Syllabus for
Master of Science (Economics and Analytics)
Academic Year  (2023)

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% 

Meaning and Definition, Scarcity, Resources and Opportunity Cost. Production Possibility Frontier, Micro vs. Macro Economics, Analyzing Economic Problems, relevance of microeconomics, key analytical tools..  Demand and Supply Analysis  Concept of Demand, -Determinants of Demand, Law of Demand, Movement along vs. Shift in Demand Curve; Elasticity of Demand Supply-Meaning, Law of Supply, Exceptions to the Law of Supply, Changes or Shifts in Supply. Elasticity of supply, Factors Determining Elasticity of Supply, Other Elasticities, Elasticity in the long run versus short run.

Consumer Preferences and the concept of utility- Representation of preferences, assumptions about consumer preferences, ordinal and cardinal ranking, utility functions: marginal utility, total utility, indifference curves, Marginal rate of substitutions (MRS); Special preferences: perfect substitutes, perfect complements, the Cobb- Douglas utility function. Consumer Choice- The budget constraint, change in price   affect the budget line, change in income affect the budget line, Optimal choice, consumer choice with composite goods.

The theory of Demand- Optimal choice and demand (change in price and change in demand), substitution effect and income effect (Hicks and Slutsky), consumer surplus, ordinary and compensated demand curves, inferior goods and Giffen goods, price consumption and income consumption curves,), Revealed Preference Hypothesis (weak axiom and substitution). Case Studies on: Inferior goods, Compensated Demand Curves, Application of Hicks and Slutsky

Theory of cost: Traditional and Modern, Iso-cost line cost minimization and expansion path of linear homogenous Production Function, Effect of changes in factor prices: factor substitution, substitute and complementary factors. Laws of production: Returns to scale, law of variable proportion, economies of scale, Technological progress and production function, Graphical derivation of cost curves from the production function, Production Possibility Curve of a firm Production Function: Cobb Douglas, self- work on: CES Elasticity of Supply. Cases of real world business models with least or no fixed cost   Concept of revenue: Marginal and Average : Revenue under conditions of perfect and imperfect competition.

Market Equilibrium and Changes in Market Equilibrium. Market Equilibrium and Government Policies .  Characterizing perfect competition; Pricing and output under perfect competitive markets; Monopoly markets: Pricing, Multidimensional business platform discrimination; welfare costs; Monopoly Market structures: Multiplant firm, price discrimination and effects of price discrimination, price discrimination and elasticity of demand, group discussion on: Government regulated Monopoly, degree of monopoly power Monopolistic competition: Characteristics; Long run and short run behavior. Case studies and discussions on the current scenario of the market.

7.     Henderson, J.M. and R.E. Quandt (2003), Microeconomic Theory: A Mathematical Approach, McGraw Hill, New Delhi.

Mid Sem- 25%

CIA III-20%

END Sem- 30%

Attendance

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 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.

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.

MID Term- 25%

CIA III- 20%

END Term- 30%

Attendance- 5%

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

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

ESE 30%

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 InjectivityRange 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. 

ESE 30%

This course enables the students to

●      Understand the importance of research in creating and extending the knowledge base of their subject area;

●   Distinguish between the strengths and limitations of different research approaches regarding their subject/research area

●      Know the range of qualitative and quantitative research methods potentially available to them;

●      Differentiate between the role of practitioners and the role of researchers;

●      Understand and analyze critically reflect upon issues of ethics and role of the researcher; 

●    Independently work, to plan and to carry out a small-scale research project.

Definition and objectives of research– types of research; Steps in research process; Criteria of good research; Characteristics of a good research problem. Ethical issues in research.

 Review of Literature - The place of the literature review in research; Identification of Research Gaps; Meaning and sources of a research problem; Identification, selection and formulation of research problem.

Concept of Population and a sample; Sampling techniques ; Types and sources of data; Methods of data collection; Design of questionnaire; Characteristics of a good questionnaire, Problems in data collection; Measurement scales.

Concept of Null and Alternative hypothesis, Testing of hypothesis; Type I and Type II error. Important parametric tests– applications of z– test, t– test, chi– square test. Presentation of research findings, writing a research report, Referencing and bibliography.

2.  Kothari C. R. (2019). Research Methodology: Methods and Techniques (4th Edition), New Age International Publishers.

2.   Bairagi V. & Munot M. V. (2019). Research Methodology-A Practical And Scientific Approach. CRC Press Taylor & Francis Group.

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 Mutability- Problem Solving Using Lists and Functions.

Lab Programs:- 

1. Demonstrate usage of branching and looping statements

2. Demonstrate Recursive functions

3. Demonstrate Lists

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

Lab programs :-

4. Demonstrate Tuples and Sets

5. Demonstrate Dictionaries

6.Demonstrate inheritance and exceptional handling

7. Demonstrate use of “re”

Basics of NumPy - Computation on NumPy-Aggregations-Computation on Arrays- Comparisons, Masks and Boolean Arrays-Fancy Indexing-Sorting Arrays-

Structured Data: NumPy’s Structured Array.

Lab programs :-

8. Demonstrate Aggregation

9. Demonstrate Indexing and Sorting

Introduction to Pandas Objects-Data indexing and Selection-Operating on Data in Pandas- Handling Missing Data-Hierarchical Indexing - Combining Data Sets

Aggregation and Grouping-Pivot Tables-Vectorized String Operations -Working with Time Series-High Performance Pandas- and query().

Lab Programs:- 

10. Demonstrate handling of missing data

11. Demonstrate hierarchical indexing

12. Demonstrate usage of Pivottable

13. Demonstrate use of andquery()

Basic functions of Matplotlib- Simple Line Plot, Scatter Plot-Density and Contour Plots- Histograms, Binnings and Density-Customizing Plot Legends, Color Bars-Three- Dimensional Plotting in Matplotlib.

Lab programs:- 

14. Demonstrate ScatterPlot

15. Demonstrate 3D plotting sciPy

Factor Pricing 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, Externalities  and tragedy of commons .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 behavior of firms in a market–Bertrand, Cournot and Stackleberg models – and entry deterrence. 

Macro theories of distribution – Ricardian, Marxian, Kalecki and Kaldor’s. Theories of Profit (Clarks dynamic theory ,  Schumpeter’s Innovation theory of profit, Risk and Uncertainty theory of Profit ) 

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.

2.       Henderson, J.M. and R.E. Quandt (2003), Microeconomic Theory: A Mathematical Approach, McGraw Hill, New Delhi.

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.

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

CIA II: 25

CIA III:20

ESE: 30

Att: 05

 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 – Classical theory of interest, Neo-Classical theory of interest,  Keynesian liquidity preference theory, Wicksellian theory, Fisher’s theory, Modern theory of interest (Hicksian-Hansen theory), Credit market imperfections - Adverse selection and moral hazard.

The Classical view of the demand for money, Keynes view of the demand for money, Post-Keynesian Approaches – Friedman’s approach, Baumol’s approach to the demand for money, Tobin’s portfolio optimization approach. Meaning of The term structure of interest rates, Theories of The term structure of interest rates - Pure Expectations, Pure segmentation and Substitutability theories.

Meaning and Phases of Business cycle, Theories of business cycle – Sunspot theory, Psychological theory, Overproduction theory, Oversaving Theory, Hawtrey’s theory, Hayek’s theory, Sameulsons’s theory, Hicks’s theory, Goodwin’s theory, and Kaldor,s theory. Real Business Cycle Theory, Inter temporal substitution of labour, Technology shocks, neutrality of money and flexibility of wages and prices. The modern monetarism, major postulates, Keynesian policy framework- The New Classical macroeconomics, The Supply-Side economics-major implications..                                            

 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).  Macroeconomics,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

Mid-Term-25%

CIA-III- 20%

END Term-30%

Attendance-5%

On completion of the course students should be able to:

●      Understanding of simple and multiple linear regression, its assumptions, and the impact of violations of its assumptions.

●      Developing their proficiency with the econometric software like EViews and Stata required to model economic data in practice.

●      Formulating, estimating, testing, and interpreting suitable models for the empirical study of economic events.

*    Ability to evaluate the performance of alternative econometric models through the appropriate use of tests.

Meaning of econometrics- why a separate discipline, methodology of econometrics, the concept of population regression function (PRF) and sample regression function (SRF), statistical versus deterministic relationships, the significance of the stochastic disturbance term, regression versus correlation, regression versus causation, terminology and notation in econometric analysis.

Simple and multiple regression, Assumptions, Gauss-Markov theorem, Approaches to hypothesis testing – individual and joint significance, partial effects, and elasticity, R-squared and Adjusted R-squared. 

Problems of Regression Analysis - Multicollinearity, Heteroskedasticity, and Autocorrelation; Nature of these problems, causes, consequences, detection,, and remedy.

Attributes of a good econometric model, model selection criteria, types of specification errors, consequences of model specification errors, tests of specification errors – Omitted variables test, Redundant variable test, Ramsey RESET test. 

The nature and use of Dummy Variables, ANOVA models, Dummy variable trap and perfect multicollinearity, structural stability of regression models- Chow test, The Dummy Variable Alternative to the Chow Test.

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

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

3.          Gujarati, D. N. & Porter, D. C. (2010). Essentials of Econometrics, 4th McGraw Hill International Edition.

4.          Brooks, C. (2019). Introductory Econometrics for Finance 4th Edition Cambridge University Press.

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

6.          Dougherty, C. (2016). Introduction to Econometrics, 5th Edition. Oxford University Press.

7.          Patterson K. (2000). An Introduction to Applied Econometrics: A Time Series Approach. Palgrave Macmillan.

8.          Asteriou, D., & Hall, S. G. (2021). Applied Econometrics (4th ed.). Red Globe Press.56

9.          Gujarati, D. (2015). Econometrics by Example, (2nd Edition). Palgrave Macmillan.

10.       Hilmer, C. E. & Hilmer, M. J. (2014). Practical Econometrics. McGraw Hill Education.

11.       Ramanathan, R. (2002). Introductory Econometrics with Applications, 5th Edition. Thomson Asia Private Limited.

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-20 (External)

The Course enables students to

• Introduce the historical development of statistics, presentation of data, descriptive measures and fitting mathematical curves for the data.
• Introduce the measurement of the relationship of quantitative and qualitative data and the concept of probability.
• Understand and apply the descriptive measures and probability for data analysis.

Bivariate Normal Distribution (BVN): p.d.f. of BVN, properties of BVN, marginal and conditional p.d.f. of BVN. Multivariate Data: Random Vector: Probability mass/density functions, Distribution function, Mean vector & Dispersion matrix, Marginal & Conditional distributions.

Multivariate Normal distribution and its properties. Sampling distribution for mean vector and variance- covariance matrix. Multiple and partial correlation coefficient and their properties.

Applications of Multivariate Analysis: Discriminant Analysis, Principal Components Analysis and Factor Analysis.

Nonparametric Tests: Introduction and Concept, Test for randomness based on total number of runs, Empirical distribution function.

Kolmogrov Smirnov test for one sample, Sign tests- one sample and two samples, Wilcoxon-Mann-Whitney test, Kruskal-Wallis test.

2. Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis (3rd ed.) John Wiley.

3. Muirhead, R. J. (1982). Aspects of Multivariate Statistical Theory. John Wiley.

4. Kshirsagar, A.M. (1972). Multivariate Analysis (1st ed.) Marcel Dekker.

2. Johnson, R.A. and Wichern, D.W. (2007). Applied Multivariate Analysis (6th ed.) Pearson & Prentice Hall

3. Gibbons, J. D. and Chakraborty, S (2003). Nonparametric Statistical Inference (4th ed.) Marcel Dekker, CRC.

ESE: 30%

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 Demand for Securities

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.

The Supply of Securities Regulations Governing Supply of Securities

– 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.

Securities Markets and Efficiency of Stock exchanges

The over the counter stock market – Operational efficiency and the Efficient Market Hypothesis(EMH) – The weak, semi-strong and the strong form of EMH.

The Determination of Equity Prices

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).

Security Analysis and Market Efficiency

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.

Financial Instruments

Derivatives Uses of Derivatives – Futures contracts and futures markets – Forward contracts – The origins of Futures trading – Basic elements and organization of futures contract.

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.

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.

ESE - 30%

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.

3. Salvatore, D. (2019). International economics. John Wiley & Sons.

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

16. Foray, D. & Freeman, C. (Eds.) (1993). Technology and the Wealth of Nations: The

Dynamics of Constructed Advantage. Pinter.

The course discusses fundamental models used to analyze 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.

Meaning of Economic Growth and Economic Development. Measures of Economic Development- GDP, GDP per capita, PQLI and HDI, 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; Meade’s Model of Economic Growth, Kaldor’s Model of Growth, 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 model, underdevelopment as coordination failure, multiple equilibria, the big push theory and Lebenstence Theory of Critical Minimum Efforts.

The Capital Controversy, The Neo-Classical Capital Theory- Assumptions, The Cambridge Criticisms, Joan Robinson’s Critique, Samuelson’s Critique, and the Surrogate Production Function, convergence and its types.

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

MID-Term- 25%

CIA 3- 20%

ESE- 30%

Attendance- 5

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. 

To provide the students with an in-depth understanding of the work done by some of these scholars and practitioners, and make them an expert at understanding, diagnosing, and designing behavioural change interventions that help people make better decisions and achieve policy or social welfare outcomes.

What is behavioural economics? - History and evolution- relation with other disciplines objectives, and scope- themes and methodology of behavioural economics (theory, evidence, consilience) – application. Anticipation and information avoidance as introductory example.

Values, preferences and choice- believes- heuristic and biases- state dependent preferences (such as habit formation and addiction)- mis-prediction and projection bias-anticipation and information avoidance-decision making under risk and uncertainty- prospect theory- the role of reference- dependent preference in both risky (loss aversion) and risk free (endowment) choices-mental accounting- applications. How do people care about those around them? Both distributional social preferences (altruism, inequality aversion) and intentions-based social preferences (reciprocity, fairness). The possibility of self-deception.

The discounted utility model (origin, features, methodology, anomalies with discounted utility models)- alternative inter temporal choice models (time preferences, time inconsistent preferences- hyperbolic discounting- modifying the instantaneous functions)- applications. How do people make predictions about their own future utility? State-dependent preferences (e.g. habit-formation and addiction) and projection bias.

Behavioural game theory (nature, equilibrium, mixed strategies, bargaining, iterated games, signalling, learning)- application Modelling of social preferences –nature and factors affecting social preferences distributional social preferences based on altruism, inequality aversion models- reciprocity models, evidence and policy implications. How do people make predictions about their opponents in strategic interactions? Models of limited social inference (level-k reasoning, cursedness).

Nudges, Policy, and Happiness- the application. How and when should governments intervene if people are “behavioural”? The theory of nudges, and happiness as an outcome.

What are animal spirits? How does Human Psychology drive the economy? We overview five different aspects of animal spirits and how they affect economic decisions

Mid Sem- 25%

CIA III-20%

End Sem- 30%

Attendance -5%

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,

●        Create an Interactive dashboard from different datasets. 

●        Illustrate importing data from different data sources and then learn to clean data with tool.  

●        Visualize data using graphs and plots. The graphs so build can give you the overall information of the data. 

Query design, SQL Views, M Queries, Query Folding, M query Examples, M editing tools

The data model, Relationships, Model Metadata, Optimizing Performance 

Power BI deployment models, Dataset design process, Dataset Planning 

Report Planning, Live Connections to Power BI datasets, Visual interactions, Slicers, Report filter scopes, Visualization Formatting, Map Visuals, Mobile optimized reports 

Drillthrough report pages, Bookmarks, Waterfall chartbreakdown, Analytics Pane, Custom Visuals, Animation and Data Storytelling 

Dashboard Vs Reports, Dashboard Design, Multi-dashboard architectures, Dashboard titles, Live report pages, Mobile optimized dashboards

Power BI Apps, Sharing dashboards and reports, SharePoint Online embedding, Custom application embedding, Publish to Web Power BI Admin Portal

1.      Create workspace

2.      Set up power BI desktop option

3.      Get data from SQL Server

4.      Create Model Relationships

5.      Create and publish a Report

6.      Create a Drill through page add bookmarks buttons and publish the report

7.      Create a Dashboard, edit title details, configure an alert

8.      Create an animated scatter chart, 

9.      Create a forecast 

10.  Publish an App 

2.      Rob Collie, Avichal Singh “Power Pivot and Power BI: The Excel User's Guide to DAX, Power Query, Power BI & Power Pivot in Excel”,2016. 

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