Department of ECONOMICS NCR

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

 
1 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MEA131N MICROECONOMIC THEORY AND APPLICATIONS-I Core Courses 4 4 100
MEA132N MACROECONOMIC THEORY AND POLICY-I Core Courses 4 4 100
MEA133N ADVANCED MATHEMATICAL ECONOMICS Core Courses 4 4 100
MEA134N FUNDAMENTAL OF STATISTICS Core Courses 4 4 100
MEA135N PRINCIPLES OF DATA SCIENCE Core Courses 4 4 100
MEA136N RESEARCH METHODOLOGY Core Courses 2 2 50
MEA171N PYTHON PROGRAMMING Discipline Specific Elective 6 5 150
2 Semester - 2021 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MEA231N MICROECONOMIC THEORY AND APPLICATIONS-II Core Courses 4 4 100
MEA232N MACROECONOMIC THEORY AND POLICY-II Core Courses 4 4 100
MEA233N ECONOMETRIC METHODS Core Courses 4 4 100
MEA234N STATISTICS USING R Core Courses 5 6 150
MEA235N RESEARCH MODELLING AND IMPLEMENTATION Discipline Specific Elective 2 2 50
MEA241AN MULTIVARIATE ANALYSIS Discipline Specific Elective 4 4 100
MEA242AN APPLIED INSTITUTIONAL ECONOMICS Discipline Specific Elective 4 4 100
MEA272N PREDICTIVE ANALYTICS Discipline Specific Elective 4 4 100
    

    

Department Overview:

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.

Mission Statement:

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

MISSION

  

Introduction to Program:

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.

Program Objective:

 

Programme Objective

          Toenablelearnerstodevelopknowledgeandskillsincurrentandemergingareas of dataanalytics.

 

          Tostrengthenanalyticalandproblem-solvingskillsthroughdevelopingreal-time applications.

 

          Toempowerstudentswithtoolsandtechniquesforhandling,managing, analysing,andinterpretingdata.

 

          Toimbibequalityresearchanddevelopsolutionstosocialissues.

 

 

Programme Outcomes

On successful completion of the MSc programme students will be able to

PO1:Engageincontinuousreflectivelearninginthecontextoftechnologyandscientific advancement.

PO2: Identify the need and scope of Interdisciplinary research.

PO3: Enhance research culture and uphold scientific integrity and objectivity.

PO4: Understand the professional, ethical, and social responsibilities.

PO5: Understand the importance and the judicious use of technology for the sustainability of the environment.

PO6: Enhance disciplinary competency, employability, and leadership skills.

    Programme Specific Outcomes (PSO)

 

 

 

PSO1:ProblemAnalysisandDesign:Abilitytoidentifyanalyzeanddesignsolutions for data analytics problems using fundamental principles of economics, mathematics, statistics,computingsciences,andrelevantdomaindisciplines.

 

PSO2: Modern Software Tool Usage: Acquire the skills in handling data analytics programming tools towards problem-solving and solution analysis for domain-specific problems.

 

PSO3:SocietalandEnvironmentalConcern:Utilizetheeconomicsandanalytics theoriesforsocietalandenvironmentalconcerns.

 

PSO4:ProfessionalEthics:Understandandcommittoprofessionalethicsandcyber regulations,responsibilities,andnormsofprofessionalcomputingpractices.

 

PSO5: Applications in Multidisciplinary Domains: Understand the role of statistical approachesandapplythesametosolvereal-lifeproblemsinthefieldsofeconomicsand analytics.

 

PSO6: ProjectManagement:Applytheresearch-basedknowledgetoanalyzeandsolve advancedproblemsineconomicsandanalytics.

 

 

Assesment Pattern

Assessment Strategy

       Internal assessment 70%

       CIA1- written assignment, group work, presentations

       CIA2 - midterm examination

       CIA3 - written assignment, group work, presentations

       End Semester Examination 30%

The assessment strategy involves specific rubric for evaluation of each component.

Examination And Assesments

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.

MEA131N - MICROECONOMIC THEORY AND APPLICATIONS-I (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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,

Learning Outcome

Apply the microeconomic theory to micro-level real world economic problems,

Unit-1
Teaching Hours:5
Methodology
 

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

Unit-2
Teaching Hours:15
Utility and Demand
 

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. 

Unit-3
Teaching Hours:20
Production and Supply
 

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.

Unit-4
Teaching Hours:20
Markets
 

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;

Text Books And Reference Books:

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

Essential Reading / Recommended Reading

Reference Books

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

Evaluation Pattern

 

Evaluation Pattern

CIA I    : 20 %

CIA II   : 25 % (Mid Semester Examination)

CIA III  : 20 %

Attendance: 05 %

ESE       : 30% 

 

 

 

ESE - 30%

MEA132N - MACROECONOMIC THEORY AND POLICY-I (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Learning Outcome

 

 

 

                   Identify the determinants of various macroeconomic aggregates such as output, unemployment, inflation, productivity and the major challenges associated           with the measurement of these aggregates.

                  Understand the theoretical foundation of macroeconomics and the contribution of different schools of thought to the further development of macroeconomics.
                  Describe the main macroeconomic theories of short-term fluctuations and long-term growth in the economy.
                  Understand the factors influencing the interest rate and BOP.
                  Apply the Macroeconomic theories to understand the existing economic problems and their possible solutions
                  Evaluate the Macroeconomics policies in the current economic environment

Unit-1
Teaching Hours:12
Introduction and Output Determination
 

 

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

Unit-2
Teaching Hours:10
Product and Money Market Equilibrium
 

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.

Unit-3
Teaching Hours:8
Supply of Money and Demand for Money
 

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

Unit-4
Teaching Hours:10
Money in Walrasian and non-Walrasian Economies and Theories of Disequilibrium
 

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)

Unit-5
Teaching Hours:10
Theories of the Interest Rate
 

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

Unit-6
Teaching Hours:10
Monetary Institutions & Monetary Policy
 

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.

Text Books And Reference Books:

1. Burda and Wyplosz (2015). Macroeconomics: A European Text, Fifth Edition, Oxford University Press, New York.

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

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

Essential Reading / Recommended Reading

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

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.

Evaluation Pattern

CIA I: 20 %

CIA II: 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%

MEA133N - ADVANCED MATHEMATICAL ECONOMICS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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

 

Learning Outcome

·       To understand the fundamentals of Introduction to Mathematical Economics.

·       Assess the approaches to Mathematical economics and their applications

  •   Exhibiting a sound understanding of mathematical techniques discussed.
  •  Formulating economic problems in mathematical terms.
  •  Applying the relevant tools for analyzing economic problems.

 

Unit-1
Teaching Hours:12
Introduction to Mathematical Economics -Equilibrium (Or Static) Analysis
 

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

 

Unit-2
Teaching Hours:6
Integration
 

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

Unit-3
Teaching Hours:15
Unconstrained Optimization
 

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

Unit-4
Teaching Hours:15
Constrained Optimization Problems
 

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

Unit-5
Teaching Hours:12
Difference and Differential Equations and Economic Applications
 

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

Text Books And Reference Books:
  1. Simon, C. and L. Blume, Mathematics for Economists, Norton, London, 1994.
  2. Edward Dowling (2000), Introduction to Mathematical Economics, McGraw Hill Ltd,NewDelhi.
  3. Chiang, Wainwright, Kevin (2005), Fundamental Methods of Economics, McGraw Hill Ltd, NewDelhi.
  4. Allen R G D(1974).Mathematical Analysis for Economists, McMillan Press and ELBS, London.
  5. Allen R G D (1967). Macroeconomic Theory, McMillan Co., Ltd.,.
  6. Chiang A C (1986). Fundamental Methods of Mathematical Economics, McGraw Hill,New York.
  7. Koutsoyiannis A. (1979). Modern microeconomics, 2nded, ELBS with McMillan.
  8. Monga G S. (1996) Mathematics and Statistics for Economics, Vikas Publishing House Pvt. Ltd., Delhi.
Essential Reading / Recommended Reading

N/A

Evaluation Pattern

CIA I    : 20 %

CIA II   : 25 % (Mid Semester Examination)

CIA III  : 20 %

Attendance: 05 %

ESE       : 30%  

MEA134N - FUNDAMENTAL OF STATISTICS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 
  • To introduce the historical development of statistics, presentation of data, descriptive measures and fitting mathematical curves for the data.
  • To introduce measurement of the relationship of quantitative and qualitative data and the concept of probability.
  • To enable the students to understand and apply the descriptive measures and probability for Economics analysis.

Learning Outcome

  1. After Successful completion of the course students will be able to

  • Demonstrate the history of statistics and present the data in various forms.
  • Infer the concept of correlation and regression for relating two or more related variables.
  • Demonstrate the probabilities for various events.

Unit-1
Teaching Hours:10
Organization and Presentation of data
 

Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data. Presentation of data by tables: construction of frequency distributions for discrete and continuous data, graphical representation of a frequency distribution by histogram and frequency polygon, cumulative frequency distributions (inclusive and exclusive methods).

Unit-2
Teaching Hours:15
Descriptive Statistics
 

Measures of location or central tendency: Arithmetic Mean, Median, Mode, Geometric mean, Harmonic mean. Partition values: Quartiles, Deciles and percentiles. Measures of dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of variation. Moments: measures of skewness, Kurtosis.

Unit-3
Teaching Hours:10
Correlation and Regression:
 

Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only). Regression: Concept of errors, Principles of Least Square, Simple linear regression and its properties.

Unit-4
Teaching Hours:10
Basics of Probability
 

Random experiment, sample point and sample space, event, algebra of events. Definition of Probability: classical, empirical and axiomatic approaches to probability, properties of probability. Theorems on probability, conditional probability and independent events, Laws of total probability, Baye’s theorem and its applications.

Unit-5
Teaching Hours:15
Probability Distribution
 

Binomial Distribution and their properties with practical examples, Poisson Distribution and their properties with practical examples, Normal Distribution and their properties with practical examples,

Text Books And Reference Books:

Text Books

  1. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc., New Jersey, 2015.

  2. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014.

Essential Reading / Recommended Reading
  1. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015.

  2. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017.

  3. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013.

  4. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi,

Evaluation Pattern

Evaluation Pattern

 

CIA I

20%

CIA II

25%

CIA III

20%

Attendance

5%

ESE

30%

MEA135N - PRINCIPLES OF DATA SCIENCE (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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 into the important and useful techniques of modelling and also an understanding of the broad applications of econometrics

 

Learning Outcome

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

    Use modern econometrics for advanced data analysis

    Interpret the empirical findings

   Develop   hands-on   skills   in   the   application   of   econometric   and   quantitative techniques to real world economic and business data.

Unit-1
Teaching Hours:12
Introduction to Data Science
 

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

Unit-2
Teaching Hours:12
Data Science Process
 

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

Unit-3
Teaching Hours:12
Machine Learning
 

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

Unit-4
Teaching Hours:12
First Steps in Big Data
 

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

Unit-5
Teaching Hours:12
Databases
 

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

 Data Visualization

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

Text Books And Reference Books:

1.Think Like a Data Scientist, Brian Godsey, Manning Publications, 2017.

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

Essential Reading / Recommended Reading

1.Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1st edition,

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

 

 

Evaluation Pattern

CIAs Only

MEA136N - RESEARCH METHODOLOGY (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

Understanding   of   the   importance   of   research   in   creating   and   extending   the knowledgebase of their subject area; Ability to distinguish between the strengths and limitations  of  different  research  approaches  regarding  their  subject/research  area; Knowledge of the range of qualitative and quantitative research methods potentially available to them; The ability to differentiate between the role of practitioners and the role of researchers; An understanding of and begin to critically reflect upon issues of ethics and role of the researcher; The skills to work independently, to plan and to carry out a small-scale research project.

 

Learning Outcome

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

 

    demonstrate the knowledge of the range of qualitative and quantitative research methods potentially available.

    should  be  able  to  differentiate  between  the  role  of  practitioners  and  the  role  of researchers.

    should work independently, to plan and to carry out a small-scale research project.

    demonstrate  the  understanding  of  and  ability  to  critically  reflect  upon  issues  of ethics and  research

Unit-1
Teaching Hours:8
Introduction
 

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

Unit-2
Teaching Hours:6
Research Design
 

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

Unit-3
Teaching Hours:8
Overview of Social Science Methodology
 

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

Unit-4
Teaching Hours:8
Data analysis and Research Communication
 

Choice of Statistical and Processing Techniques  - Interpretative Narrative Methods - Theory of the Testing of Hypotheses - Presentation of Research Findings, Products of Research, Thesis Writing - Factors conducive to research utilization firm; Compensating variation; Price and output determination under monopsony and bilateral monopoly;

Text Books And Reference Books:

1.Blair  J,  Czaja  R,  Blair  E  (2014).  Designing  Surveys:  A  Guide  to  Decisions  and

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

Essential Reading / Recommended Reading

1.Bryman, Alan (2015). Social Research Methods. Oxford: Oxford University Press

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

Evaluation Pattern

CIAs Only

MEA171N - PYTHON PROGRAMMING (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:6
Max Marks:150
Credits:5

Course Objectives/Course Description

 

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

Learning Outcome

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

    Write programs in python using basic constructs

    Apply various conditionals and iterative statements

    Implement algorithms using control structures like List, Tuples and Dictionary.

    Implement programs with file handling

    Formulate the results from data using various Data Processing and Visualization Libraries.

Unit-1
Teaching Hours:12
Introduction to Python
 

Python Basics,

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

 

Unit-2
Teaching Hours:12
 

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

 

Unit-3
Teaching Hours:12
 

Pattern Matching with Regular Expressions,

Finding Patterns of Text Without Regular Expressions, Finding Patterns of Text with Regular  Expressions,  More  Pattern  Matching  with  Regular  Expressions,  Greedy  and Nongreedy  Matching,  The  findall()  Method,  Character  Classes,  Making  Your  Own Character  Classes,  The  Caret  and  Dollar  Sign  Characters,  The  Wildcard  Character, Review  of  Regex  Symbols,  Case-Insensitive  Matching,  Substituting  Strings  with  the sub() Method, Managing Complex Regexes, Combining re .IGNORECASE, re .DOTALL, and re .VERBOSE, Project: Phone Number and Email Address Extractor

 Reading and Writing Files,

Files  and  File  Paths,  The  os.path  Module,  The  File  Reading/Writing  Process,  Saving Variables with the shelve Module, Saving Variables with the pprint. pformat() Function, Project: Generating Random Quiz Files, Project: Multiclipboard,

 Organizing Files,

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.

 

Unit-4
Teaching Hours:12
 

NumPy Libraries for Arrays, Pandas Library for Data Processing

Unit-5
Teaching Hours:12
 

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

Statistics

Unit-6
Teaching Hours:30
Lab
 

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

Text Books And Reference Books:
  • 1. l Sweigart,“Automate the Boring Stuff with Python”,1st Edition, No Starch Press, 2015. (Available under CC-BY-NC-SA license at https://automatetheboringstuff.com/) (Chapters 1 to 18)
  • 2.   Michael Bowles, Machine Leaning in Python, Essential techniques for predictive analysis, Wiley
Essential Reading / Recommended Reading

1. John  V  Guttag,  ―Introduction  to  Computation  and  Programming  Using  Python  ‘‘,

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.

Evaluation Pattern

CIAs Only 

MEA231N - MICROECONOMIC THEORY AND APPLICATIONS-II (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Learning Outcome

  • To provide the students with a thorough knowledge and understanding of the foundations of modern economic analysis.
  • To provide a fundamental understanding of game theory, theory of factor pricing, theory of general equilibrium and welfare economics.
  • To understand the role of institutions by focusing on transaction costs, absolute property rights and relative property rights.

Unit-1
Teaching Hours:10
Game Theory
 

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.

Unit-2
Teaching Hours:10
Distribution
 

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.

Unit-3
Teaching Hours:10
General Equilibrium
 

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).

 

Unit-4
Teaching Hours:10
Welfare Economics
 

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.

 

Unit-5
Teaching Hours:20
New Institutional Economics
 

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.

Text Books And Reference Books:

 

  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.
Essential Reading / Recommended Reading

 

  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
Evaluation Pattern

CIA I: 20 %

CIA II: 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%  

MEA232N - MACROECONOMIC THEORY AND POLICY-II (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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. 

Learning Outcome

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

• Identify the determinants of various macroeconomic aggregates such as output,  unemployment, inflation, productivity, and the major challenges associated with the measurement of these aggregates. 

• Understand the theoretical foundation of macroeconomics and the contribution of different schools of thought to the further development of macroeconomics. • Describe the main macroeconomic theories of short-term fluctuations and long-term growth in the economy. 

• Analyse the existing idea of different schools of thought/ theories. To have some  idea on why those theories have not been able to influence/ different economic  conditions 

• Understand the factors influencing the Balance of Payment and analyze the cause of disequilibrium in the Balance of payment.

• Evaluate the consequences of basic macroeconomic policy options under differing economic conditions within a business cycle. 

Unit-1
Teaching Hours:15
Inflation, Unemployment and Productivity
 

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. 

Unit-2
Teaching Hours:15
Theories of Money Demand and Interest rates
 

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. 

Unit-3
Teaching Hours:15
Business Cycles, post Keynesian Macroeconomics
 

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. 

Unit-4
Teaching Hours:15
External Sector and Emerging Issues
 

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. 

Text Books And Reference Books:

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

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

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

Essential Reading / Recommended Reading

1. Burda and Wyplosz (2009). Macroeconomics: A European Text, Fifth Edition, Oxford  University Press, New York. 

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

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

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

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

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

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

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

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

Evaluation Pattern

CIA I : 20 %

CIA II : 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%  

MEA233N - ECONOMETRIC METHODS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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.

Learning Outcome

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

 

• Acquire the analytical and critical skills relevant to econometric analysis

• Demonstrate the rigorous quantitative training that analytical economics  requires, 

• Apply the econometric methods to micro and macro level real-world economic problems

Unit-1
Teaching Hours:12
Regression Analysis
 

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 

Unit-2
Teaching Hours:12
Extension of Linear Regression Models
 

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

Unit-3
Teaching Hours:12
Dummy Variables
 

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

Unit-4
Teaching Hours:12
Simultaneous Equation Models
 

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 

Unit-5
Teaching Hours:12
Distributed Lag Models
 

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 

Text Books And Reference Books:

1. Wooldridge, J., Introductory Econometrics: A Modern Approach, South-Western

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

Essential Reading / Recommended Reading

1. Johnston, J., Econometric Methods, third edition, McGraw Hill 

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 

Evaluation Pattern

CIA I : 20 %

CIA II : 25 % (Mid Semester Examination)

CIA III: 20 %

Attendance: 05 %

ESE: 30%  

MEA234N - STATISTICS USING R (2021 Batch)

Total Teaching Hours for Semester:90
No of Lecture Hours/Week:5
Max Marks:150
Credits:6

Course Objectives/Course Description

 

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 

Learning Outcome

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

  • Understanding data using statistical tool 
  • Perform graphical representation of data using R 
  • Apply their knowledge of various tools to create R programs 
  • Design and create applications that can handle multivariate data.
  • Find correlation and apply data visualization 

Unit-1
Teaching Hours:12
Introduction and preliminaries
 

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 

Unit-2
Teaching Hours:12
Lists and Data Frames
 

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. 

Unit-3
Teaching Hours:12
Data Exploration for Univariate and Bivariate
 

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 

Unit-4
Teaching Hours:12
Data Exploration for Multivariate Data
 

Data Exploration for Multivariate Data-Multivariate Data -Storing multivariate data  in R data frames, Accessing and manipulating data in R data frames, view multivariate  data, apply( ) family functions - apply( ), sapply( ), lapply( ), tapply( ), dplyr package select( ), filter( ), arrange( ), rename( ), mutate( ), group_by( ), %>%,  summarize( )

 

Unit-5
Teaching Hours:12
Correlation and Data Visualization
 

Pearson correlation, Spearman rank correlation lattice package in R - 1D, 2D, 3D plots using lattice, ggplot2 package in R- 1D, 2D, 3D plots using ggplot2   

 

Unit-6
Teaching Hours:30
Lab Programs
 

 1. Demonstrate the usage of Numbers and Vectors in R 

2. Simple manipulations on Numbers and Vectors, Objects- modes and attributes,  Ordered and unordered Factors 

3. Implement the concepts of Arrays and Matrices 

4. Demonstrate the usage of Data Frames and Lists and its attributes -attach,  detach, scan and importing a file 

5. Implement the concept of grouping and conditional execution on Data Frames  and Lists 

6. Demonstrate repetitive executions on Data Frames 

7. Use a Dataset to handle the Categorical and numerical data 

8. Use a Dataset to handle the Bi-variate categorical data 

9. Use a Dataset to handle the Multivariate categorical data 

10. Demonstrate the usage of apply () functions. 

11. Implement the usage of dplyr package 

12. Utilize a lattice package to plot 1D, 2D and 3D plots for a given dataset.

13. Utilize ggplot2 package to plot 1D, 2D and 3D plots for a given dataset.

14. Demonstrate Pearson correlation and Spearman rank correlation. 

 

 

Text Books And Reference Books:

1.Allen B. Downey, “Think Python: How to Think Like a Computer Scientist‘‘, 2nd  edition, Updated for Python 3, Shroff/O‘Reilly Publishers, 2016 

Essential Reading / Recommended Reading

1. John V Guttag, ―Introduction to Computation and Programming Using Python  ‘‘, 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.

Evaluation Pattern

CIAs only

 

 

 

MEA235N - RESEARCH MODELLING AND IMPLEMENTATION (2021 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
Credits:2

Course Objectives/Course Description

 

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.

Learning Outcome

Develop a strong theoretical background which would help the students to better understand applicability of various methods and tools in different economic contexts or scenarios.

Unit-1
Teaching Hours:30
Research Modelling and Implementation
 

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)

Text Books And Reference Books:

Text Books

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.

Essential Reading / Recommended Reading

Research articles and publication from peer reviewed journals and established government reports.

Evaluation Pattern

CIA I

CIA II

Total

 

 

Marks and Weightage

30 marks / 60 %

20 Marks / 40 %

50 Marks/100 %

MEA241AN - MULTIVARIATE ANALYSIS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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. 

Learning Outcome

Demonstrate knowledge and understanding of parametric and nonparametric  tests 

Understand discriminant analysis, factor analysis 

Apply Principal component analysis in medical, industrial, engineering, business and many other scientific areas. 

                   Solve the Industrial and real world problems 

Unit-1
Teaching Hours:12
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. 

Unit-2
Teaching Hours:12
Multivariate Normal distribution
 

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

Unit-3
Teaching Hours:12
Applications of Multivariate Analysis
 

Discriminant Analysis, Principal Components  Analysis and Factor Analysis.

Unit-4
Teaching Hours:12
Nonparametric Tests
 

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

Unit-5
Teaching Hours:12
Non Parametric Techniques
 

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

Text Books And Reference Books:

1. Anderson, T.W. (2003): An Introduction to Multivariate Statistical Analysis,  3rdEdn., John Wiley 

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

Essential Reading / Recommended Reading

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

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. 

Evaluation Pattern

 

 

CIA I

CIA II

CIA III

ESE

Attendance

20%

25%

20%

30%

5%

MEA242AN - APPLIED INSTITUTIONAL ECONOMICS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 
  • 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 

Learning Outcome

  • Upon successful completion of this course, the students will be able
  • to identify the types and nature of institutions and their impact on economic development. Identify the reason for institutional failures and its impact on the economic prosperity of nations. 
  • Analyze of the institutional structure of society 
  • Use the concept of transaction costs in the explanation of institutions, business  practices, and contract types
  • Analyse the behaviour of the firm based on its property rights structure • Conduct economic analysis of the behaviour of the state 
  • Analyse institutional changes

Unit-1
Teaching Hours:12
Introduction to Institutional Economics
 

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. 

Unit-2
Teaching Hours:12
Transaction Costs and Bounded Rationality
 

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. 

Unit-3
Teaching Hours:12
Contract Theories
 

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. 

Unit-4
Teaching Hours:12
Institutions of Property Rights
 

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. 

Unit-5
Teaching Hours:12
Applications of NIE
 

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. 

Text Books And Reference Books:

1. North, Douglas C. (2004). Institutional Change and Economic Performance. Cambridge  University Press. 

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

 

Essential Reading / Recommended Reading
  1. Olson Mancur. (1965). The Logic of Collective Action. Cambridge: Harvard University  Press. 
  2. Shaw, M E. (1971). Group Dynamics: The Psychology of Small Group Behaviour. New  York: McGraw Hill. 
  3. Frank, Robert H. (1991). Microeconomics and Behaviour. McGraw Hill International  Editions. 
  4. Pindyck, Robert S., Rubinfeld, Daniell L., & Mehta, Prem L. (2009). Microeconomics. (7th  Ed.). Pearson. 
Evaluation Pattern

CIA 1-20%

CIA 2-25%

CIA 3- 20%

End Sem-30%

Attendance-5%

MEA272N - PREDICTIVE ANALYTICS (2021 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:4

Course Objectives/Course Description

 

 

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

Learning Outcome

Demonstrate knowledge and understanding of prdictive analysis.

Application of techniques in medical, industrial, engineering, business  and many other scientific areas.

  • Solve the Industrial and real-world problems

Unit-1
Teaching Hours:12
Introduction to Predictive Modelling and Process of Predictive Modelling
 

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

 

Unit-2
Teaching Hours:16
Predictive Models- Regression
 

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

Unit-3
Teaching Hours:12
Time Series Analysis
 

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.

 

Unit-4
Teaching Hours:16
Classification and Clustering Methods
 

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

Unit-5
Teaching Hours:4
Association Techniques
 

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

 

Text Books And Reference Books:

Forte, R. M. (2015). Mastering Predictive Analytics with R. Packt Publishing Ltd.

Essential Reading / Recommended Reading

1. Wu, J., & Coggeshall, S. (2012). Foundations of Predictive Analytics. Boca Raton: CRC Press, Taylor & Francis Group LLC.

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. 

Evaluation Pattern

 

 

CIA I

CIA II

CIA III

ESE

Attendance

20%

25%

20%

30%

5%