Syllabus for
Master of Science (Economics and Analytics)
Academic Year  (2022)

The Department of Economics, CHRIST (Deemed to be University) Delhi NCR Campus, formed in 2019 consists of a faculty pool with rich experience in teaching, research and consultancy. The Department has five full-time faculty members with specialisation in Development Economics, Rural and Health Economics, Quantitative Economics, Agricultural Economics, Resource Economics, involving in advanced research.

Vision

Establish an identity as a department of high standard in teaching and research in Economics.

 Mission

Equip students with advanced knowledge and skill sets to address real world economic problems and undertake cutting edge research on contemporary economic issues.

The Master of Science in Economics and Analytics is an intensive program that will guide students through economic modelling and theory to computational practice and cutting-edge tools, providing a thorough training in descriptive, predictive and prescriptive analytics. Students will be equipped with a solid knowledge of econometric and machine learning methods, optimization and computing. These big-data skills, combined with knowledge of economic modelling, will enable them to identify, assess and seize the opportunity for data-driven value creation in the private and public sectors. Students will be trained to contribute significantly to empirical and applied work in the upcoming field of Economics. 

CIA - 70% 

ESE - 30% 

1. Pindyck, Robert & Rubinfeld, Daniel (2013), Micro Economics, 8th Edition, Pearson Education, USA. 

2. Besanko, D. and Braeutigam, R. (2015) Microeconomics, 5th Edition, Wiley India 

3. Nicholson, W, and Snyder, C (2021) Microeconomic Theory: Basic Principles and Extensions, Cengage Learning, USA

CIA II- 25

CIA III- 20

Attendance-5

ESE-30

This paper aims at strengthening the knowledge of important macroeconomic variables and their role in determining the equilibrium level of output and employment and provides insights into the factors influencing the capital inflows and outflows in an open economy model. It helps the students to understand the theoretical foundation of macroeconomics and the contribution of different schools of thought to the further development of macroeconomics.

The development of macroeconomics- Circular flow of money and product, Actual and potential output- GNP identity on the product, income, and disposition side-The government sector and foreign sector-Classical theory of income and employment- Behaviour of Aggregate Demand and Aggregate supply, money and prices in the classical model- Keynes’ theory of employment- Role of Aggregate Demand- Consumption function, investment demand- Effective demand- Determination of equilibrium income- Theory of multiplier-Derivation of Investment, expenditure and trade multiplier

Equilibrium income and the interest rate determination in the product market- Equilibrium income and the interest rate determination in the money market- Derivation of IS and LM curves-Shift in IS and LM curves-Simultaneous equilibrium- Fiscal and monetary policy effects on demand-Interaction of monetary and fiscal policies Aggregate supply in the short run and long run-Supply side disturbances and reactions-Demand side disturbances and reactions-Determination of equilibrium income, employment, rate of interest and price level.

Financial intermediation — a mechanistic model of bank deposit determination; A behavioural model of money supply determination, a demand determined money supply process; RBI approach to money supply; High powered money and money multiplier; budget deficits and money supply; control of money supply. Neo-classical and Keynesian Synthesis

Classical approach to the demand for money — Quantity theory approach, Fisher’s equation, Cambridge quantity theory, Keynes’s liquidity preference approach, transaction, precautionary and speculative demand for money — aggregate demand for money; demand for money — Patinkin and the Real Balance Effect, Approaches of Baumol and Tobin; Friedman and the modern quantity theory; Crisis in Keynesian economics and the revival of monetarism

Mundell-Fleming model — Asset markets, expectations and exchange rates; Monetary approach to the balance of payments.

Monetary transmission mechanism and targeting Inflation Money growth and interest rates  Interest rate rules Taylor rule Rules versus discretion  Central Bank autonomy Dynamic inconsistency of monetary policy credibility and reputation Coordination of fiscal and monetary policy, Rationale and impact of reforms since 1991 on BOP.

2. Dornbusch, Fischer, Startz. (2010). Macroeconomics. 11th Edition, Tata McGraw

Hill.

Oxford University Press, New York.

2. Graeme Chamberline& Linda Yueh (2006). Thomson Learning.

3. N. Gregory Mankiw. (2012). Macroeconomics. 8th Edition, Worth Publishers.

4. Dornbusch, Fischer, Startz. (2010). Macroeconomics. 11th Edition, Tata McGraw

Hill.

5. M. Maria John Kennedy (2011). Macroeconomic Theory, PHI Learning Private

Limited, New Delhi.

6. H. L. Ahuja. (2012). Macroeconomics: Theory and Policy. 18th Revised Edition,

Sultan Chand Publishers.

7. Brain Snowdown, Howard Vane and Peter Wynarczyk. (1995). A Modern Guide to

MacroEconomics: An Introduction to Competing School of Thought, Edward Elgar

Publishing.

8. Edward Shapiro. (2011). Macroeconomic Analysis. 5th Edition, Galgotia

Publication Ltd.

9. Ackley. G. (1978). Macroeconomics: Theory and Policy, Macmillan, New York.

10. Mishkin Frederic (2007), The Economics of Money Banking and Financial Markets,

8th ed Addison Wesley Longman Publishers.

11. Bain, Keith & Howells,Peter (2009), Monetary Economics: Policy and Its

Theoretical Basis, Palgrave.

12. .Friedman, Ben & Hahn F.H. (Eds.), (1990), Handbook of Monetary Economics,

Vols. 1, 2, & 3, North Holland Publishers.

13. Langdana Farrokh (2009), Macroeconomic Policy: Demystifying Monetary and

Fiscal Policy, 2nd Edition, Springer.

CIA II: 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%

The principles of data science deals with the econometric scientific methods of analyzing data. Today, we live in a big data world, where the amount of data generated everyday is very huge, therefore we need methods to clearly transform and analyze data. Therefore, machine learning, which is included in this syllabus, does the job. Also, the students here are introduced into different scenarios and methodologies to get results out of data.

Preparing and gathering data and knowledge - Philosophies of data science - data all around us: the virtual wilderness - Data wrangling: from capture to domestication - Data science in a big data world - Benefits and uses of data science and big data - facts of data - data science processes

Overview of the data science process - retrieving data - Cleansing, integrating, and transforming data - Exploratory data analysis - Build the model - Presenting finding and building applications on top of them

Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms

First steps in big data - Distributing data storage and processing with frameworks - Case study: Assessing risk when loaning money - Join the NoSQL movement - Introduction to NoSQL - Case Study

The rise of graph databases - Introducing connected data and graph databases - Text mining and text analytics - text mining in real world - text mining techniques

Data Visualization

Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools.

2. Cielen, D. & Meysman A, (2016). Introducing Data Science, Manning Publications

2. O'Neil, C., & Schutt, R. (2013). Doing data science: Straight talk from the frontline. " O'Reilly Media, Inc.

3. Rajaraman, A., & Ullman, J. D. (2011). Mining of massive datasets. Cambridge University Press.

4. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning, Springer

Linear Algebra plays a fundamental role in the theory of Data Analytics. This course aims at introducing the basic notions of vector spaces, Linear Algebra and the use of Linear Algebra in applications to Data Analytics.

Vector Spaces: Rn and Cn, lists, Fn and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension.

Definition of LinearMaps -Algebraic Operations on L(V,W) - Null spaces and Injectivity-Range and Surjectivity-Fundamental Theorems of Linear Maps-Representing a Linear Map by a Matrix-Invertible Linear Maps-Isomorphic Vector spaces-Linear Map as Matrix Multiplication - Operators - Products of Vector Spaces - Product of Direct Sum - Quotients of Vector spaces.

Eigenvalues and Eigenvectors - Eigenvectors and Upper Triangular matrices - Eigenspaces and Diagonal Matrices - Inner Products and Norms - Linear functionals on Inner Product spaces. Multiple commodity markets- IS-LM Model- Mundell-Fleming Model

Matrix Norms – Least square problem - Singular value decomposition- Householder Transformation and QR decomposition- Non Negative Matrix Factorization – bidiagonalization.

2. Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007.

2. J. V. Kepner and J. R. Gilbert, Graph algorithms in the language of linear algebra, Society for Industrial and Applied Mathematics, 2011.

3. D. A. Simovici, Linear algebra tools for data mining, World Scientific Publishing, 2012.

4. P. N. Klein, Coding the matrix: linear algebra through applications to computer science, Newtonian Press, 2015.

CIA I- 10 Marks

CIA 2-10 Marks

CIA 3-30 Marks

The objective of this course is to provide comprehensive knowledge of Python programming paradigms required for Data Science.

Structure of Python Program-Underlying mechanism of Module Execution-Branching and Looping-Problem Solving Using Branches and Loops-Functions - Lists and MutabilityProblem Solving Using Lists and Functions 

Sequences, Mapping and Sets- Dictionaries- -Classes: Classes and Instances-InheritanceExceptional Handling-Introduction to Regular Expressions using “re” module.

Basics of NumPy - Computation on NumPy-Aggregations-Computation on ArraysComparisons, Masks and Boolean Arrays-Fancy Indexing-Sorting ArraysStructured Data: NumPy’s Structured Array.

Introduction to Pandas Objects-Data indexing and Selection-Operating on Data in PandasHandling Missing Data-Hierarchical Indexing - Combining Data Sets Aggregation and Grouping-Pivot Tables-Vectorized String Operations -Working with Time Series-High Performance Pandas- and query()

Basic functions of Matplotlib- Simple Line Plot, Scatter Plot-Density and Contour PlotsHistograms, Binnings and Density-Customizing Plot Legends, Color Bars-ThreeDimensional Plotting in Matplotlib.

1. Demonstrate usage of branching and looping statements

2. Demonstrate Recursive functions

3. Demonstrate Lists

4. Demonstrate Tuples and Sets

5. Demonstrate Dictionaries

6. Demonstrate inheritance and exceptional handling

7. Demonstrate use of “re”

8. Demonstrate Aggregation

9. Demonstrate Indexing and Sorting

10. Demonstrate handling of missing data

11. Demonstrate hierarchical indexing

12. Demonstrate usage of Pivottable

13. Demonstrate use of andquery()

14. Demonstrate ScatterPlot

15. Demonstrate 3D plotting sciPy

1. Jake VanderPlas (2016) ,Python Data Science Handbook - Essential Tools for Working with Data, O’Reily Media,Inc.

2. Zhang.Y (2016) , An Introduction to Python and Computer Programming, Springer Publications

2. T.R.Padmanabhan(2016), Programming with Python, Springer Publications.

The main objective of the course is to introduce both traditional as well as modern ideas and theoretical concepts in microeconomics. It also deals with a fundamental understanding of market theory, theory of factor pricing, theory of general equilibrium and welfare economics. It also includes understanding the role of institutions by focusing on transaction costs, absolute property rights and relative property rights. 

Neo-classical approach: Marginal productivity theory - in perfect and imperfect product and factor markets; Product exhaustion theorem; Elasticity of technical substitution, technical progress and factor shares.

Asymmetric Information, The Payoff Matrix of a Game, Nash Equilibrium, Mixed Strategies, The Prisoner’s Dilemma, Repeated Games, Games of Coordination, Games of Competition and Games of Coexistence.

Applications: strategic behaviour of firms in a market–Bertrand, Cournot and Stackleberg models – and entry deterrence

Macro theories of distribution – Ricardian, Marxian, Kalecki and Kaldor’s

Existence, stability and uniqueness of partial equilibrium and general equilibrium. Pareto optimality; Fundamental Theorems of Welfare Economics; Theory of second-best –Arrow’s impossibility theorem.

Education, USA

2. Henderson, J.M. and R.E. Quandt (2003), Microeconomic Theory: A Mathematical

Approach, McGraw Hill, New Delhi.

contribution of the new institutional economics. University of Michigan Press.

2. Andreu Mas-Colell, M D Whinston and J R Green (1995), Microeconomic Theory,

Oxford University Press.

3. Kreps, D. M. (1990). A course in microeconomic theory. Princeton university press.

4. Krugman, P., & Wells, R. (2015). Macroeconomics, 4th.edition.

5. Koutsoyiannis, A. (1979), Modern Microeconomics, (2nd Edition), Macmillan Press,

London.

6. Mukherjee, Anjan (2002), An Introduction to General Equilibrium Analysis, Oxford

University Press.

7. Osborne, Martin J. (2009), An Introduction to Game Theory, Oxford University

Press.

8. Sen, Anindya (2007), Microeconomics: Theory and Applications, Oxford University

Press, New Delhi.

9. Varian, H. (2000), Microeconomic Analysis, W.W. Norton, New York.

CIA II: 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%  

This course aims at strengthening the knowledge of important macroeconomic variables and their role in determining the equilibrium level of output and employment and provides insights into factors influencing the capital inflows and outflows in an open economy model. It helps the students to understand the theoretical foundation of macroeconomics and the contribution of different schools of thought to the further development of macroeconomics. Upon successful completion of this course, the students will be able to: critically evaluate the consequences of basic macroeconomic policy options under differing economic conditions within a business cycle. 

Real and monetary theories of the interest rate Keynesian theory, Wicksellian theory, Fisher’s theory, Hicksian theory Credit market imperfections Adverse selection and moral hazard

The Classical and Neoclassical views on holding money- Real and monetary theories of  the rate of interest: liquidity preference and loanable funds theories of interest- The  term structure of interest rates: Pure Expectations, Pure segmentation and  Substitutability theories- Portfolio theories of demand for money- Baumol-Tobin  approach to transaction demand for money- Tobin’s portfolio optimization approach 

Friedman’s quantity theory of money. 

Measurement, Endogenous theories (Hicks, Goodwin, Kaldor), Exogenous theories - Real Business Cycle Theories - Real Business Cycle School and inter temporal substitution of labour- Real Business Cycle theory- technology shocks- neutrality of money and flexibility of wages and prices- Real Business cycle view on the great depression- The modern monetarism, major postulates-Keynesian policy framework New Classical macroeconomics- Stagflation trend-The Supply-Side economicsmajor implications. Great recession and Covid-19 Pandemic. 

The new classical critique of micro-foundations, the new classical approach; Policy implications of new classical approach — empirical evidence. Rational expectation revolution 

2. D.N. Dwivedi. (2005). Macroeconomics: Theory and Policy. 2^nd Edition, Tata McGraw  Hill Education. 

3. Levacic and Rebman. (1982). Macro Economics: An Introduction to Keynesian and  Neoclassical Controversies. 2^nd Edition, Macmillan Publishers. 

2. Graeme Chamberline& Linda Yueh (2006). Thomson Learning. 

3. N. Gregory Mankiw. (2012). Macroeconomics. 8^th Edition, Worth Publishers. 

4. Dornbusch, Fischer, Startz. (2010). Macroeconomics. 11^th Edition, Tata McGraw Hill.

5. M. Maria John Kennedy (2011). Macroeconomic Theory, PHI Learning Private  Limited, New Delhi. 

6. H. L. Ahuja. (2012). Macroeconomics: Theory and Policy. 18^th Revised Edition, Sultan  Chand Publishers. 

7. Brain Snowdown, Howard Vane and Peter Wynarczyk. (1995). A Modern Guide to  Macroeconomics: An Introduction to Competing School of Thought, Edward Elgar  Publishing. 

8. Edward Shapiro. (2011). Macroeconomic Analysis. 5th Edition, Galgotia Publication  Ltd. 

9. Ackley. G. (1978). Macroeconomics: Theory and Policy, Macmillan, New York. 

CIA II : 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%  

On completion of the course students should be able to:

● Formulate, estimate, test and interpret suitable models for the empirical study

of economic events;

● Acquire the ability to evaluate the performance of alternative econometric

models through the appropriate use of tests.

● Attain knowledge reading and interpreting applied economic articles

Linear regression model, two variables and multi variables, BLUE property, general and  confidence approach to hypothesis testing, partial effects and elasticity, goodness of fit,  model evaluation

Consequences and detection of multicollinearity, heteroskedasticity, and  autocorrelation; and remedial measures 

Regression on qualitative and quantitative variables, dummy variable trap, structural  stability of regression models, Chow test, piecewise linear regression model 

Simultaneity bias, structural versus reduced form, identification: rank versus order  condition, exact and over identifications, triangular model, methods of estimation  including indirect least squares, two-stage least squares and three-stage least squares,  LIML and FIML 

Formation of expectations, naïve expectation versus adaptive expectations models,  partial adjustment models, distributed lag models; Koyck’s model, Almon lag,  polynomial distributed lag models, end point restriction, rational expectations models 

2. Gujarati, N.D., Basic Econometrics, fourth edition, McGraw Hill, 2003

2. Maddala G. S. (1992) Introduction to Econometrics (2nd Ed.) Macmillan Publishing

Company.

3. Gujarati D. N. & Porter D. C. (2010). Essentials of Econometrics, 4thMcGraw Hill

International Edition.

4. Brooks C. (2019). Introductory Econometrics for Finance 4th Ed. Cambridge Univ.

Press.

5. Hill, C., Griffiths, W. E., & Lim G. C. (2018). Principles of Econometrics, 5th Edition.

Wiley.

6. Ramanathan, R. (2002). Introductory Econometrics with Applications, 5th edition,

Thomson Asia Private Limited. 

CIA II : 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%  

The main objectives of the course are to train the students to grasp the use of mathematical techniques and operations to analyse economic problems and to introduce students to various economic concepts which are amenable to mathematical treatment.

Equilibrium analysis in Economics-Definition of equilibrium-Solution of equilibrium- Single vs. multiple equilibrium-Partial vs. general equilibrium. 

Application: single vs. multiple commodity markets

Areas under curve-Definite and indefinite Integration, Application- Consumer Surplus and Producer Surplus

Concavity, Convexity, Quasi concavity, Quasi convexity

Optimization of functions of one variable -Main concepts- First order conditions-Second order conditions (sufficient conditions)

Applications: Profit maximization (one product) under: - perfect competition - monopoly. – Monopolistic –Oligopoly (Collusive and Non Collusive Oligopoly Models - Cournot model, stackelberg model)

Optimization of functions of more than one variable- The differential version of optimization conditions- Extreme values of function of two variables and comparative static aspect of optimization

Application: Profit maximization (two products) under perfect competition- extreme values of function of n variables. Applications: i)Monopolist selling in segmented markets

Two variables, one constraint-Lagrange-multiplier method-First order conditions-Second order conditions, Hessian Border Condition. 

Applications: Utility maximization and consumer demand (two goods, one period)-Utility

maximization and consumer demand (one goods, two periods)- perfect access to international capital markets.-financial autarky -welfare implications

First order linear difference equations- Second order difference equations

First order differential equations- Second order differential equations

Application: Cobweb Market Model, Dynamic stability of Market price

2. Dowling, E. T. (2001). Introduction to mathematical economics. McGraw-Hill.

3. Alpha Chiang and Kevin Wainwright (2004), Fundamental Methods of Mathematica Economics. McGraw-Hill Book Company, 4th Edition (Chiang)

4. Carl P. Simon and Lawrence Blume (2006), Mathematics for Economists, W.W. Norton & Company (Simon)

5. Hoy, M., Livernois, J., McKenna, C., Rees, R., & Stengos, T. (2011). Mathematics for economics. MIT press.

3. Allen R G D (1967). Macro-economic Theory, McMillan Co., Ltd.,

4. Chiang A C (1986). Fundamental Methods of Mathematical Economics, McGraw Hill, New York.

5. Koutsoyiannis A. 2003. Modern microeconomics, 2nded, ELBS with McMillan.

6. Monga G S. 2001 Mathematics and Statistics for Economics, Vikas Publishing House Pvt. Ltd., Delhi.

7. Yamane, Taro (1981) Mathematics for Economists, Prentice Hall of India, New Delhi.

8. Mehta and Madnani (2005) Mathematics for Economists, Sultan Chand and Sons, New Delhi.

CIA II   : 25 % (Mid Semester Examination)

CIA III  : 20 %

Attendance: 05 %

ESE       : 30%  

The course is designed to train and equip the students to carry out research. The course enables the students to 

• Gain knowledge of core research techniques which forms a basis for understanding and critical analysis of the published work in economics. 
• Develop the analytical skills required to conduct research in the economics discipline. 

There is only CIA for this paper. Research work carried out in this semester is divided in two parts. 

Part A constitutes data collection and pre-processing in which students should carry out the following tasks and submit the document for the same before the MSE. 

Literature survey of existing data sets or any primary data sets in the respective area 

● Gather the datasets from various sources (like visiting websites, universities, person, creating individually, etc.) 

● Steps in pre-processing 

Part B constitutes modelling and implementation of their research work. Students should perform the following tasks: 

● Methodology 

● Evaluation and Discussion of Results 

● Limitations, Conclusions and Scope for future enhancements 

● Plagiarism report 

1. Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage learning. 

2. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press. 

3. Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2009). Handbook on impact evaluation: quantitative methods and practices. World Bank Publications. 

4. Gujarati, D. N. (2021). Essentials of econometrics. SAGE Publications. 

5. Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics. Princeton university press. 

Research articles and publications from peer-reviewed journals and established government reports. 

CIA II-10

CIA III-30

CIA 2-25%

CIA 3-20%

ESE - 50%

Attendance 5%

The objective of this course is to present an introduction to public economics, which analyzes the impact of public policy on the allocation of resources and the distribution of income and wealth in the economy. We study various market failures as a justification for public action, and analyze how government policies can improve market outcomes. We address the financing side of government and consider key issues in tax policy along with contemporary issues in redistribution and social policy. Finally, selected analytical tools in public economics are discussed.

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

• The objective of the course is to make the students aware of the role of government in the broader economy.
• The course would help the students to analyze the impact of government taxation on public expenditure.
• The course would help the students to understand the various roles of fiscal institutions.
• The students would get to understand and debate on the budgetary policies in general and Indian experience in particular.

Public Economics, Theory and Policy, Positive Versus Normative Analysis of Government Action Pareto-Criterion, Theory of The Second Best, General Vs. Partial Equilibrium Theory, Theorems of Welfare Economics

Economics of Externalities, Properties of Public Goods, Optimum’s Provision of Public and Private Goods, Political Economy: Lindahl Pricing, Voting Theory-Arrow’s Impossibility Theorem, Clarke-Grove’s Mechanism,

Taxation In Economics and Around the World, The Equity Implications of Taxation: Tax Incidence, Tax Inefficiencies and Their Implications for Optimal Taxation, Taxation and Evidence: Commodity, Income and Capital

Social Insurance and Security: The New Function of Government, Health Economics and Private Health Insurance, Private Saving for Retirement, Political Economy of Redistribution, Income Distribution and Welfare Programs

Constrained Utility Maximization, Cost-Benefit Analysis, Decision Criteria for Benefit–Cost Analysis, Policy Analysis Involving Risk and Uncertainty, The Expected Utility Model, Risk Aversion and The Willingness to Pay

2. Myles, G. D. & Hindriks, J. (2013). Intermediate Public Economics. MIT Press Limited.

2. Bellinger, W. K. (2015). The Economic Analysis of Public Policy. Taylor & Francis.

3. Varian, H. R. (2019). Intermediate Microeconomics with Calculus. W.W. Norton.

The course enables the students to

● Familiarize students with the financial system and its components viz. financial instruments, financial institutions, financial markets and financial regulations.

● Acquaint them with contemporary theories about the workings of different financial markets including money market, capital markets (bonds, stocks and hybrids) and derivative markets.

● Introduce them with the policy and regulatory framework within which financial institutions are required to function.

The time dimension – Present value and duration – The calculation of yields on zero-

coupon bonds – The term structure of interest rates – The risk dimension –

 Measurement of risk. Bivariate distributions–Conditional probabilities and expected

values – Estimating the mean and variance of returns – Expected utility.

– General characteristics of securities – Government bonds – Index-linked bonds –

Corporate Securities – equities, bonds, convertible securities – Stock market

operations – Money market funds – Claims on financial institutions.

The over the counter stock market – Operational efficiency and the Efficient Market

Hypothesis(EMH) – The weak, semi-strong and the strong form of EMH.

Shares as claims on future dividends and on corporate net worth – The Capital Asset

Pricing Model (CAPM) – The simplest form – Estimating betas- Implications for

portfolio management – Validity of CAPM – Arbitrage Pricing theory. An alternative

approach – Stock indices – Bombay Sensitive Index, Bombay National Index, Dow

Jones Industrial Index(DJI), New York Stock exchange composite index(NYSE).

A modern view of security analysis – Macroeconomic developments and securities

markets– Performance of securities markets – Industry growth, structure and firm

specific factors- Uses and pitfalls of Price / Earnings ratios.

Derivatives Uses of Derivatives – Futures contracts and futures markets – Forward

contracts – The origins of Futures trading – Basic elements and organization of futures

contract.

McGraw Hills Education.

2. Shapiro, A. C. (2012), Multinational Financial Management (9th edition), Wiley.

3. Campbell, J. Y., Lo, A. W., & Mackinlay, A. C. (1997). The Econometrics of

Financial Markets (2nd edition) Princeton University Press.

Oxford University Press

2. Eichberger, J and Harper, I. R. (1997), Financial Economics, Oxford University

Press

3. Ross, S. A., & Westerfield, R. (2018), Fundamentals of Corporate Finance (12th

edition), McGraw Hill Education.

4. Fabozzi (2009), Bond Markets (7th revised edition), Pearson Publications, USA,

February 27

5. Fama E.F. (1970), Efficient Capital Markets: A Review of Theory and Empirical

Work, Journal of Finance, 25 May, pp. 383-417

6. Fama, E. F. (2021). Efficient capital markets II (pp. 122-173). University of Chicago

Press.

7. Graves, Affleck, Hegde, J.S. & Miller, R. (1994),Trading Mechanisms and the

Components of the Bid Ask Spread, Journal of Finance, 44, pp. 1471-1488.

8. Barsky, R. and Long, J. De (1993), Why Does the Stock Market Fluctuate,

Quarterly Journal of Economics, 108, pp. 291-311

9. Black, F., Jensen, M.C. & Scholes, M.A. (1972),The Capital Asset Pricing Model: Some

Empirical Tests in M.C. Jensen (ed.), Studies in the Theory of Capital Markets,

Praeger, New York.

CIA 2- 25

CIA 3- 20

ESE- 30

Attendance- 5 

The course provides a deep understanding about the broad principles and theories, which tend to govern the flow of trade in goods, services and capital — both short-term and longterm — at the global level. The contents of the course help them to examine the impact of the trade policies followed both at the national and international levels as also their welfare implications at macro level and the distribution of gains from trade to North and South with particular reference to India, also the likely consequences on income, employment and social standards and possible policy solutions

Inter-regional versus international trade - Mercantilist doctrine of the balance of trade - Adam Smith and absolute advantage theory of trade - Ricardo and comparative advantage, its limitations - production possibility curve - Community indifference curve. The Standard Theory of International trade-The PPC with increasing cost, Community Indifference Curves and Equilibrium, The Basis for and Gains from Trade-Small country with increasing cost, Demand and Supply, Offer Curves and Terms of Trade (ToT), Trade flows, Economic Geography and the Gravity model

Comparative advantage in Heckscher Ohlin Model - definitions of factor abundance - relationship between factor prices and commodity prices - Factor price equalization theorem - Factor intensity reversal - the empirical evidence on Heckscher Ohlin theory-the Leontief Paradox

Economies of Scale and International Trade, Imperfect Competition and International TradeTrade Based on Product Differentiation, Measuring Intra-Industry Trade, Trade Based on Dynamic Technological Differences- Product Cycle Models, “New new” trade theory: Melitz Model and extensions

The rationale of tariffs- infant industry argument Partial equilibrium analysis of a Tariff, General Equilibrium Analysis of a Tariff in a Small Country The Stolper–Samuelson Theorem Effective Protection and Optimum Tariff, Non-tariff Barriers- quotas and subsidies, VER, Dumping, Economic Integration-Customs Unions and Free Trade Areas, Trade creation and Trade diversion , The Theory of second best

The Balance of Payments and National Account; Accounting Balances and the Balance of Payments, Case Study: The BoP of India Exchange Rates: Meaning, Determinants, Equilibrium in FX market, Spot and Forward Rates, Currency Swaps, Futures, and Options, Purchasing Power Parity; Sluggish Price and Overshooting Exchange Rate Model; Effect of Interventions in the Foreign Exchange Market, The Exchange-Rate Regime Choice and a Common Currency Area: Policy Assignment Problems; International Policy Coordination International Trade & Financial Organizations- The International Trading and Monetary System: Past, Present, and Future, The Role of the IMF, WTO, and Other International Financial Organizations

2. Grossman, G. M. & Rogoff K. (1997). Handbook of International Economics (Vol. 3). 52 North-Holland.

2. Leamer, E.(2001). International Economics. Worth Publishers.

3. Markusen, J. R., Melvin, J. R., Kaempfer, W. H. & Maskus, K. E. (1995). International Trade: Theory and Evidence. McGraw-Hill.

4. Sodersten, B. & Reed, G.(1994). International Economics (3rd ed.). Macmillan.

5. Appleyard, D. &Field, J. (2013). International Economics. McGraw-Hill.

6. Vanags, A. (2001). International Economics. University of London, Subject Guide.

7. Ethier, W.(1997) Modern International economics (3rd ed.). W.W. Norton &Co.

8. Winters, A. P.(1991) International Economics (4th ed.). Routledge.

9. Bhagwati, J. N. (1987). International Trade: Selected Readings (2nd ed.). Cambridge,MA: MIT Press.

10. Bhagwati, J. N., Panagariya, A. & Srinivasan, T. N. (1998). Lectures on International Trade (2nd ed.). MIT Press.

11. Cline, W. R. (1997). Trade and Income Distribution. Institute for International Economics.

12. Cohen, S. D., Blecker, R. A. &Whitney, P. D.(2003).Fundamentals ofU.S. ForeignTrade Policy: Economics,Politics, Laws, and Issues (2nd ed.). Westview.

13. Collins, S. M. (Ed.) (1998). Imports, Exports, and the American Worker. Brookings Institution.

14. Dosi, G., Pavitt, K. & Soete, L. (1990). The Economics of Technical Change and International Trade. NYUPress.

15. Findlay, R. (1995). Factor Proportions, Trade, and Growth. MIT Press. Foray, D. & Freeman, C. (Eds.) (1993). Technology and the Wealth of Nations: The Dynamics of Constructed Advantage.Pinter

The course discusses about fundamental models used to analyse theoretical and empirical issues in economic growth and development. The main objective of the course is to familiarize students with the problem of development in underdeveloped and developing economies. In addition, this course also discusses the major theoretical developments in areas of Growth economics and policy discourses.

How the Other Half Live; Economics and Development Studies; What Do We Mean by Development-Traditional Economic Measures; The New Economic View of Development; Amartya Sen’s “Capability” Approach; Development and Happiness ; Three Core Values of Development;; The Three Objectives of Development. The Millennium Development Goals; Sustainable Development Goals.

Four Approaches of classical theories of growth; Development as Growth and the Linear

Stages Theories ; Rostow’s Stages of Growth; The Harrod- Domar Growth Model; Obstacles and

Constraints Necessary versus Sufficient Conditions: Some Criticisms of the Stages Model;

Structural-Change Models-The Lewis Theory of Development;:Structural Change and Patterns

of Development; The International-Dependence Revolution- The Neocolonial Dependence 

Growth Models with Exogenous Saving Rates (the Solow–Swan Model); The Fundamental Equation of the Solow–Swan Model; The Steady State; The Golden Rule of Capital Accumulation and Dynamic Inefficiency; Ramsey model with infinitely lived agents- Application to household behaviour and interaction with the government - Ricardian equivalence; Models of Endogenous Growth-Theoretical Dissatisfaction with Neoclassical Theory- The AK Model - Long run AK model – AK model with externalities New Growth theory: Human capital, Externalities and ideas; endogenous technological progress and development.

Theories of endogenous growth with special reference to Romer‘s modl, underdevelopment as coordination failure, multiple equilibria, the big push theory and Lebenstence Theory of Critical Minimum Efforts.

Two concepts of convergence- Convergence across the US states, Japanese Perfectures, European regions and other regions across the world- recent research works 

2. Todaro, M.P. & Smith S.C. (2015). Economic Development (12th ed.). Addison-Wesley.

3. Meier, G. M. & James E. R. (2005). Leading issues in Economic Development (8th ed.). Oxford

University Press.

4. Thirlwall, A.P. (2006). Growth and Development (8th ed.). Palgrave Macmillan.

2 . Pomeranz, K. (2000). The Great Divergence: China, Europe and the Making of the Modern

World. Princeton University Press.

CIA 2- 25

CIA 3- 20

ESE- 30

Attendance- 5

The course covers Multiple Regression, Model Specification, Qualitative Response Models, Time Series Econometrics and Panel Data Econometrics with a focus on applications in the field of economics in general. The course is meant to equip students with Applied part of the subject matter. The objective of the course to develop a comprehensive set of tools and techniques for analyzing various forms of Cross-sectional, Time Series and Panel Data frameworks.

Simple and multiple linear regression model – Assumptions; OLS and properties of estimators; Gauss-Markov theorem; partial regression coefficients; The coefficient of determination r2 and the adjusted r2. Hypothesis testing - The Confidence-Interval Approach, The Test-of-Significance Approach, and the p-value approach. 

Model selection criteria, types of specification errors, omission of relevant variables, inclusion of irrelevant variables, incorrect functional form, errors in measurement, tests of specification errors. 

The Nature of Qualitative Response Models, The Linear Probability Model (LPM), applications of LPM, alternatives to LPM – Logit and Probit models. 

Nature of the time series data, stationarity, testing stationarity- graphical analysis, correlogram, unit roots tests - ADF and PP, Spurious regression, Cointegration and Error Correction Mechanism. 

Nature of Panel data, Panel data models and estimation techniques: pooled OLS regression, fixed and random effects models. 

Maddala, G. S. (1992) Introduction to Econometrics (2nd Ed.) Macmillan Publishing Company. 

Gujarati, D. N., Porter, D.C., & Gunasekar, S. (2017). Basic Econometrics. (5th ed.). McGraw-Hill. 

Wooldridge, J. M. (2014). Introductory Econometrics: A Modern Approach (4th ed.). New Delhi: Cengage Learning 

CIA2-25 Marks

CIA3-20 Marks

Attendance-5 Marks

ESE-30

This course enables students to

● Understand the differences between supervised and unsupervised machine learning models

● Optimize the models and understand the effect of algorithm parameters’ modification

● Combine various models and create strategies to overcome commonly faced challenges in machine learning algorithm implementation

● Implement machine learning models in various economics-related applications 

Learning: Human Comprehension, Cognition, Past Experiences, Predicting Future, Thought Process, Inputs and Output System, Human Memory, Information, Machine Comprehension of Information, Data, Representation of Data, Data Processing, Data Storage, Data Processing, Types of Data: Structured, Unstructured, Semi-Structured, Human-Machine Mapping of Peripherals Machine Learning: Definition, Objectives, Components, Features, Applications. Traditional Programming v/s Machine Learning, Types of Machine Learning: Supervised, Unsupervised, Semi-supervised and Reinforcement Learning, Predictive Models, Techniques, Statistical Inferences Subsets of Machine Learning: Natural Language Processing, Image Processing, Computer Vision, Robotics, Export Systems, Neural Networks, Deep Learning, Generative Networks, Big Data Analysis, ASR, Text-to-speech, Extreme Learning Machines, Genetic Algorithms, Optimization Problems, Latest Advancements in Artificial Intelligence and Machine Learning, Learning from Examples  

Decision Functions, Distance Measures, Outliers, Gradient Descent, Probably Approach Correct (PAC) Learning Regression and Classification Problems, Linear Regression, KNN for Regression, Types of Classification Methods, Multi-Class and Multiple Class Classification, Logistic Regression, Distance-Based Classification, K-Nearest Neighbors, Histogram Estimators, Naive Bayes Classifier, Decision Trees, Support Vector Machines, Generalization to Multivariate Data, Uncertainty in Multi-class Classification, Notes on Imbalanced Classification Probabilistic Approaches in Machine Learning, Maximum Likelihood Estimation, Bernoulli Density, Multinomial Density, Gaussian Density, Bayes Estimator, Parametric Classification. 

Types of Unsupervised Learning, Challenges in Unsupervised Learning, Distance Measures in Clustering, Elbow Method, Types of Clustering: Connectivity-based, Centroid-based, Distribution-based, Density-based, Fuzzy Clustering, and Constraint-based Clustering, K-Means Clustering, Spectral Clustering, Hierarchical Clustering, DBSCAN, Mixture Densities, Expectation-Maximization Algorithm, Comparison of Clustering Algorithms, Supervised Methods after Clustering Subset Selection, Principal Component Analysis (PCA), Independent Component Analysis, Linear Discriminant Analysis (LDA), Using Low Variance and High Correlation Filter, Multidimensional Scaling, Random Forest, Backward Feature Elimination, Forward Selection, Isomaps, Singular Value Decomposition, Applications of Dimensionality Reduction Combining Multiple Learners: Ensemble Learning, Voting, Weighted Averages, Stacking, Bagging, Boosting, AdaBoost, CatBoost. 

Challenges in ML: Insufficient Quantity of Data, Non-representative and Poor Quality Data, Irrelevant Features, Estimation of Missing Values, Parameter Estimation Preparing Data for ML Algorithms: Generalization, Normalization, Sampling, Overfitting, Underfitting, Bias, Variance, Bias-Variance Tradeoff, Relation between Bias, Variance Overfitting and Underfitting, Learning Noise in a Dataset, Ideal Machine Learning Model, Process of Model Selection, Effect of Data Preparation on Results, Encoding, Nominal and Ordinal Representation, Feature Transformation and Feature Engineering, Feature Selection - Various Methods Testing and Validating: Performance measures - Confusion matrix, Precision-Recall Tradeoff, F1 Score, ROC Curve, Error Analysis, Changing Parameters of Algorithm, Hyperparameter Tuning, Model Selection, Algorithm Tuning

Evaluation of Time-Series Data, Big Data Analysis, Stock Exchange Analysis, Role of Recurrent Neural Networks in Time Series Data Prediction, Modelling Supply and Demand Functions, Forecasting, Identifying Loan Defaulters, Decision Making in Financial Sectors, Impact in Agricultural Economics

Lab Programs Hours: 

1. Introduction to Machine Learning Libraries in Python

2. Data Exploration and Prediction of Numeric Values

3. Classification - Logistic Regression

4. Classification - KNN, Naive Bayes

5. Classification - Support Vector Machines

6. Clustering - K Means, Elbow Method

7. Clustering - Agglomerative Clustering

8. Dimensionality Reduction - PCA and LDA

9. Analysis of Time Series Data - Stock Market Prediction

10. Analysis of Supply and Demand Based Applications

11. Identifying Insurance/Loan Defaulters

12. Geographic Location based Data Analysis

2. Alpaydin, E. (2005). Introduction to Machine Learning. Prentice Hall of India.

3. Russell, S. & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

2. Hastie, Tibshirani & Friedman (2008). The Elements of Statistical Learning (2nd ed.). Springer.

3. Basuchoudhary, Atin, Bang, James, T., Sen, Tinni (2017). Machine-learning Techniques in Economics. Springer International Publishing.

The course enables students to

● Understand and Analyze Visualization case studies

● Understand the nuances of Data Visualization

● Analyze data using Tabular Reports and Charts

● Catch and Debug data errors

● Visualize Data to derive actionable insights

● Create reports based on specifications

● Study patterns in data using charts

● Derive Actionable insights from raw data

Foundational principles of Tableau, basics of connecting to data, exploring and

analyzing the data visually, examine and filter data, clean and shape data, joins and

blends. Connecting to data sets, data section and worksheets

Data Science overview, Data Visualization, modelling, data preparation, communication

and presentation. Descriptive, predictive and prescriptive analytics. Techniques for

making Data Visualization useful and beautiful. Case Study: (Minard’s Map, Broadstreet

Cholera Map, Interactive Government Budget, US Population study, Film Dialogue, Selfie

City)

Marks and Filters, Charts Types: Scatter, Bar, Line, Gantt, Heat Map, Tree Map,

creating calculated field, dates in tableau, jittering, multiple mark types, and dual axis

chart.

Working with Time Series, Understanding Aggregation and Granularity, Area Charts,

Highlighting and Filters, Animation

Additional Charts: Pareto Chart, Box and Whisker plot, Map Chart, Calculate Z Score,

Population Pyramid

Trends, Clustering, Distributions, and Forecasting, enhance your data visualizations

with statistical analysis. Trend models, clustering, distributions, and forecasting

Telling a Data Story with Dashboards, demonstrates how Tableau allows you to bring

together related data visualizations in a single dashboard. Static view of various aspects

of the data, or a fully interactive environment (dynamically filter, drill down, and

interact with the data visualizations)

1. Install Tableau and create different kind of basic charts on existing dataset in Tableau

(like Superstore) Scatter, Bar, Line, Gantt, Heat Map, Tree Map

2. Create advanced charts on existing dataset

Pareto Chart, Box and Whisker plot, Map Chart, Population Pyramid

3. Design and auto generate a dataset on an assigned domain (using Python or Excel)

4. Identify key reports in the assigned domain, Create and visualisekey reports

5. Creating a Dashboard to support a business case, use a couple of calculated fields in

the same

6. Designing a Key Performance Indicator (KPI) (eg. Best Striker in Football in EPL,

Team Ranking in Cricket)

7. Telling a story using visual reports and a dashboard

8. Collate all work done in this course into a single pdf based report

2. Sleeper, Ryan(2018). Practical Tableau: 100 tips, tutorials, and strategies from a

Tableau Zen master. " O'Reilly Media, Inc."

2. Knaflic, Cole Nussbaumer(2019),Storytelling with Data: Let's Practice!. John Wiley &

Sons,

3. Ware, Colin(2010). Visual thinking for design. Elsevier

The course is designed to provide a real-world project development and deployment environment

for the students.

·      Project Topic identification 

·      Identify the contributing research papers, domain of work and specialization concept to be implemented

 Final Report preparation

Final Report Submission

The Vision of Christ University is “Excellence and Service” and this can be achieved through the holistic development of individuals enabling effective contribution to society. Christ University provides the nurturing ground for all stakeholders to realize academic, personal, interpersonal and societal growth and upliftment. Industry internship focuses on learning by doing and making students more responsible and dynamic so that they can harness their hidden potential and get ready to take up tasks and challenges of the industry with confidence and motivation. Industry Internship provides students with exposure to life beyond academics enabling them to solve real-life problems. It provides students with practical knowledge of the application of Economics and Analytics in the industry and also the importance of discipline, hard work and dedication. This internship aims to widen the horizons of the students to make informed decisions regarding their future and career. The students shall learn new skills and make good professional interpersonal relationships. The skills learned during the Industry Internship will also have a bearing on students’ placements and career planning. 

Internship Report:

The student shall work with the organization as an intern for a period of 4 months and submit an internship report of 2000 words in consultation with the allotted faculty guide. The student shall ensure regular weekly contact with the faculty guide during the entire period of the internship. The student will report to the faculty guide every week and apprise him/her of the weekly progress of the internship. In addition to the submission of the internship report, the student will also present his internship report before a panel of examiners followed by a Question and Answer session.

NA

Research Publication enables students to raise their intellectual abilities and contribute to the existing literature through research. It also enables them to dive deeper into researchable problems and come up with novel ideas and bring them to the forefront by publishing them as a research article/paper in esteemed research journals.

The student will be allotted a faculty guide to supervising the research work. The students are expected to undertake quality research in consultation with the faculty guide. The student will be in regular contact with the faculty guide and try to complete the research work in the stipulated time. The faculty guide will guide the student in all matters related to the finalization of the topic, writing of the research article/paper, analysis of data, selection of the journal and communications with the journal.

NA