Syllabus for
Master of Science (Economics and Analytics)
Academic Year  (2021)

The Department of Economics at the Delhi NCR campus is functioning under the School of Social Sciences in CHRIST (Deemed to be University). Established in the year 2019 with the commencement of the new Campus at Delhi NCR, the department has representation of faculty from all cultures and regions in India and with qualifications from the top institutions in the country and abroad. Together with rich experience in teaching, research and consultancy, they specialises in Monetary and Financial Economics, Environmental Economics, Behavioural Economics, Industrial Economics, Informal Economy and so on, involving in advanced research.

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.

TheMasterofScienceinEconomicsandAnalyticsisanintensiveprogramthatwill guidestudentsthrougheconomicmodellingandtheorytocomputationalpracticeand cutting-edge tools, providing a thorough training in descriptive, predictive and prescriptive analytics. Students will be equipped with a solid knowledge of econometricandmachinelearningmethods,optimizationandcomputing.Thesebig- dataskills,combinedwithknowledgeofeconomicmodelling,willenablethemto identify,assessandseizetheopportunityfordata-drivenvaluecreationintheprivate andpublicsectors.Studentswillbetrainedtocontributesignificantlytoempiricaland appliedworkintheupcomingfieldofEconomics.

Examination and Assessment

 The evaluation is divided in to two components: Continuous Internal Assessment (CIA) including Mid Semester Examination (MSE), and the End Semester Examination (ESE).

 Assessment Pattern

 The Continuous Internal Assessment (CIA) will be assessed for seventy per cent weightage and the End Semester Examination (ESE) for thirty per cent weightage. The practical courses and the common core courses will be assessed out of hundred marks in various components including attendance. The Mid Semester and End Semester written examination question pattern consists of questions divided into two or three sections with short answers, short essays and long essays.

Course Objectives This course aims at analyzing the Economic behavior of the firms and markets. It is mainly concerned with the objective of equipping the students in a comprehensive manner with various aspects of consumer behavior and demand analysis, Production theory and behavior of cost, equilibrium of firm and various forms of market.

Course Outcomes

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

• Demonstrate the analytical and critical skills relevant to economics thinking,

• Demonstrate the rigorous quantitative training that analytical economics requires,

Construction of theories: Deduction and induction; Empirical verification; Theories and tautologies. 

Consumer preferences; Axioms of preference ordering; Utility function: existence and characteristics; concavity and quasiconcavity; Budget sets; Demand functions: Zero homogeneity; Income and substitution effects; Slutzky theorem: Indirect utility functions; Hicksian compensated demand functions; Expenditure functions; Substitutes and complements: gross and pure; Revealed preference. 

Production functions; Concavity and quasiconcavity; Returns to a factor and to scale; Total, marginal and average cost function; Long run cost curves: envelopes; Factor demand functions, Conditional factor demands; Profit maximization; Supply functions, cost minimization – first and second order conditions; Linear homogeneous production functions and their properties; Cobb-Douglas, CES, VES and Translog production functions and their properties;Leontief’s production functions, Elasticity of substitution, its derivation for C-D and CES functions; the impact of tax/subsidy.

Characterizing perfect competition; Pricing and output under perfect competitive markets; Monopoly markets: Pricing, discrimination; welfare costs; Monopolistic competition: Characteristics; Long run and short run behavior; Oligopoly: Cournot’s model; Stackleberg framework: Instability; Dominant firm; Compensating variation; Price and output determination under monopsony and bilateral monopoly;

1.Andreu Mas-Colell, M D Whinston and J R Green (1995), Microeconomic Theory, Oxford University Press.

2.Kreps, David M. (1990), A Course in Microeconomic Theory, Princeton University Press, Princeton.

3.Krugman, Paul. and Wells, Robin. (2005), Microeconomics, Worth Publishers.

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

5.Sen, Anindya (2007), Microeconomics: Theory and Applications, Oxford University Press, New Delhi.

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

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

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.

Nature, functions, types, and evaluation of money The debate relating to the definition of money  Liquidity theory,  Gurley and  Shaw  Hypothesis,  Alternative money stock measures, The quantity and components of money stock in India and broad trend in them. Base money, money multipliers, the Quantity theory of money Demand for money Keynesian theory of demand for money Baumol-Tobin theory Issues regarding endogenous and exogenous supply of money

Dynamics Money in neo-classical models Money in non-neo-classical models Walrasian interpretation of Keynesian unemployment (Patinkin, Clower and Leijonhufvud) Post- Keynesian interpretation (Sidney Weintraub, Paul Davidson, Kelecki and Minsky)

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

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

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

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

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

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

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

6.      Mishkin Frederic (2007), The Economics of Money Banking and Financial Markets, 8th ed Addison Wesley Longman Publishers.

7.      Bain, Keith & Howells, Peter (2009), Monetary Economics: Policy and Its Theoretical Basis, Palgrave.

8.      Friedman, Ben & Hahn F.H. (Eds.), (1990), Handbook of Monetary Economics, Vols.1, 2, & 3, North Holland Publishers.

9.      Langdana  Farrokh  (2009),  Macroeconomic  Policy:  Demystifying  Monetary  and Fiscal Policy, 2nd Edition, Springer.

10.   William. H. Branson (2005). Macroeconomic Theory and Policy, Third Edition, All India Traveller BookSeller Publishers, New Delhi.

11.   D.N.  Dwivedi.  (2005).  Macroeconomics:  Theory and Policy.  2nd Edition, TataMcGraw Hill Education.

CIA II: 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%

This course proposes to familiarize students with Introduction to Mathematical Economics, is designed with the intention to understand Areas under curve-Definite and indefinite Integration, Application- Consumer Surplus and Producer Surplus, the focus is on Concavity, Convexity, Quasi concavity, Quasi convexity Optimization of functions. This course intend to discuss about the Constrained Optimization Problems two variables, one constraint-Lagrange-multiplier method-First order conditions-Second order conditions, aims to discuss the Difference and Differential Equations and Economic Applications. 

Course Objectives

The main aim of the course is to provide knowledge about mathematical techniques in advanced forms and their applications.

The main objectives of the paper are to train the students to grasp the use of mathematical techniques and operations to analyze economic problems and to initiate students into 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

Linear Models and Matrix Algebra -Matrix algebra -Determinants-Inverse-Eigenvalues and Eigen vectors -Cramer’s rule-Quadratic Forms

Applications: Multiple commodity markets- Heckscher-Ohlin model, IS-LM Model- Mundell-Fleming Model

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

CIA II   : 25 % (Mid Semester Examination)

CIA III  : 20 %

Attendance: 05 %

ESE       : 30%  

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

 Data Visualization

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

2.Introducing  Data  Science,  Davy  Cielen,  Arno  D.  B.  Meysman  and  Mohamed  Ali, Manning Publications, 2016.

3.Introducing Data Science, Davy Cielen, Arno D. B. Meysman, Mohamed Ali, Manning

Publications Co., 1st edition, 2016

2015.

2.Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt, O’

Reilly, 1st  edition, 2013.

3.Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2nd edition, 2014

4.An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela

Witten, Trevor Hastie, Robert Tibshirani, Springer, 1st  edition, 2013

The nature of  knowledge  and  theory  -  Philosophy  of  Social  Science  Research  - Relevance of Social Science Research - Objectivity and Values in Social Sciences; logic of   Scientific   Investigation:   Theory   Construction   in   Social   Science   Research   - Approaches  to  Social  Science  Research,  Theoretical,  Applied  and  Action  Research  - Ethical Issues in Research on Human or Social Subjects - Non-sexist approach in Social Sciences

Review   of   Literature   -   Identification   of   Research   Gaps   and   Research   Needs   - Identification,    selection    and    formulation    of    research    problem    -    Formulating Hypotheses/Propositions/Issues, conceptualizing research problem

Uni-disciplinary,  interdisciplinary,  multi-disciplinary  methodologies  -  Quantitative Research  Methods:  An  Overview  -  Qualitative  Research  Methods:  An  Overview  - Historical Method - Case Study Method - Action Research - Monitoring and Evaluation

- Triangulation (including/mixing Qualitative and Quantitative) Methods

Procedures. SAGE Publications. 3rd

2.Kumar R (2010). Research methodology: a step by step  guide for beginners. SAGE Publications Ltd; Third Edition.

3.Hay M Cameron (2015) Methods That Matter: Integrating Mixed Methods for More

Effective Social Science Research

2.Schutt Russell K. (2016), Investigating the social world: the process and practice of research

The course aims to explain the basic concepts of python programming. The course aims to enable the students to do python programming using conditionals, loops, functions, various data structures and to implement file handling

Entering Expressions into the Interactive Shell, The Integer, Floating-Point, and String Data  Types,  String  Concatenation  and  Replication,  Storing  Values  in  Variables,  Your First Program, Dissecting Your Program,

Flow control,

Boolean   Values,   Comparison   Operators,   Boolean   Operators,   Mixing   Boolean   and

Comparison  Operators,  Elements  of  Flow  Control,  Program  Execution,  Flow  Control

Statements, Importing Modules, Ending a Program Early with sys.exit(),

 Functions,

def  Statements  with  Parameters,  Return  Values  and  return  Statements,  The  None Value, Keyword Arguments and print(), Local and Global Scope, The global Statement, Exception Handling, A Short Program: Guess the Number

Lists,

The List Data Type, Working with Lists, Augmented Assignment Operators, Methods, Example  Program:  Magic  8  Ball  with  a  List,  List-like  Types:  Strings  and  Tuples, References,

 Dictionaries and Structuring Data,

The Dictionary Data Type, Pretty Printing, Using Data Structures to Model Real-World

Things

Manipulating Strings,

Working with Strings, Useful String Methods, Project: Password Locker, Project: Adding Bullets to Wiki Markup

The  shutil  Module,  Walking  a  Directory  Tree,  Compressing  Files  with  the  zipfile Module, Project: Renaming Files with American-Style Dates to European-Style Dates, Project: Backing Up a Folder into a ZIP File,

 Debugging,

Raising  Exceptions,  Getting  the  Traceback  as  a  String,  Assertions,  Logging,  IDLE’s

Debugger.

NumPy Libraries for Arrays, Pandas Library for Data Processing

Matplotlib  for  Visualization,  Seaborn  Library  for  Visualization,  SciPy  Library  for

Statistics

1.  Installing python to your computer

2.  Demonstrate the usage Conditional Statements

3.  Demonstrate the use of Iterative statements

4.  Demonstrate the usage of Functions.

5.  Demonstrate the usage of different Data Types

6.  Demonstrate the usage of String Functions

7.  Demonstrate the Exception handling

8.  Demonstrate creation and use of a class

9.  Demonstrate the working of Inheritance concepts

10.  Demonstrate seeking and finding of file.

11.  Demonstrate opening a file and writing in to it

12. Demonstrate the usage of list data structure.

13.  Demonstrate the usage of Tuples data structure.

14. Demonstrate the usage of a Dictionary data structure.

15. Demonstrate Sending an email using python.

16. Accessing Array index using NumPy

17. Aggregation function using NumPy

18. Implement

a)   Matplotlib b)   Seaborn

Revisedand expanded Edition, MIT Press, 2013

2.Robert Sedgewick, Kevin Wayne, Robert Dondero, ―Introduction to Programming in

3.Python: An Interdisciplinary Approach, Pearson India  Education Services Pvt. Ltd.,

2016.

4.Timothy A. Budd, ―Exploring Python‖, Mc-Graw Hill Education (India) Private Ltd

2015.

5.Kenneth   A.   Lambert,   ―Fundamentals   of   Python:   First   Programs‖,   CENGAGE

Learning, 2012.

6.Charles Dierbach, ―Introduction to Computer Science using Python: A Computational

Problem-Solving Focus, Wiley India Edition, 2013.

A good grasp of microeconomics is vital for managerial decision making, for designing and understanding public policy. The course is intended to provide a good understanding and base to the students in applying the concepts and methods of microeconomics in the practical field. This course will equip the students to understand the various aspects of the traditional Microeconomic theory as well as the latest developments in this field and the applications of theories in analyzing current economic problems and to develop the ability to synthesize knowledge.

Extensive and normal form representation of games– Nash equilibrium (impure and mixed strategies); definition and existence – subgame perfection dynamic games; Applications: strategic behaviour of firms in a market–Bertrand, Cournot and Stackelberg models – and entry deterrence.

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

Partial and general equilibrium; Walrasian excess demand and input-output approaches to general Equilibrium; Existence, stability and uniqueness of partial equilibrium and general equilibrium; Relationship between relative commodity and factor prices (Stopler-Samuelson theorem); Relationship between output-mix and real factor prices-effect of changes in factors supply in closed economy(Rybczynsky theorem).

Pigovian welfare economics; Pareto optimal conditions; Value judgement; Social welfare function; Compensation principle; Inability to obtain optimum welfare–Imperfections, market failure, decreasing costs; Uncertainty and non–existent and incomplete markets; Theory of second-best –Arrow’s impossibility theorem, Rawl’s theory of Justice; Equity efficiency trade-off.

Definition of Transaction Cost and types of transaction costs; General Principles in Modelling Transaction Costs; Modelling Transaction Costs by modelling transaction activity. Emergence of Property Rights: The invisible hand and the optimistic theory; contracting for Property Rights: the role of political bargaining and the Liebcap Thesis. Principles of contractual obligations; economic theories of contract: agency theory, self-enforcing agreement theory and relational contract theory; types of private ordering and their dynamics.

1. Kreps, David M. (1990), A Course in Microeconomic Theory, Princeton University Press, Princeton.
2. Koutsoyiannis, A. (1979), Modern Microeconomics, (2nd Edition), Macmillan Press, London
3. Furburton & Richter, ‘Institutions and Economic Theory’, Dryden Press.

1. Andreu Mas-Colell, M D Whinston and J R Green (1995), Microeconomic Theory, Oxford University Press.
2. Henderson, J.M. and R.E. Quandt (2003), Microeconomic Theory: A Mathematical Approach, McGraw Hill, New Delhi.
3. Krugman, Paul. and Wells, Robin. (2005), Microeconomics, Worth Publishers.
4. Mukherjee, Anjan (2002), An Introduction to General Equilibrium Analysis, Oxford University Press.
5. Osborne, Martin J. (2009), An Introduction to Game Theory, Oxford University Press.
6. Sen, Anindya (2007), Microeconomics: Theory and Applications, Oxford University Press, New Delhi.
7. Varian, H. (2000), Microeconomic Analysis, W.W. Norton, New York.
8. Pindyck, Robert & Rubinfeld, Daniel (2013), Micro Economics, 8th Edition, Pearson Education, USA

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. 

Classical dichotomy and monetary neutrality- Classical, Neo-classical and modern theories of inflation- Keynesian and monetarist views on inflation -Inflation in the static model -Wages, prices and productivity-The relation of wages to unemployment- Short-run and long-run Phillips curve and the policy implications- Modifications in Phillips curve- Natural rate of unemployment-Seigniorage and hyperinflation-disinflation. 

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 intertemporal  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 

The New Classical macroeconomics- Stagflation trend-The Supply-Side economics major implications. 

Rationale and impact of reforms since 1991 on BOP, Problems of India’s international debt export policies, working and regulations of MNCs in India- The issues and policies towards financial stability- International reserves, Monetary integration- European  Monetary System-Contemporary macroeconomic debates in India and the world. 

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%  

The course is designed to impart the learning of principles of econometric methods and tools.  This is expected to improve student’s ability to understand econometrics in the study of economics. This course is intended to provide a thorough and sound understanding of the essential theoretical base, an introduction to the important and useful techniques of modeling and also an understanding of the broad applications of econometrics.

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, matrix approach to linear regression models 

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. Ramanathan, R., Introductory Econometrics with applications, fifth edition, Thomson  Asia Private Limited, 2002 

3. Brooks, C., Introductory Econometrics for Finance, first edition, Cambridge University  Press, 2003 

CIA II : 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%  

This course is planned to give the students the basic knowledge in R programming  language and to make them familiar with the flexible graphical capabilities of R. It also  covers the Statistical computational features of R and exploratory analysis and  modeling using R 

The R environment, R and statistics, R commands, Data permanency and removing  objects, Simple manipulations, Numbers and Vectors, Objects- modes and attributes,  Ordered and unordered Factors, Arrays and Matrices 

Lists and Data Frames- Constructing and modifying lists, Making Data frames, attach( )  and detach( ), Working with data frame, Reading data from files using read.table( ),  scan( ), Grouping, Conditional execution: if statements, Repetitive execution: for loops,  repeat and while loops, Functions. 

Data Exploration for Univariate and Bivariate Data-Univariate Data - Handling  categorical data and numerical data using R, Bivariate Data -Handling bivariate  categorical data using R, Categorical vs. Numerical, Numerical vs. Numerical 

2. Robert Sedgewick, Kevin Wayne, Robert Dondero, ―Introduction to  Programming in 

3. Python: An Interdisciplinary Approach, Pearson India Education Services Pvt.  Ltd., 2016. 

4. Timothy A. Budd, ―Exploring Python‖, Mc-Graw Hill Education (India) Private  Ltd 2015. 

5. Kenneth A. Lambert, ―Fundamentals of Python: First Programs‖, CENGAGE  Learning, 2012. 

6. Charles Dierbach, ―Introduction to Computer Science using Python: A  Computational Problem-Solving Focus, Wiley India Edition, 2013.

The course is designed to train and equip the students to carryout 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 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, Conclusion and Scope for future Enhancements

• Plagiarism Report

Week 1 - Discussion and Identification of Research Domain (Updations)

Week 2 - Identification of Research Gap / OBJECTIVES OF RESEARCH

Week 3 - Research Design Phase - I

Week 4 - Research Design Phase - II

Week 5 - Research Design Phase - III

Week 6 - Methods of Data Collection - Processing - Analysis of Data

Week 7 - Methods of Data Collection - Processing - Analysis of Data

Week 8 - Methods of Data Collection - Processing - Analysis of Data

Week 9 - Methods of Data Collection – Processing- Analysis of Data

Week 10 - Methods of Data Collection - Processing - Analysis of Data

Week 11 - Implementation Phase - I

Week 12 - Implementation Phase - I (a)

Week 13 - Implementation Phase - I (b)

Week 14 - Implementation Phase - I (c)

Week 15 - Implementation Phase - I (d)

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.

The Course enables Students to introduce the historical development of statistics, presentation of data,  descriptive measures, and fitting mathematical curves for the data., enables students to understand and apply the descriptive measures and probability for data analysis. Students will be able to evaluate the relationship between quantitative and qualitative data and the concept of probability. 

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.

Discriminant Analysis, Principal Components  Analysis and Factor Analysis.

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. Muirhead, R.J. (1982): Aspects of Multivariate Statistical Theory, John Wiley. 3. Kshirsagar, A.M. (1972): Multivariate Analysis, 1stEdn. Marcel Dekker. 

2. Mukhopadhyay, P. :Mathematical Statistics. Books and Allied, January 2016 3. Gibbons, J. D. and Chakraborty, S (2003): Nonparametric Statistical Inference. 4th  Edition. Marcel Dekker, CRC. 

• Introduce the main concepts that describe the institutional structure of the society
• Provide an overview of the recent developments in the field of institutional  economics 
• Help the students to understand the role of institutional environment in economic  theory and in business practice
• Enable to use the concepts and methods of institutional economics in the analysis of  institutions of the society 

Institutional Economics as a departure from Neo-Classical and Marxian Economics – Historic development of Institutional Economics - Core issues in New Institutional 

Economics. Rational choice model. Full and perfect information. Bounded rationality.  Incomplete and imperfect information. Ultimate game. Beauty contexts game.  Assumptions of New Institutional Economics. Incomplete specification of rules. 

Defining Transaction: Williamson, Ronald Coase and Hobbs meaning of Transaction - Types of Transaction Cost: Information cost, Bargaining cost, Monitoring cost- Market,  Managerial and Political transaction costs - Identification and measurements of transaction costs: some general principals - Modeling Transaction Costs by modeling the transaction activity. 

Incomplete contracts. Grossman-Hart model. Decision rights. Principal-agent framework. Asymmetric information. Adverse selection. Signaling. Screening. Moral hazard. Hidden action and information. Delegation. Agency costs. Incentive contracts.  Opportunistic behavior. 

Definition of property rights. The Coase theorem and externalities. Categories of  property rights. Property rights regimes. Collective property. Common property.  Residual rights. Land rights. The naive theory of property rights emergence. 

The New Institutional Economics of the Market: Market as Organisation, Price Rigidity,  Market Organisation as Market Cooperation and Neo-institutionalist view of Market  Organisation – The New Institutional Economics of the Firm: The Orthodox Neo-Classical Firm, The Incentives and the Limits to Integration, Ownership and Control,  Institutional Models within the Neo-Classical Framework, Co – Determination and  Comparison of the Formal Models of the Firm. 

2. Eggertson, Thrainn. (1999). Economic Behaviour and Institutions. Cambridge  University Press. 

CIA 2-25%

CIA 3- 20%

End Sem-30%

Attendance-5%

Predictive analytical tools are increasingly used in business decision making. The course will introduce the participants to business problems where models that involve prediction, classification, clustering and association can be applied. The course is designed as a hands on exercises using data available in the public domain. The course requires that the  participants are familiar with basic working in R and have sufficient understanding of basic statistics. At the end of the course participants will be able to classify predictive models and distinguish which models are suitable for a particular problem. The course will also emphasize on performance and accuracy problems of predictive models as and when they arise and be able to fix them

I.AIntroduction to Predictive Modelling

Understanding Data- Core Components of a Model – Types of Models- Supervised- Unsupervised- Semi supervised and Reinforcement Learning Models – Parametric and Non Parametric Models- Regression and Classification Models

I.BProcess of Predictive Modelling

Defining the Model’s Objective- Collecting Data- Picking a Model- Preprocessing Data- Exploratory Data Analysis- Data Transformations-Missing Data- Outliers- Training and Assessing the Model- Exploring Alternate models – Model Deployment

II.A Linear Regression 

Introduction to Linear Regression- Assumptions- Estimating a Simple Linear Regression- Multiple Linear Regression (MLR)- Assessing Linear Models-  Residual Analysis-  Significance Tests- Performance Metrics for Linear Regression- Problems with MLR- Multicollinearity- Outliers

II. B Logistic Regression

Classifying with Linear Models-Introduction to Logistic Regression-Generalized Linear Models- Assumptions-Interpretation of Coefficients- Maximum Likelihood Estimation- Assessing Logistic Regression Models- Model Deviance – Test Performance- Introduction to Multinomial Logistic Regression

Fundamental Concepts of Time Series- White Noise- Fitting White Noise Time Series- Random Walk- Fitting Random Walk- Stationarity- Stationary Time Series Models- Moving Average Models- Autoregressive Models- Autoregressive Moving Average Models- Non Stationary Time Series Models- Autoregressive Integrated Moving Average Models- Autoregressive Conditional Heteroscedasticity Models- Generalized Autoregressive heteroscedasticity Models.

I. Classification 

Introduction to classification techniques-Nearest Neighbor Method- K- Nearest Neighbor algorithm-Discriminant Analysis-Fisher’s Linear Discriminant Function- Decision Trees

II. Clustering

Introduction to Clustering- k-Means Clustering-Expectation Maximization Algorithm- Hierarchical Clustering Procedures

Introduction to Association techniques-Quick Review of Probability- Bayesian Probability Methods-Market Basket Analysis- Association Rules and Lift

2. Albright C. S., Winston Wayne L. and Zappe C. J (2009).  Decision Making Using Microsoft Excel (India Edition). Cengage Learning. 

3. Evans J. R (2013). Business Analytics Methods, Models and Decisions. Pearson, Upper Saddle River, New Jersey.