Department of ECONOMICS NCR

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

 
1 Semester - 2023 - 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 PRINCIPLES OF DATA SCIENCE Core Courses 3 3 100
MEA134N MATHEMATICAL FOUNDATION FOR DATA ANALYTICS Core Courses 4 4 100
MEA135N STATISTICAL METHODS FOR ECONOMICS Core Courses 4 4 100
MEA136N RESEARCH METHODOLOGY Core Courses 2 2 50
MEA171N PYTHON PROGRAMMING Core Courses 5 4 100
2 Semester - 2023 - 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 ADVANCED MATHEMATICAL ECONOMICS Core Courses 4 4 100
MEA235N RESEARCH MODELLING Core Courses 2 2 50
MEA241AN MULTIVARIATE ANALYSIS Discipline Specific Elective Courses 4 4 100
MEA242BN FINANCIAL ECONOMICS Discipline Specific Elective Courses 4 4 100
MEA271N R FOR ANALYTICS Core Courses 6 5 150
3 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MEA331N INTERNATIONAL ECONOMICS Core Courses 4 4 100
MEA332N ECONOMICS OF GROWTH AND DEVELOPMENT Core Courses 4 4 100
MEA333N APPLIED ECONOMETRICS Core Courses 4 4 100
MEA341AN BEHAVIORAL ECONOMICS Discipline Specific Elective Courses 4 4 100
MEA371N APPLIED MACHINE LEARNING Core Courses 6 5 150
MEA372BN BUSINESS INTELLIGENCE Discipline Specific Elective Courses 5 4 150
MEA381PN SPECIALIZATION PROJECT Discipline Specific Elective Courses 4 2 100
4 Semester - 2022 - Batch
Course Code
Course
Type
Hours Per
Week
Credits
Marks
MEA481N INDUSTRY INTERNSHIP Core Courses 0 10 300
MEA482N RESEARCH PUBLICATION Core Courses 0 2 100
      

    

Department Overview:

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

Mission Statement:

Vision

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

 Mission

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

Introduction to Program:

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

Program Objective:
Assesment Pattern

CIA - 70%

ESE - 30% 

Examination And Assesments

CIA - 70% 

ESE - 30% 

MEA131N - MICROECONOMIC THEORY AND APPLICATIONS-I (2023 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 firms and various forms of market.

Course Outcomes

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

       Demonstrate an understanding, usage and application of basic economic principles.

       Describe and apply the methods for analyzing consumer behavior through demand and supply, elasticity and marginal utility

       Identify and appraise various models of how markets are organized, and the price and output decisions for maximizing profit.

        Demonstrate the rigorous quantitative training that analytical economics requires.

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

Learning Outcome

CO1: Demonstrate an understanding, usage and application of basic economic principles.

CO2: Describe and apply the methods for analyzing consumer behavior through demand and supply, elasticity and marginal utility

CO3: Identify and appraise various models of how markets are organized, and the price and output decisions for maximizing profit.

CO4: Demonstrate the rigorous quantitative training that analytical economics requires.

CO5: Apply the microeconomic theory to micro-level real world economic problems.

Unit-1
Teaching Hours:10
Introduction, Demand and Supply Analysis
 

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

Unit-2
Teaching Hours:15
Theory of Consumer Behavior
 

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

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

Unit-3
Teaching Hours:20
Theory of Cost, Revenue and Production
 

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

Unit-4
Teaching Hours:15
Price and Output Determination
 

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

Text Books And Reference Books:
  1. Pindyck, Robert & Rubinfeld, Daniel (2013), Micro Economics, 8th Edition, Pearson Education, USA.
  2. Besanko, D. and Braeutigam, R. (2015) Microeconomics, 5th Edition, Wiley India
Essential Reading / Recommended Reading
  1. Andreu Mas-Colell, M D Whinston and J R Green (1995), Microeconomic Theory, Oxford University Press.
  2. Kreps, D. M. (1990), A Course in Microeconomic Theory, Princeton University Press, Princeton.
  3. Krugman, Paul. and Wells, R. (2005), Microeconomics, Worth Publishers.
  4. Koutsoyiannis, A. (1979), Modern Microeconomics, (2nd Edition), Macmillan Press, London.
  5. Sen, A (2007), Microeconomics: Theory and Applications, Oxford University Press, New Delhi.
  6. Varian, H. (2000), Microeconomic Analysis, W.W. Norton, New York.

 

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

 

Evaluation Pattern

CIA I-20%

Mid Sem- 25%

CIA III-20%

END Sem- 30%

Attendance

MEA132N - MACROECONOMIC THEORY AND POLICY-I (2023 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

CO1: Identify the determinants of various macroeconomic aggregates such as output, unemployment, inflation, productivity and the major challenges associated with the measurement of these aggregates.

CO2: Understand the theoretical foundation of macroeconomics and the contribution of different schools of thought to the further development of macroeconomics.

CO3: Describe the main macroeconomic theories of short-term fluctuations and long-term growth in the economy.

CO4: Analyze the existing idea of different schools of thought/ theories. To check whether the ideology of those theories is working practically? To have some idea on why those theories have not been able to influence/ different economic conditions

CO5: Understand the factors influencing the Balance of Payment and analyse the cause of disequilibrium in the Balance of payment.

Unit-1
Teaching Hours:10
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 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:10
Supply of Money and Demand for Money
 

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

Unit-4
Teaching Hours:10
Demand for Money and Post-Keynesian approaches
 

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

Unit-5
Teaching Hours:10
Open-Economy Macroeconomics
 

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

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

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

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.

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

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

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

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

Evaluation Pattern

CIA I- 20%

MID Term- 25%

CIA III- 20%

END Term- 30%

Attendance- 5%

MEA133N - PRINCIPLES OF DATA SCIENCE (2023 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The principles of data science deals with the econometric scientific methods of analyzing data. Today, we live in a big data world, where the amount of data generated

everyday is very huge, therefore we need methods to clearly transform and analyze data. Therefore, machine learning, which is included in this syllabus, does the job.

Also, the students here are introduced into different scenarios and methodologies to get results out of data.

Learning Outcome

CO1: Understand the modern big data econometric methods.

CO2: Annotate empirical data modelling with machine learning algorithms.

CO3: Experiment econometric prediction based on the data analytics.

Unit-1
Teaching Hours:9
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:9
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:9
Machine Learning
 

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

Unit-4
Teaching Hours:9
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:9
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

Unit-5
Teaching Hours:9
Data Visualization
 

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

Text Books And Reference Books:

1.Godsey, B. (2017). Think Like a Data Scientist, Manning Publications

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

Essential Reading / Recommended Reading

1.Grus, J. (2019). Data science from scratch: first principles with python. O'Reilly Media, Inc.

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

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

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

Evaluation Pattern

CIA 70%

ESE 30%

MEA134N - MATHEMATICAL FOUNDATION FOR DATA ANALYTICS (2023 Batch)

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

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Understand the properties of Vector spaces.

CO2: Use the properties of Linear Maps in solving problems on Linear Algebra.

CO3: Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces.

CO4: Apply mathematics for some applications in Data Analytics.

Unit-1
Teaching Hours:15
INTRODUCTION TO VECTOR SPACES
 

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

Unit-2
Teaching Hours:15
LINEAR MAPS
 

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

Unit-3
Teaching Hours:15
EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES
 

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

Unit-4
Teaching Hours:15
BASIC MATRIX METHODS FOR APPLICATIONS
 

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

Text Books And Reference Books:

1. S. Axler, Linear algebra done right, Springer, 2017. 

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

Essential Reading / Recommended Reading

1. E. Davis, Linear algebra and probability for computer science applications, CRC Press, 2012.

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

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

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

Evaluation Pattern

CIA 70%

ESE 30%

MEA135N - STATISTICAL METHODS FOR ECONOMICS (2023 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

CO1: Demonstrate the history of statistics and present the data in various forms.

CO2: Infer the concept of correlation and regression for relating two or more related variables.

CO3: Demonstrate the probabilities for various events.

CO4: Identify various discrete and continuous distributions and their usage.

CO5: Apply sampling distributions to various Economic data .

Unit-1
Teaching Hours:10
Descriptive Statistics
 

Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data. Types of Measurements. Graphical and tabular representation of data. 

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

Unit-2
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-3
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-4
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.

Unit-5
Teaching Hours:15
Jointly distributed Random Variables
 

Joint distribution of vector random variables – joint moments – covariance – correlation - independent random variables conditional distribution – conditional expectation - sampling distributions: chi-square, t, F (central).

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%

MEA136N - RESEARCH METHODOLOGY (2023 Batch)

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

Course Objectives/Course Description

 

This course enables the students to

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

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

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

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

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

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

Learning Outcome

CO1: Demonstrate the knowledge of the range of qualitative and quantitative research methods potentially available.

CO2: Differentiate between the role of practitioners and the role of researchers

CO3: Demonstrate the small-scale research project independently

CO5: Demonstrate the understanding of and ability to critically reflect upon issues of ethics and research

Unit-1
Teaching Hours:8
Introduction
 

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

Unit-2
Teaching Hours:6
Review of Literature
 

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

Unit-3
Teaching Hours:8
Sampling Design
 

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

Unit-4
Teaching Hours:8
Hypothesis Testing and Report Writing
 

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

Text Books And Reference Books:

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

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

Essential Reading / Recommended Reading

1.     Keller G. (2017) Statistics for Management and Economics, 11th Edition. Cengage Learning.

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

Evaluation Pattern


CIA Only

CIA I- 10 Marks

CIA 2-10 Marks

CIA 3-30 Marks

MEA171N - PYTHON PROGRAMMING (2023 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:100
Credits:4

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Demonstrate the use of built-in objects of Python

CO2: Demonstrate significant experience with python program development environment

CO3: Implement numerical programming, data handling and visualization through NumPy, Pandas and MatplotLib modules

Unit-1
Teaching Hours:15
INTRODUCTION TO PYTHON
 

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

Lab Programs:- 

1. Demonstrate usage of branching and looping statements

2. Demonstrate Recursive functions

3. Demonstrate Lists

 

 

Unit-2
Teaching Hours:17
SEQUENCE DATA TYPES AND OBJECT-ORIENTED PROGRAMMING
 

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

Lab programs :-

4. Demonstrate Tuples and Sets

5. Demonstrate Dictionaries

6.Demonstrate inheritance and exceptional handling

7. Demonstrate use of “re”

 

 

Unit-3
Teaching Hours:13
USING NUMPY
 

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

Structured Data: NumPy’s Structured Array.

Lab programs :-

8. Demonstrate Aggregation

9. Demonstrate Indexing and Sorting

 

 

Unit-4
Teaching Hours:17
DATA MANIPULATION WITH PANDAS -I
 

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

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

Lab Programs:- 

10. Demonstrate handling of missing data

11. Demonstrate hierarchical indexing

12. Demonstrate usage of Pivottable

13. Demonstrate use of andquery()

 

 

Unit-5
Teaching Hours:13
VISUALIZATION AND MATPLOTLIB
 

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

Lab programs:- 

14. Demonstrate ScatterPlot

15. Demonstrate 3D plotting sciPy

 

 

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

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

Essential Reading / Recommended Reading
  1. JoelGrus (2016), DataScience from Scratch First Principles with Python, O’Reilly Media,

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

Evaluation Pattern

CIA- 100%

MEA231N - MICROECONOMIC THEORY AND APPLICATIONS-II (2023 Batch)

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

Course Objectives/Course Description

 

Course Objectives

The main objective of the course is to introduce both traditional as well as modern ideas and theoretical concepts in microeconomics. It also deals with fundamental understanding of market theory, theory of factor pricing, theory of general equilibrium and welfare economics.

Course Outcomes

After successful completion of the course students will be able to

      Assess the assumptions made in micro-economic literature that applies microeconomics, game theory and information economics.

      Acquire additional theorethical knowledge at an advanced level.

      Demonstrate the rigorous quantitative training that analytical economics requires.

       Design micro-economic models for various real-world problems.

Learning Outcome

CO1: Assess the assumptions made in micro-economic literature that applies microeconomics, game theory and information economics.

CO2: Acquire additional theorethical knowledge at an advanced level.

CO3: Demonstrate the rigorous quantitative training that analytical economics requires.

CO4: Design micro-economic models for various real-world problems.

Unit-1
Teaching Hours:10
Factor Pricing
 

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

Unit-2
Teaching Hours:10
Market Failure and Game Theory
 

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

Unit-3
Teaching Hours:10
Non- Collusive Oligopoly
 

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

Unit-4
Teaching Hours:15
Theories of Distribution
 

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

Unit-5
Teaching Hours:15
General Equilibrium and Welfare Economics
 

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

Text Books And Reference Books:

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

 

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

Essential Reading / Recommended Reading

1.       Furubotn, E. G., & Richter, R. (2010). Institutions and economic theory: The contribution of the new institutional economics. University of Michigan Press.

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

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

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

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

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

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

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

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

Evaluation Pattern

CIA I: 20

CIA II: 25

CIA III:20

ESE: 30

Att: 05

MEA232N - MACROECONOMIC THEORY AND POLICY-II (2023 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

CO1: Understand the structure and various approaches of interest rate.

CO2: Analyze the factors that influence the demand for money.

CO3: Equip the student with skills to analyze the phases and working of the Business cycles

CO4: To assist students in understanding the foundations of post-Keynesian economics and helping them apply these ideas to their own lives and the society they live in.

Unit-1
Teaching Hours:15
Theories of the Interest Rate
 

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

Unit-2
Teaching Hours:15
The Demand for Money and Term structure of Interest Rates
 

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

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

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

Unit-4
Teaching Hours:15
Post Keynesian Macroeconomics and Rational Expectation revolution
 

 The New Classical critique of Micro-foundations, the New Classical Approach; Policy Implications of New Classical Approach — empirical evidence. Rational Expectation Revolution.

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).  Macroeconomics,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%

Mid-Term-25%

CIA-III- 20%

END Term-30%

Attendance-5%

MEA233N - ECONOMETRIC METHODS (2023 Batch)

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

Course Objectives/Course Description

 

On completion of the course students should be able to:

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

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

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

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

Learning Outcome

CO1: Understand the methodology of econometric research.

CO2: Comprehend the assumptions upon which different econometric methods are based and their implications.

CO3: Demonstrate their understanding of applied econometric analysis with respect to model estimation and interpretation of results.

CO4: Perform post-estimation diagnostic tests.

CO5: Ability to estimate and interpret models with qualitative regressors.

Unit-1
Teaching Hours:10
Introduction to Econometrics
 

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

Unit-2
Teaching Hours:15
Simple and Multiple Regression
 

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

Unit-3
Teaching Hours:15
Relaxing OLS Assumptions
 

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

Unit-4
Teaching Hours:10
Model Specification and Diagnostic Testing
 

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

Unit-5
Teaching Hours:10
Dummy Variables
 

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

Text Books And Reference Books:

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

 

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

Essential Reading / Recommended Reading

1.          Koutsoyiannis, A. (1979). Theory of Econometrics (2nd Ed.) Palgrave Macmillan.

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

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

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

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

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

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

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

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

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

 

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

Evaluation Pattern

CIA 1-20%

Mid Sem- 25%

CIA 3- 20%

END Sem- 30%

Attendance- 5%

Total-100%

 

MEA234N - ADVANCED MATHEMATICAL ECONOMICS (2023 Batch)

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

Course Objectives/Course Description

 

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

Learning Outcome

CO1: Exhibit a sound understanding of mathematical techniques discussed.

CO2: Formulate economic problems in mathematical terms.

CO3: Apply 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

 

 

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. P., & Blume, L. (1994). Mathematics for economists (Vol. 7). New York: Norton.

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

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

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

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

Essential Reading / Recommended Reading

1. Allen R G D (1974). Mathematical Analysis for Economists, McMillan Press and ELBS, London. 2. Wainwright, K. (2005). Fundamental methods of mathematical economics/Alpha C. Chiang, Kevin Wainwright. Boston, Mass.: McGraw-Hill/Irwin,.

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

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

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

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

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

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

Evaluation Pattern

CIA I    : 20 %

CIA II   : 25 % (Mid Semester Examination)

CIA III  : 20 %

Attendance: 05 %

ESE       : 30%  

MEA235N - RESEARCH MODELLING (2023 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 carry out research. The course enables the students to 

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

 

Learning Outcome

CO1: Develop a strong theoretical background

CO2: To understand the 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, Conclusions and Scope for future enhancements 

● Plagiarism report 

 

Text Books And Reference 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 publications from peer-reviewed journals and established government reports. 

 

Evaluation Pattern

CIA I-30 (Internal - Guide)

CIA II-20 (External)

MEA241AN - MULTIVARIATE ANALYSIS (2023 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.
  • Introduce the measurement of the relationship of quantitative and qualitative data and the concept of probability.
  • Understand and apply the descriptive measures and probability for data analysis.

Learning Outcome

CO1: Demonstrate knowledge and understanding of parametric and nonparametric tests

CO2: Understand discriminant analysis, factor analysis

CO3: Apply Principal component analysis in medical, industrial, engineering, business and many other scientific areas

CO4: Solve the Industrial and real world problems

Unit-1
Teaching Hours:12
UNIT 1
 

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
UNIT 2
 

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
UNIT 3
 

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

Unit-4
Teaching Hours:12
UNIT 4
 

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

Unit-5
Teaching Hours:12
UNIT 5
 

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. Hardle, W. K. & Simar, L. (2019). Applied Multivariate Statistical Analysis (5th ed.) Springer.

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

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

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

Essential Reading / Recommended Reading

1. Mukhopadhyay, P. (2016). Mathematical Statistics. Books and Allied.

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

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

Evaluation Pattern

CIA: 70%

ESE: 30%

MEA242BN - FINANCIAL ECONOMICS (2023 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 the students to

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

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

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

Learning Outcome

CO1: Apply economics models to understand the functions of financial markets and products.

CO2: Analyze, interpret and present financial data

CO3: Explain the alternative approaches to economic problems

Unit-1
Teaching Hours:10
The Demand for Securities
 

The Demand for Securities

The time dimension – Present value and duration – The calculation of yields on zero-coupon bonds – The term structure of interest rates – The risk dimension – Measurement of risk. Bivariate distributions–Conditional probabilities and expected values – Estimating the mean and variance of returns – Expected utility.

Unit-2
Teaching Hours:10
The Supply of Securities Regulations Governing Supply of Securities
 

The Supply of Securities Regulations Governing Supply of Securities

– General characteristics of securities – Government bonds – Index linked bonds – Corporate Securities – equities, bonds, convertible securities – Stock market operations – Money market funds – Claims on financial institutions.

Unit-3
Teaching Hours:10
Securities Markets and Efficiency of Stock exchanges
 

Securities Markets and Efficiency of Stock exchanges

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

Unit-4
Teaching Hours:10
The Determination of Equity Prices
 

The Determination of Equity Prices

Shares as claims on future dividends and on corporate net worth – The Capital Asset Pricing Model (CAPM) – The simplest form – Estimating betas- Implications for portfolio management – Validity of CAPM – Arbitrage Pricing theory. An alternative approach – Stock indices – Bombay Sensitive Index, Bombay National Index, Dow Jones Industrial Index(DJI), New York Stock exchange composite index(NYSE).

Unit-5
Teaching Hours:10
Security Analysis and Market Efficiency
 

Security Analysis and Market Efficiency

A modern view of security analysis – Macroeconomic developments and securities markets– Performance of securities markets – Industry growth, structure and firm specific factors- Uses and pitfalls of Price / Earnings ratios.

Unit-6
Teaching Hours:10
Financial Instruments
 

Financial Instruments

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

Text Books And Reference Books:

1. Chandra, P. (2017), Investment Analysis and Portfolio Management (5th edition), McGraw Hills Education.

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

3. Campbell, J. Y., Lo, A. W., & Mackinlay, A. C. (1997). The Econometrics of Financial Markets (2nd edition) Princeton University Press.

Essential Reading / Recommended Reading

1. Houthakker H. S. & Williamson P. J. (1996), The Economics of Financial Markets, Oxford University Press

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

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

4. Fabozzi (2009), Bond Markets (7th revised edition), Pearson Publications, USA, February 27

5. Fama E.F. (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, 25 May, pp. 383-417

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

7. Graves, Affleck, Hegde, J.S. & Miller, R. (1994),Trading Mechanisms and the Components of the Bid Ask Spread, Journal of Finance, 44, pp. 1471-1488.

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

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

9. Black, F., Jensen, M.C. & Scholes, M.A. (1972),The Capital Asset Pricing Model: Some Empirical Tests in M.C. Jensen (ed.), Studies in the Theory of Capital Markets, Praeger, New York.

Evaluation Pattern

CIA - 70%

ESE - 30%

MEA271N - R FOR ANALYTICS (2023 Batch)

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

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

CO1: Understanding data using statistical tool

CO2: Demonstrate graphical representation of data using R

CO3: Apply their knowledge of various tools create R programs

CO4: Design and create applications which can handle multivariate data.

CO5: Evaluate the correlation between data and apply Exploratory Data Analysis

Unit-1
Teaching Hours:10
Introduction and preliminaries
 
  1. 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:10
Lists and Data Frames
 
  1. 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:10
Data Exploration for Univariate and Bivariate
 
  1. 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:10
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:10
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:10
Regression and Diagnostic Tests
 

Multiple Regression, Qualitative Regressor Models, Qualitative Response Regression Models, Checking the assumptions of Regression Model and Model Diagnostics.

Unit-7
Teaching Hours:30
Lab Programs
 

Lab Programs Hours: 30

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

14. Demonstrate Pearson correlation and Spearman rank correlation.

15. Demonstrate the use of Qualitative and Quantitative Response Regression Models in establishing statistical association between variables.

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

2. Kerns, J. (2010).“Introduction to Probability and Statistics Using R” (First Edition).

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

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

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

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

Evaluation Pattern
CIA-I CIA-II CIA-IiI CIA-IV Attendance
35 35 35 40 5

MEA331N - INTERNATIONAL ECONOMICS (2022 Batch)

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

Course Objectives/Course Description

 

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

Learning Outcome

This course enables students to Understand international and inter regional trade,  Identify and understand various trade theories, analyze the various types of restrictions of international trade  Analyze the links between trade, international finance, economic growth and globalization, with a particular emphasis on the experiences of developing countries.  Analyze the relationship between Foreign Trade Theory and Economics Development.  Critically evaluate the consequences of some ofthe International Trade policy.  Critically comment on and participate in current debates on international economic policy

Unit-1
Teaching Hours:12
Core Trade Models
 

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

Unit-2
Teaching Hours:12
Heckscher-Ohlin Theory and Empirics
 

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

Unit-3
Teaching Hours:12
New Trade Theories
 

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

Unit-4
Teaching Hours:12
Trade Policies under Alternative Assumptions
 

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

Unit-5
Teaching Hours:12
Balance of Payment and Exchange rate
 

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

Text Books And Reference Books:

1. Krugman, P. R., Obstfeld, M. & Melitz, M.(2018). International Economics: Theory and Policy (11th ed.).

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

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

Essential Reading / Recommended Reading

1. Feenstra, R. C. (2004), Advanced InternationalTrade:Theory andEvidence. Princeton University Press.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Dynamics of Constructed Advantage. Pinter.

Evaluation Pattern
AssessmentCIA 1CIA 2CIA 3AttendanceESE
Marks 20 25 20 5 30

MEA332N - ECONOMICS OF GROWTH AND DEVELOPMENT (2022 Batch)

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

Course Objectives/Course Description

 

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

Learning Outcome

1: Use both classical and modern theories of growth and development to analyze the problems of the developing world.

2: Understand the roles of population growth and human capital in the development problem.

3: Analyze macroeconomic policies aimed at facilitating development and their implications.

4: Use the tools developed in this course to analyse the development problems of selected nations.

5: Enable students to understand critical issues in Neo-classical and other growth models.

Unit-1
Teaching Hours:10
Introducing Economic Development
 

Meaning of Economic Growth and Economic Development. Measures of Economic Development- GDP, GDP per capita, PQLI and HDI, The New Economic View of Development; Amartya Sen’s “Capability” Approach; Development and Happiness ; Three Core Values of Development;; The Three Objectives of Development. The Millennium Development Goals; Sustainable Development Goals.

Unit-2
Teaching Hours:16
Classic Theories of Economic Growth and Development
 

Four Approaches of classical theories of growth; Development as Growth and the Linear Stages Theories ; Rostow’s Stages of Growth; The Harrod- Domar Growth Model; Obstacles and Constraints Necessary versus Sufficient Conditions: Some Criticisms of the Stages Model; Structural-Change Models-The Lewis Theory of Development;: Structural Change and Patterns of Development; The International-Dependence Revolution- The Neocolonial Dependence.

Unit-3
Teaching Hours:16
The Neoclassical Models of Growth
 

Growth Models with Exogenous Saving Rates (the Solow–Swan Model); The Fundamental Equation of the Solow–Swan Model; The Steady State; The Golden Rule of Capital Accumulation and Dynamic Inefficiency; Meade’s Model of Economic Growth, Kaldor’s Model of Growth, Ricardian equivalence; Models of Endogenous Growth-Theoretical Dissatisfaction with Neoclassical Theory- The AK Model - Long run AK model – AK model with externalities New Growth theory: Human capital, Externalities and ideas; endogenous technological progress and development.

Unit-4
Teaching Hours:8
Contemporary Models of Development and Underdevelopment
 

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

Unit-5
Teaching Hours:10
Cambridge Capital Controversy in the Neo-classical Analysis of Growth
 

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

Text Books And Reference Books:

1.  Barro, R. J. & X. Sala-i-Martin (2003). Economic Growth (2nd ed.). MIT Press.

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

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

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

Essential Reading / Recommended Reading

1. Ray, D. (2009). Development Economics. Princeton University Press.

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

Evaluation Pattern

CIA 1- 20%

MID-Term- 25%

CIA 3- 20%

ESE- 30%

 

Attendance- 5

MEA333N - APPLIED ECONOMETRICS (2022 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

  • Understand core concepts and techniques in econometrics, with a special focus

    on the time series and panel data econometrics.

  • Understand the assumptions upon which different econometric methods are

    based and their implications.

  • Demonstrate the rigorous quantitative training that analytical economics

    requires.

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

    economic events.

  • Develop practical skills, which are necessary to perform independent research.

  • Read and interpret applied economic articles.

Learning Outcome

CO1: Understand core concepts and methods used in the estimation of economic relationships.

CO2: Demonstrate the analytical and critical skills relevant to economic thinking.

CO3: pply econometric software packages to employ various techniques taught using various types of data.

CO4: Interpret and critically evaluate applied work and econometric findings.

Unit-1
Teaching Hours:15
A Review of Regression Analysis
 

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

Unit-2
Teaching Hours:10
Model Specification and Diagnostic Testing
 

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

Unit-3
Teaching Hours:10
Qualitative response models
 

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

Unit-4
Teaching Hours:15
Time Series Econometrics
 

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

Unit-5
Teaching Hours:10
Panel Data Econometrics
 

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

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

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

Essential Reading / Recommended Reading
  1. Koutsoyiannis, A. (1979). Theory of Econometrics (2nd Ed.) Palgrave Macmillan.

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

    Company.

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

    International Edition.

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

    Press.

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

    Wiley.

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

    Press.

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

    Approach. Palgrave Macmillan.

  8. Asteriou, D., & Hall, S. G. (2021). Applied Econometrics (4th ed.). Red Globe Press.9.

  9. Gujarati, D. (2015), Econometrics by Examples, Second Edition. Palgrave Macmillan. Hilmer, C. E & Hilmer, M. J. (2014). Practical econometrics. McGraw Hill Education.

Evaluation Pattern

CIA I

CIA II

CIA III

ESE

Attendance

20

25

20

30

5

MEA341AN - BEHAVIORAL ECONOMICS (2022 Batch)

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

Course Objectives/Course Description

 

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

Learning Outcome

1: An understanding of the theoretical and empirical underpinnings of behavioural economics

2: Demonstrate how we can meaningfully predict and influence human behaviour 'for good'

3: Examine applications and case studies from real world policy settings

4: Develop a methodology, mindset, and framework to design and implement behavioural change techniques to policy problems

Unit-1
Teaching Hours:10
Introduction
 

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

Unit-2
Teaching Hours:10
Making Choices Under Risk: Prospect Theory
 

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

Unit-3
Teaching Hours:10
Inter temporal choice
 

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

Unit-4
Teaching Hours:10
Strategic interaction
 

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

Unit-5
Teaching Hours:10
Nudges, Policy and Happiness
 

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

Unit-6
Teaching Hours:10
Animal Spirits
 

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

Text Books And Reference Books:

1.     Wilkinson, N., & Klaes, M. (2012). An introduction to behavioural economics (2 p.) Palgrave Macmillan. New York.

2.     Akerlof, G. A., & Shiller, R. J. (2009). Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Gfobal Capitalism Princeton University Press.

3.     Bernheim, B. D., DellaVigna, S., & Laibson, D. (2019). Handbook of Behavioral Economics-Foundations and Applications 2. Elsevier.

4.     Diamond, P. A., & Vartiainen, H. (2007). Behavioral economics and its applications (pp. 1-336). Princeton, NJ: Princeton University Press.

5.     Dhami, S. (2020). The Foundations of Behavioral Economic Analysis: Volume VII: Further Topics in Behavioral Economics (Vol. 7). Oxford University Press, USA.

6.     Ianole, R. (Ed.). (2016). Applied Behavioral Economics Research and Trends. IGI Global.

Essential Reading / Recommended Reading
  1. Loewenstein (1987) “Anticipation and the Valuation of Delayed Consumption”. Economic Journal, 97(387): 666—684.
  2.  Oster, Emily, Ira Shoulson, and E. Ray Dorsey. 2013. "Optimal Expectations and Limited Medical Testing: Evidence from Huntington Disease." American Economic Review, 103(2): 804-30.
  3. Brunnermeier, Markus, K., and Jonathan A. Parker (2005). "Optimal Expectations." American Economic Review, 95(4): 1092-1118.
  4. Kahneman and Tversky (1979) “Prospect Theory: An Analysis of Decision Under Risk”, Econometrica, 47(2): 263–291.
  5. List (2003) “Does Market Experience Eliminate Market Anomalies?”, Quarterly Journal of Economics, 118(1): 41–71.
  6. Koszegi and Rabin (2006), “A Model of Reference-Dependent Preferences”, Quarterly Journal of Economics, 121(4): 1133–1165.
  7. Sydnor, Justin. 2010. "(Over)insuring Modest Risks." American Economic Journal: Applied Economics, 2(4): 177-99.
  8. Charness and Rabin (2002) “Understanding Social Preferences with Simple Tests” Quarterly Journal of Economics, 117(3): 817–869.
  9. Lazear, Edward P., Ulrike Malmendier, and Roberto A. Weber. 2012. "Sorting in Experiments with Application to Social Preferences." American Economic Journal: Applied Economics, 4(1): 136-63.10. 
  10. DellaVigna, List, Malmendier. 2012. “Testing for Altruism and Social Pressure in Charitable Giving”. Quarterly Journal of Economics, 127(1): 1–56.
  11. Rabin (1993) “Incorporating Fairness into Game Theory and Economics”, American Economic Review, 83(5): 1281–1302.
  12. Fehr and Gachter, (2000), “Fairness and Retaliation: The Economics of Reciprocity”, Journal of Economic Perspectives, 14(3): 159–181.
Evaluation Pattern

CIA I- 20%

Mid Sem- 25%

CIA III-20%

End Sem- 30%

Attendance -5%

MEA371N - APPLIED MACHINE LEARNING (2022 Batch)

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

Course Objectives/Course Description

 

This course enables students to

● Understand the differences between supervised and unsupervised machine learning models

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

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

● Implement machine learning models in various economics-related applications 

Learning Outcome

CO1: Understand the basic concepts, applications and different types of learning in respect of Machine Learning Algorithms.

CO2: Apply various supervised and unsupervised algorithms to various datasets and analyze the impact of hyperparameter tuning.

CO3: Compare and evaluate the performance of machine learning algorithms.

CO4: Evaluate advanced machine learning models with respect to benchmark discoveries and applications.

CO5: Create machine learning models to facilitate the application needs in economics domain.

Unit-1
Teaching Hours:10
INTRODUCTION TO MACHINE LEARNING
 

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

Unit-2
Teaching Hours:15
SUPERVISED LEARNING AND PROBABILISTIC APPROACHES
 

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

Unit-3
Teaching Hours:15
UNSUPERVISED LEARNING, DIMENSIONALITY REDUCTION AND ENSEMBLE LEARNING
 

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

Unit-4
Teaching Hours:10
STRATEGIES TO OVERCOME CHALLENGES
 

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

Unit-5
Teaching Hours:10
MACHINE LEARNING IN ECONOMICS
 

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

Unit-6
Teaching Hours:30
Lab Hours
 

Lab Programs Hours: 

1. Introduction to Machine Learning Libraries in Python

2. Data Exploration and Prediction of Numeric Values

3. Classification - Logistic Regression

4. Classification - KNN, Naive Bayes

5. Classification - Support Vector Machines

6. Clustering - K Means, Elbow Method

7. Clustering - Agglomerative Clustering

8. Dimensionality Reduction - PCA and LDA

9. Analysis of Time Series Data - Stock Market Prediction

10. Analysis of Supply and Demand Based Applications

11. Identifying Insurance/Loan Defaulters

12. Geographic Location based Data Analysis

Text Books And Reference Books:

1. Müller, A. C. & Guido, S. (2017). Introduction to Machine Learning with Python A Guide For Data Scientists. O’Reilly book.

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

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

Essential Reading / Recommended Reading

1. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.

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

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

Evaluation Pattern

CIAs only

MEA372BN - BUSINESS INTELLIGENCE (2022 Batch)

Total Teaching Hours for Semester:75
No of Lecture Hours/Week:5
Max Marks:150
Credits:4

Course Objectives/Course Description

 

The course enables students to,

        Create an Interactive dashboard from different datasets. 

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

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

Learning Outcome

CO1: Acquainted with Power BI

CO2: Create Datasets and Data Models

CO3: Create Reports and apply animation and Analytics Technique

CO4: Explore Dashboards, and model Data for Analytics

Unit-1
Teaching Hours:15
Transforming Data
 

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

Unit-1
Teaching Hours:15
Designing Import and Direct Query Data Models
 

The data model, Relationships, Model Metadata, Optimizing Performance 

Unit-1
Teaching Hours:15
Introduction to Power BI
 

Power BI deployment models, Dataset design process, Dataset Planning 

Unit-2
Teaching Hours:15
Creating and Formatting Power BI Reports
 

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

Unit-2
Teaching Hours:15
Applying Custom Visuals, Animation, and Analytics
 

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

Unit-3
Teaching Hours:15
Designing Power BI Dashboards
 

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

Unit-3
Teaching Hours:15
Creating Power BI Apps
 

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

Unit-4
Teaching Hours:30
Lab Programs
 

1.      Create workspace

2.      Set up power BI desktop option

3.      Get data from SQL Server

4.      Create Model Relationships

5.      Create and publish a Report

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

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

8.      Create an animated scatter chart, 

9.      Create a forecast 

10.  Publish an App 

Text Books And Reference Books:

Brett Powell, “Mastering  Microsoft Power BI”, Packt Publishing, 2018

Essential Reading / Recommended Reading

1.      Mike Morris, “Power BI: A complete step-by-step Guide for beginners in Understanding Power BI”, Albano Publishing, 2019. 

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

Evaluation Pattern

CIA=100%

MEA381PN - SPECIALIZATION PROJECT (2022 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:100
Credits:2

Course Objectives/Course Description

 

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

for the students.

Learning Outcome

CO1: Identify the problem and relevant analytics for the selected domain.

CO2: Apply appropriate design/development strategy and tools.

Unit-1
Teaching Hours:30
INTRODUCTORY PHASE
 

                                                

·      Project Topic identification 

 

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

Unit-2
Teaching Hours:30
PROJECT EVALUATION PHASE
 

 Final Report preparation

Final Report Submission

Text Books And Reference Books:

NA

Essential Reading / Recommended Reading

NA

Evaluation Pattern

Evaluation Pattern CIA only

MEA481N - INDUSTRY INTERNSHIP (2022 Batch)

Total Teaching Hours for Semester:0
No of Lecture Hours/Week:0
Max Marks:300
Credits:10

Course Objectives/Course Description

 

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

Internship Report:

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

Learning Outcome

CO1: To apply the knowledge of Economics and Analytics to undertake various tasks and duties assigned in the industry

CO2: To acquire industry-specific skills through practical experience, research and experiential learning.

CO3: To develop personal, interpersonal and societal skills.

Unit-1
Teaching Hours:0
NA
 

NA

Text Books And Reference Books:

1. Guide to report writing by Netzley

  (2010)

Essential Reading / Recommended Reading

NA

Evaluation Pattern

S.No.

Component

Marks

1

Weekly Progress 

100

2

Final Internship Report 

100

3

Presentation and Viva 

100

MEA482N - RESEARCH PUBLICATION (2022 Batch)

Total Teaching Hours for Semester:0
No of Lecture Hours/Week:0
Max Marks:100
Credits:2

Course Objectives/Course Description

 

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

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

Learning Outcome

  • To apply the knowledge of Economics and Analytics to undertake and publish a research article/paper.
  • To learn specific data analysis skills required for research in Economics and Analytics.
  • To acquire academic writing skills required by journals for publication purposes.

 

Unit-1
Teaching Hours:0
NA
 

NA

Text Books And Reference Books:

A Guide to Research and Publication Ethics A Text Book As per UGC Guidelines for UG, PG, MPhil and PhD, New Delhi Publisher

Essential Reading / Recommended Reading

NA

Evaluation Pattern

Publication in a reputed Journal