Forecasting
This course will cover applied statistical methodologies related to time series data with an emphasis on model building and forecasting. The course will emphasize on developing insights using modelling tools to analyze real-world economics, health, climate, and financial data. Along with theory, students will be working with real-life data with statistical software such as Matlab and R. Additional topics may include nonlinear methods and analysis of high-frequency financial data.
International Economics
This course in international economics will provide students a foundation in both neoclassical and heterodox theories and empirical analyses, applications, and debates in international trade and finance while focusing on policy-oriented approaches. Additionally, this course will approach international trade and finance from the perspective of development theory and policy. The discussion will apply to developing, emerging, and advanced economic contexts. The first half of the course will engage with the theory and policy on international trade, including comparative advantage, factor growth, market distortions, and commercial policy. The second half of the course will discuss the historical evolution of international financial markets and the policy implications of the financial crisis and global imbalances in the world economy.
Health Economics
Health economics informs how people weigh different choices, make difficult decisions about prioritizing resources, and help people live a healthier and longer life. This course will introduce students to economic theory related to health and health care, focusing on empirical analysis. The course is based on a highly valued set of quantitative, theoretical, and analytical competencies required to make essential health and healthcare decisions. The course will introduce students to the economic approach in health, health care, and health insurance. It will discuss the main components of issues of the organization, financing, and delivery of healthcare services. It will help students learn and critically evaluate the political, social, and economic policies and policy debates in health systems at the local, state, and national levels. The course will introduce students to different empirical techniques to analyze and discuss various dimensions of health economics research. Students will be able to apply these empirical techniques in analyzing data related to health system financing and equity and evaluate the effectiveness of health programs. Students will learn to explain and predict people's health-related behaviors and use this understanding in predicting the consequences of different social actions. Through analysis of big health datasets, students will also develop policy-relevant research questions and outcomes. The course will contribute to the development of economists as health professionals who will participate in various health policy debates, analyze large-scale health datasets, and provide empirical evidence to support policy-based health economics research.
Behavioural Economics
Traditional neoclassical economics shy away from considering the role of human emotions and human judgment from its models. Some of these models have stood the test of time and are still taught at universities, but many fail to be replicated in the real world. Interestingly, the role of human sentiments and behaviour has been studied since the times of Adam Smith, but was never given mainstream attention till the Nobel prize-winning works of Herbert Simon (in 1978), Gary Becker (in 1992), Daniel Kahneman (in 2002) and Richard Thaler (in 2017). Behavioural Economics is a novel field of economics which borrows heavily from other disciplines such as psychology, biology, neurology, and sociology to explain departures of traditional theoretical models and strategic games. The objective of this course, therefore, is to provide an understanding of important concepts in behavioural economics. This course also aims to provide some intuition into human behaviour through games and experiments and make the learner thoroughly updated on this new and dynamic field of enquiry.
Informal Economy
Contrary to popular belief, the informal economy has expanded rather than contracted over the last few decades. Liberalization and Globalization have led to the creation of more informal jobs in developing economies. According to ILO estimates, more than 60 percent of the world's workers are now part of the informal economy. Therefore, the role of the informal economy is gaining traction among practitioners, policymakers, and academics. This course aims to analyze the evolution process of the high levels of informality in developing countries. The course will discuss issues related to the challenges of measurements of the informal economy. What are the implications of a high informality on poverty, employment, social inclusion, gender empowerment, social protection, taxation, governance, and politics? The course will draw on empirical evidence and comparative case studies from across the developing world.
Experimental Economics
This course will introduce students to experimental economics, where they will learn to apply experiments to guide alternative policy options. The primary objective of Experimental Economics is to test the validity of conventional theories taught in the economics literature, identify departures from those theories, and establish new behavioural patterns that help to construct new theories. Economic experiments are conducted in the laboratory or the field setup. A real-world incentive structure is replicated in economic experiments. Students will be exposed to the methods of applying experiments using actual goods rather than induced valuation. In the methodology section, students will learn how the field experiments differ from the lab-based economic experiments. In addition, they will study some of the recent developments in experiments and explore further applications of experiments in the context of gender, resource, and environmental issues
Financial Technology (FinTech)
Rapid advancements in financial technology, or FinTech, are opening the door to greater financial inclusion and sustainable development. This course will introduce how FinTech is reshaping the traditional currency regime. Students will go through the historical perspective of the evolution of FinTech. Students will explore the adoption and driving forces of financial innovation using real-world examples to assess the changes, disruptions, and adaptations resulting from the rise of FinTech solutions. They will evaluate the impact of FinTech on several sectors, including real estate, financial trading, and payment system. The course will also examine the latest advancements in FinTech, its financial inclusion implications, and strategies and tools for mitigating associated risks.
Programming for Data Science
Programming is an integral element of data science. This course takes a problem-solving approach to introduce the fundamentals of scientific programming. Using simple examples, it will introduce basic programming paradigms which are useful for data science applications. The emphasis will be on designing and writing correct code including testing and debugging. The course will mainly employ Python and R to teach programming concepts.
Computational Statistics and Data Analysis
Computational statistics utilises the advances in computational technologies to solve statistical problems. In this course, students will learn to manipulate and visualise data using advanced statistical programming environments, and write code to design and perform experiments using Monte Carlo simulations. The course will use computers to recapitulate standard descriptive and inferential statistical concepts using random samples of probability distributions. It will then progress towards introducing Gaussian processes, optimization techniques, numerical integration, random variable generation, Markov chain Monte Carlo (MCMC, boot strap methods, density estimation methods which are useful to analyse large amounts of data. R and Python will be the main programming environments that will be used in this course.
Machine Learning for Data Science 1
Machine Learning is an important computational tool to create knowledge and gain insights from large amounts of data. This course, which is the first of two courses will provide a broad introduction to machine learning, datamining, and statistical pattern recognition using supervised and Unsupervised learning methods. Topics to be covered include Regression, K -Nearest Neighbours, Classification, Dimensionality Reduction, Decision Trees and Random Forests, Principal Component Analysis and Clustering Analysis. The approach will be to gain practical knowledge to quickly and effectively apply the concepts learned to new contexts. R and Python will be used extensively.
Machine Learning for Data Science 2
This is the second of a two-part course which will focus on the relationship between Machine Learning and Econometrics. This course will emphasise on the difference between prediction and explanation, estimation of treatment effects using machine learning, dimensionality reduction and variable generation. The methods that will be covered include Causal Trees and Forests, Double Robust Machine Learning, and Text Mining with applications to economics.
Data Mining for Business and Economic Intelligence
In today’s knowledge economy, Data is the most important commodity and the methods required to acquire, store, and analyse them is critical for the operations of business and the economy. This course relates to data collection, storage, and its analysis. Using practical examples, it aims to inform students of the tools and techniques of leveraging data and technology for intelligent decision making. It will cover basic methods of data preprocessing, association rules, classification, clustering, sequence mining and visualization. Real world data open data sets will be used as case studies.