Department of ECONOMICS NCR 

Syllabus for

1 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MEA131N  MICROECONOMIC THEORY AND APPLICATIONSI  Core Courses  4  4  100 
MEA132N  MACROECONOMIC THEORY AND POLICYI  Core Courses  4  4  100 
MEA133N  ADVANCED MATHEMATICAL ECONOMICS  Core Courses  4  4  100 
MEA134N  FUNDAMENTAL OF STATISTICS  Core Courses  4  4  100 
MEA135N  PRINCIPLES OF DATA SCIENCE  Core Courses  4  4  100 
MEA136N  RESEARCH METHODOLOGY  Core Courses  2  2  50 
MEA171N  PYTHON PROGRAMMING  Discipline Specific Elective Courses  6  5  150 
2 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MEA231N  MICROECONOMIC THEORY AND APPLICATIONSII  Core Courses  4  4  100 
MEA232N  MACROECONOMIC THEORY AND POLICYII  Core Courses  4  4  100 
MEA233N  ECONOMETRIC METHODS  Core Courses  4  4  100 
MEA234N  STATISTICS USING R  Core Courses  5  6  150 
MEA235N  RESEARCH MODELLING AND IMPLEMENTATION  Discipline Specific Elective Courses  2  2  50 
MEA241AN  MULTIVARIATE ANALYSIS  Discipline Specific Elective Courses  4  4  100 
MEA242AN  APPLIED INSTITUTIONAL ECONOMICS  Discipline Specific Elective Courses  4  4  100 
MEA272N  PREDICTIVE ANALYTICS  Discipline Specific Elective Courses  4  4  100 
 
Department Overview:  
The Department of Economics at the Delhi NCR campus is functioning under the School of Social Sciences in CHRIST (Deemed to be University). Established in the year 2019 with the commencement of the new Campus at Delhi NCR, the department has representation of faculty from all cultures and regions in India and with qualifications from the top institutions in the country and abroad. Together with rich experience in teaching, research and consultancy, they specialises in Monetary and Financial Economics, Environmental Economics, Behavioural Economics, Industrial Economics, Informal Economy and so on, involving in advanced research.  
Mission Statement:  
Establish an identity as a department of high standard in teaching and research in Economics. MISSION 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:  
TheMasterofScienceinEconomicsandAnalyticsisanintensiveprogramthatwill guidestudentsthrougheconomicmodellingandtheorytocomputationalpracticeand cuttingedge tools, providing a thorough training in descriptive, predictive and prescriptive analytics. Students will be equipped with a solid knowledge of econometricandmachinelearningmethods,optimizationandcomputing.Thesebig dataskills,combinedwithknowledgeofeconomicmodelling,willenablethemto identify,assessandseizetheopportunityfordatadrivenvaluecreationintheprivate andpublicsectors.Studentswillbetrainedtocontributesignificantlytoempiricaland appliedworkintheupcomingfieldofEconomics.  
Program Objective:  
Assesment Pattern  
Assessment Strategy ● Internal assessment 70% ○ CIA1 written assignment, group work, presentations ○ CIA2  midterm examination ○ CIA3  written assignment, group work, presentations ● End Semester Examination 30% The assessment strategy involves specific rubric for evaluation of each component.  
Examination And Assesments  
Examination and Assessment The evaluation is divided in to two components: Continuous Internal Assessment (CIA) including Mid Semester Examination (MSE), and the End Semester Examination (ESE). Assessment Pattern The Continuous Internal Assessment (CIA) will be assessed for seventy per cent weightage and the End Semester Examination (ESE) for thirty per cent weightage. The practical courses and the common core courses will be assessed out of hundred marks in various components including attendance. The Mid Semester and End Semester written examination question pattern consists of questions divided into two or three sections with short answers, short essays and long essays. 
MEA131N  MICROECONOMIC THEORY AND APPLICATIONSI (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course Objectives This course aims at analyzing the Economic behavior of the firms and markets. It is mainly concerned with the objective of equipping the students in a comprehensive manner with various aspects of consumer behavior and demand analysis, Production theory and behavior of cost, equilibrium of firm and various forms of market. Course Outcomes Upon successful completion of this course, the students will be able to: • Demonstrate the analytical and critical skills relevant to economics thinking, • Demonstrate the rigorous quantitative training that analytical economics requires, 

Learning Outcome 

CO1: Demonstrate the analytical and critical skills relevant to economics thinking CO2: Demonstrate the rigorous quantitative training that analytical economics requires CO3: Apply the microeconomic theory to microlevel real world economic problems 
Unit1 
Teaching Hours:5 

Methodology


Construction of theories: Deduction and induction; Empirical verification; Theories and tautologies.  
Unit2 
Teaching Hours:15 

Utility and Demand


Consumer preferences; Axioms of preference ordering; Utility function: existence and characteristics; concavity and quasiconcavity; Budget sets; Demand functions: Zero homogeneity; Income and substitution effects; Slutzky theorem: Indirect utility functions; Hicksian compensated demand functions; Expenditure functions; Substitutes and complements: gross and pure; Revealed preference.  
Unit3 
Teaching Hours:20 

Production and Supply


Production functions; Concavity and quasiconcavity; Returns to a factor and to scale; Total, marginal and average cost function; Long run cost curves: envelopes; Factor demand functions, Conditional factor demands; Profit maximization; Supply functions, cost minimization – first and second order conditions; Linear homogeneous production functions and their properties; CobbDouglas, CES, VES and Translog production functions and their properties;Leontief’s production functions, Elasticity of substitution, its derivation for CD and CES functions; the impact of tax/subsidy.  
Unit4 
Teaching Hours:20 

Markets


Characterizing perfect competition; Pricing and output under perfect competitive markets; Monopoly markets: Pricing, discrimination; welfare costs; Monopolistic competition: Characteristics; Long run and short run behavior; Oligopoly: Cournot’s model; Stackleberg framework: Instability; Dominant firm; Compensating variation; Price and output determination under monopsony and bilateral monopoly;  
Text Books And Reference Books: 1.Henderson, J.M. and R.E. Quandt (2003), Microeconomic Theory: A Mathematical Approach, McGraw Hill, New Delhi.  
Essential Reading / Recommended Reading Reference Books 1.Andreu MasColell, M D Whinston and J R Green (1995), Microeconomic Theory, Oxford University Press. 2.Kreps, David M. (1990), A Course in Microeconomic Theory, Princeton University Press, Princeton. 3.Krugman, Paul. and Wells, Robin. (2005), Microeconomics, Worth Publishers. 4.Koutsoyiannis, A. (1979), Modern Microeconomics, (2nd Edition), Macmillan Press, London. 5.Sen, Anindya (2007), Microeconomics: Theory and Applications, Oxford University Press, New Delhi. 6.Varian, H. (2000), Microeconomic Analysis, W.W. Norton, New York. 7.Pindyck, Robert & Rubinfeld, Daniel (2013), Micro Economics, 8th Edition, Pearson Education, USA  
Evaluation Pattern
ESE  30%  
MEA132N  MACROECONOMIC THEORY AND POLICYI (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

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

Learning Outcome 

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. 
Unit1 
Teaching Hours:12 
Introduction and Output Determination


The development of macroeconomics Circular flow of money and product, Actual and potential output GNP identity on the product, income, and disposition sideThe government sector and foreign sectorClassical theory of income and employment Behaviour of Aggregate Demand and Aggregate supply, money and prices in the classical model Keynes’ theory of employment Role of Aggregate Demand Consumption function, investment demand Effective demand Determination of equilibrium income Theory of multiplierDerivation of Investment, expenditure and trade multiplier  
Unit2 
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 curvesShift in IS and LM curvesSimultaneous equilibrium Fiscal and monetary policy effects on demandInteraction of monetary and fiscal policies Aggregate supply in the short run and long runSupply side disturbances and reactionsDemand side disturbances and reactionsDetermination of equilibrium income, employment, rate of interest and price level.  
Unit3 
Teaching Hours:8 
Supply of Money and Demand for Money


Nature, functions, types, and evaluation of money The debate relating to the definition of money Liquidity theory, Gurley and Shaw Hypothesis, Alternative money stock measures, The quantity and components of money stock in India and broad trend in them. Base money, money multipliers, the Quantity theory of money Demand for money Keynesian theory of demand for money BaumolTobin theory Issues regarding endogenous and exogenous supply of money  
Unit4 
Teaching Hours:10 
Money in Walrasian and nonWalrasian Economies and Theories of Disequilibrium


Dynamics Money in neoclassical models Money in nonneoclassical models Walrasian interpretation of Keynesian unemployment (Patinkin, Clower and Leijonhufvud) Post Keynesian interpretation (Sidney Weintraub, Paul Davidson, Kelecki and Minsky)  
Unit5 
Teaching Hours:10 
Theories of the Interest Rate


Real and monetary theories of the interest rate Keynesian theory, Wicksellian theory, Fisher’s theory, Hicksian theory Credit market imperfections Adverse selection and moral hazard  
Unit6 
Teaching Hours:10 
Monetary Institutions & Monetary Policy


Monetary transmission mechanism and targeting Inflation Money growth and interest rates Interest rate rules Taylor rule Rules versus discretion Central Bank autonomy Dynamic inconsistency of monetary policy credibility and reputation Coordination of fiscal and monetary policy, Rationale and impact of reforms since 1991 on BOP.  
Text Books And Reference Books: 1. Burda and Wyplosz (2015). Macroeconomics: A European Text, Fifth Edition, Oxford University Press, New York. 2. N. Gregory Mankiw. (2012). Macroeconomics. 8th Edition, Worth Publishers. 3. Dornbusch, Fischer, Startz. (2010). Macroeconomics. 11th Edition, Tata McGraw Hill.  
Essential Reading / Recommended Reading 1. M. Maria John Kennedy (2011). Macroeconomic Theory, PHI Learning Private Limited, New Delhi. 2. H. L. Ahuja. (2012). Macroeconomics: Theory and Policy. 18th Revised Edition, Sultan Chand Publishers. 3. Brain Snowdown, Howard Vane and Peter Wynarczyk. (1995). A Modern Guide to Macro Economics: An Introduction to Competing School of Thought, Edward Elgar Publishing. 4. Edward Shapiro. (2011). Macroeconomic Analysis. 5th Edition, Galgotia Publication Ltd. 5. Ackley. G. (1978). Macroeconomics: Theory and Policy, Macmillan, New York. 6. Mishkin Frederic (2007), The Economics of Money Banking and Financial Markets, 8th ed Addison Wesley Longman Publishers. 7. Bain, Keith & Howells, Peter (2009), Monetary Economics: Policy and Its Theoretical Basis, Palgrave. 8. Friedman, Ben & Hahn F.H. (Eds.), (1990), Handbook of Monetary Economics, Vols.1, 2, & 3, North Holland Publishers. 9. Langdana Farrokh (2009), Macroeconomic Policy: Demystifying Monetary and Fiscal Policy, 2nd Edition, Springer. 10. William. H. Branson (2005). Macroeconomic Theory and Policy, Third Edition, All India Traveller BookSeller Publishers, New Delhi. 11. D.N. Dwivedi. (2005). Macroeconomics: Theory and Policy. 2nd Edition, TataMcGraw Hill Education.  
Evaluation Pattern CIA I: 20 % CIA II: 25 % (Mid Semester Examination) CIA III: 20 % Attendance: 05 % ESE: 30%  
MEA133N  ADVANCED MATHEMATICAL ECONOMICS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course proposes to familiarize students with Introduction to Mathematical Economics, is designed with the intention to understand Areas under curveDefinite and indefinite Integration, Application Consumer Surplus and Producer Surplus, the focus is on Concavity, Convexity, Quasi concavity, Quasi convexity Optimization of functions. This course intend to discuss about the Constrained Optimization Problems two variables, one constraintLagrangemultiplier methodFirst order conditionsSecond order conditions, aims to discuss the Difference and Differential Equations and Economic Applications. Course Objectives The main aim of the course is to provide knowledge about mathematical techniques in advanced forms and their applications. The main objectives of the paper are to train the students to grasp the use of mathematical techniques and operations to analyze economic problems and to initiate students into various economic concepts which are amenable to mathematical treatment


Learning Outcome 

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. 
Unit1 
Teaching Hours:12 
Introduction to Mathematical Economics Equilibrium (Or Static) Analysis


Equilibrium analysis in EconomicsDefinition of equilibriumSolution of equilibrium Single vs. multiple equilibriumPartial vs. general equilibrium. Application: single vs. multiple commodity markets Linear Models and Matrix Algebra Matrix algebra DeterminantsInverseEigenvalues and Eigen vectors Cramer’s ruleQuadratic Forms Applications: Multiple commodity markets HeckscherOhlin model, ISLM Model MundellFleming Model
 
Unit2 
Teaching Hours:6 
Integration


Areas under curveDefinite and indefinite Integration, Application Consumer Surplus and Producer Surplus  
Unit3 
Teaching Hours:15 
Unconstrained Optimization


Concavity, Convexity, Quasi concavity, Quasi convexity Optimization of functions of one variable Main concepts First order conditionsSecond 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  
Unit4 
Teaching Hours:15 
Constrained Optimization Problems


Two variables, one constraintLagrangemultiplier methodFirst order conditionsSecond 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  
Unit5 
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:
 
Essential Reading / Recommended Reading N/A  
Evaluation Pattern CIA I : 20 % CIA II : 25 % (Mid Semester Examination) CIA III : 20 % Attendance: 05 % ESE : 30%  
MEA134N  FUNDAMENTAL OF STATISTICS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 



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. 
Unit1 
Teaching Hours:10 

Organization and Presentation of data


Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data. Presentation of data by tables: construction of frequency distributions for discrete and continuous data, graphical representation of a frequency distribution by histogram and frequency polygon, cumulative frequency distributions (inclusive and exclusive methods).  
Unit2 
Teaching Hours:15 

Descriptive Statistics


Measures of location or central tendency: Arithmetic Mean, Median, Mode, Geometric mean, Harmonic mean. Partition values: Quartiles, Deciles and percentiles. Measures of dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of variation. Moments: measures of skewness, Kurtosis.  
Unit3 
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.  
Unit4 
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.  
Unit5 
Teaching Hours:15 

Probability Distribution


Binomial Distribution and their properties with practical examples, Poisson Distribution and their properties with practical examples, Normal Distribution and their properties with practical examples,  
Text Books And Reference Books: Text Books
 
Essential Reading / Recommended Reading
 
Evaluation Pattern Evaluation Pattern
 
MEA135N  PRINCIPLES OF DATA SCIENCE (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

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


Learning Outcome 

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. 
Unit1 
Teaching Hours:12 
Introduction to Data Science


Preparing and gathering data and knowledge  Philosophies of data science  data all around us: the virtual wilderness  Data wrangling: from capture to domestication  Data science in a big data world  Benefits and uses of data science and big data  facts of data  data science processes  
Unit2 
Teaching Hours:12 
Data Science Process


Overview of the data science process  retrieving data  Cleansing, integrating, and transforming data  Exploratory data analysis  Build the model  Presenting finding and building applications on top of them  
Unit3 
Teaching Hours:12 
Machine Learning


Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms  
Unit4 
Teaching Hours:12 
First Steps in Big Data


First steps in big data  Distributing data storage and processing with frameworks  Case study: Assessing risk when loaning money  Join the NoSQL movement  Introduction to NoSQL  Case Study  
Unit5 
Teaching Hours:12 
Databases


The rise of graph databases  Introducing connected data and graph databases  Text mining and text analytics  text mining in real world  text mining techniques Data Visualization Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools.  
Text Books And Reference Books: 1.Think Like a Data Scientist, Brian Godsey, Manning Publications, 2017. 2.Introducing Data Science, Davy Cielen, Arno D. B. Meysman and Mohamed Ali, Manning Publications, 2016. 3.Introducing Data Science, Davy Cielen, Arno D. B. Meysman, Mohamed Ali, Manning Publications Co., 1st edition, 2016  
Essential Reading / Recommended Reading 1.Data Science from Scratch: First Principles with Python, Joel Grus, O’Reilly, 1st edition, 2015. 2.Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt, O’ Reilly, 1st edition, 2013. 3.Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press, 2nd edition, 2014 4.An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer, 1st edition, 2013
 
Evaluation Pattern CIAs Only  
MEA136N  RESEARCH METHODOLOGY (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

Understanding of the importance of research in creating and extending the knowledgebase of their subject area; Ability to distinguish between the strengths and limitations of different research approaches regarding their subject/research area; Knowledge of the range of qualitative and quantitative research methods potentially available to them; The ability to differentiate between the role of practitioners and the role of researchers; An understanding of and begin to critically reflect upon issues of ethics and role of the researcher; The skills to work independently, to plan and to carry out a smallscale 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 smallscale research project independently. CO4: Demonstrate the understanding of and ability to critically reflect upon issues of ethics and research. 
Unit1 
Teaching Hours:8 
Introduction


The nature of knowledge and theory  Philosophy of Social Science Research  Relevance of Social Science Research  Objectivity and Values in Social Sciences; logic of Scientific Investigation: Theory Construction in Social Science Research  Approaches to Social Science Research, Theoretical, Applied and Action Research  Ethical Issues in Research on Human or Social Subjects  Nonsexist approach in Social Sciences  
Unit2 
Teaching Hours:6 
Research Design


Review of Literature  Identification of Research Gaps and Research Needs  Identification, selection and formulation of research problem  Formulating Hypotheses/Propositions/Issues, conceptualizing research problem  
Unit3 
Teaching Hours:8 
Overview of Social Science Methodology


Unidisciplinary, interdisciplinary, multidisciplinary methodologies  Quantitative Research Methods: An Overview  Qualitative Research Methods: An Overview  Historical Method  Case Study Method  Action Research  Monitoring and Evaluation  Triangulation (including/mixing Qualitative and Quantitative) Methods  
Unit4 
Teaching Hours:8 
Data analysis and Research Communication


Choice of Statistical and Processing Techniques  Interpretative Narrative Methods  Theory of the Testing of Hypotheses  Presentation of Research Findings, Products of Research, Thesis Writing  Factors conducive to research utilization firm; Compensating variation; Price and output determination under monopsony and bilateral monopoly;  
Text Books And Reference Books: 1.Blair J, Czaja R, Blair E (2014). Designing Surveys: A Guide to Decisions and Procedures. SAGE Publications. 3rd 2.Kumar R (2010). Research methodology: a step by step guide for beginners. SAGE Publications Ltd; Third Edition. 3.Hay M Cameron (2015) Methods That Matter: Integrating Mixed Methods for More Effective Social Science Research  
Essential Reading / Recommended Reading 1.Bryman, Alan (2015). Social Research Methods. Oxford: Oxford University Press 2.Schutt Russell K. (2016), Investigating the social world: the process and practice of research  
Evaluation Pattern CIAs Only  
MEA171N  PYTHON PROGRAMMING (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Demonstrate the use of builtin 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. 
Unit1 
Teaching Hours:12 
Introduction to Python


Python Basics, Entering Expressions into the Interactive Shell, The Integer, FloatingPoint, and String Data Types, String Concatenation and Replication, Storing Values in Variables, Your First Program, Dissecting Your Program, Flow control, Boolean Values, Comparison Operators, Boolean Operators, Mixing Boolean and Comparison Operators, Elements of Flow Control, Program Execution, Flow Control Statements, Importing Modules, Ending a Program Early with sys.exit(), Functions, def Statements with Parameters, Return Values and return Statements, The None Value, Keyword Arguments and print(), Local and Global Scope, The global Statement, Exception Handling, A Short Program: Guess the Number
 
Unit2 
Teaching Hours:12 
Lists, The List Data Type, Working with Lists, Augmented Assignment Operators, Methods, Example Program: Magic 8 Ball with a List, Listlike Types: Strings and Tuples, References,
Dictionaries and Structuring Data, The Dictionary Data Type, Pretty Printing, Using Data Structures to Model RealWorld Things
Manipulating Strings, Working with Strings, Useful String Methods, Project: Password Locker, Project: Adding Bullets to Wiki Markup
 
Unit3 
Teaching Hours:12 
Pattern Matching with Regular Expressions, Finding Patterns of Text Without Regular Expressions, Finding Patterns of Text with Regular Expressions, More Pattern Matching with Regular Expressions, Greedy and Nongreedy Matching, The findall() Method, Character Classes, Making Your Own Character Classes, The Caret and Dollar Sign Characters, The Wildcard Character, Review of Regex Symbols, CaseInsensitive Matching, Substituting Strings with the sub() Method, Managing Complex Regexes, Combining re .IGNORECASE, re .DOTALL, and re .VERBOSE, Project: Phone Number and Email Address Extractor Reading and Writing Files, Files and File Paths, The os.path Module, The File Reading/Writing Process, Saving Variables with the shelve Module, Saving Variables with the pprint. pformat() Function, Project: Generating Random Quiz Files, Project: Multiclipboard, Organizing Files, The shutil Module, Walking a Directory Tree, Compressing Files with the zipfile Module, Project: Renaming Files with AmericanStyle Dates to EuropeanStyle Dates, Project: Backing Up a Folder into a ZIP File,
Debugging, Raising Exceptions, Getting the Traceback as a String, Assertions, Logging, IDLE’s Debugger.
 
Unit4 
Teaching Hours:12 
NumPy Libraries for Arrays, Pandas Library for Data Processing  
Unit5 
Teaching Hours:12 
Matplotlib for Visualization, Seaborn Library for Visualization, SciPy Library for Statistics  
Unit6 
Teaching Hours:30 
Lab


1. Installing python to your computer 2. Demonstrate the usage Conditional Statements 3. Demonstrate the use of Iterative statements 4. Demonstrate the usage of Functions. 5. Demonstrate the usage of different Data Types 6. Demonstrate the usage of String Functions 7. Demonstrate the Exception handling 8. Demonstrate creation and use of a class 9. Demonstrate the working of Inheritance concepts 10. Demonstrate seeking and finding of file. 11. Demonstrate opening a file and writing in to it 12. Demonstrate the usage of list data structure. 13. Demonstrate the usage of Tuples data structure. 14. Demonstrate the usage of a Dictionary data structure. 15. Demonstrate Sending an email using python. 16. Accessing Array index using NumPy 17. Aggregation function using NumPy 18. Implement a) Matplotlib b) Seaborn  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading 1. John V Guttag, ―Introduction to Computation and Programming Using Python ‘‘, Revisedand expanded Edition, MIT Press, 2013 2.Robert Sedgewick, Kevin Wayne, Robert Dondero, ―Introduction to Programming in 3.Python: An Interdisciplinary Approach, Pearson India Education Services Pvt. Ltd., 2016. 4.Timothy A. Budd, ―Exploring Python‖, McGraw Hill Education (India) Private Ltd 2015. 5.Kenneth A. Lambert, ―Fundamentals of Python: First Programs‖, CENGAGE Learning, 2012. 6.Charles Dierbach, ―Introduction to Computer Science using Python: A Computational ProblemSolving Focus, Wiley India Edition, 2013.  
Evaluation Pattern CIAs Only  
MEA231N  MICROECONOMIC THEORY AND APPLICATIONSII (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Define and explain the basic concepts and hypothesis in Microeconomic Theory and their relations. CO2: Define market, categorize markets and analyse perfectly competitive markets. CO3: Demonstrate basic knowledge and skill in the use of cost and managerial concepts and techniques as management tools for planning, controlling, evaluating performance and making decisions. 
Unit1 
Teaching Hours:10 
Game Theory


Extensive and normal form representation of games– Nash equilibrium (impure and mixed strategies); definition and existence – subgame perfection dynamic games; Applications: strategic behaviour of firms in a market–Bertrand, Cournot and Stackelberg models – and entry deterrence.  
Unit2 
Teaching Hours:10 
Distribution


Neoclassical approach: Marginal productivity theory  in perfect and imperfect product and factor markets; Product exhaustion theorem; Elasticity of technical substitution, technical progress and factor shares; Macro theories of distribution – Ricardian, Marxian, Kalecki and Kaldor’s.  
Unit3 
Teaching Hours:10 
General Equilibrium


Partial and general equilibrium; Walrasian excess demand and inputoutput approaches to general Equilibrium; Existence, stability and uniqueness of partial equilibrium and general equilibrium; Relationship between relative commodity and factor prices (StoplerSamuelson theorem); Relationship between outputmix and real factor priceseffect of changes in factors supply in closed economy(Rybczynsky theorem).
 
Unit4 
Teaching Hours:10 
Welfare Economics


Pigovian welfare economics; Pareto optimal conditions; Value judgement; Social welfare function; Compensation principle; Inability to obtain optimum welfare–Imperfections, market failure, decreasing costs; Uncertainty and non–existent and incomplete markets; Theory of secondbest –Arrow’s impossibility theorem, Rawl’s theory of Justice; Equity efficiency tradeoff.
 
Unit5 
Teaching Hours:20 
New Institutional Economics


Definition of Transaction Cost and types of transaction costs; General Principles in Modelling Transaction Costs; Modelling Transaction Costs by modelling transaction activity. Emergence of Property Rights: The invisible hand and the optimistic theory; contracting for Property Rights: the role of political bargaining and the Liebcap Thesis. Principles of contractual obligations; economic theories of contract: agency theory, selfenforcing agreement theory and relational contract theory; types of private ordering and their dynamics.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA I: 20 % CIA II: 25 % (Mid Semester Examination) CIA III: 20 % Attendance: 05 % ESE: 30%  
MEA232N  MACROECONOMIC THEORY AND POLICYII (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

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: Analyse the existing idea of different schools of thought/ theories. To have some idea on why those theories have not been able to influence/ different economic conditions. CO5: Understand the factors influencing the Balance of Payment and analyse the cause of disequilibrium in the Balance of payment. CO6: Evaluate the consequences of basic macroeconomic policy options under differing economic conditions within a business cycle. 
Unit1 
Teaching Hours:15 
Inflation, Unemployment and Productivity


Classical dichotomy and monetary neutrality Classical, Neoclassical and modern theories of inflation Keynesian and monetarist views on inflation Inflation in the static model Wages, prices and productivityThe relation of wages to unemployment Shortrun and longrun Phillips curve and the policy implications Modifications in Phillips curve Natural rate of unemploymentSeigniorage and hyperinflationdisinflation.  
Unit2 
Teaching Hours:15 
Theories of Money Demand and Interest rates


The Classical and Neoclassical views on holding money Real and monetary theories of the rate of interest: liquidity preference and loanable funds theories of interest The term structure of interest rates: Pure Expectations, Pure segmentation and Substitutability theories Portfolio theories of demand for money BaumolTobin approach to transaction demand for money Tobin’s portfolio optimization approach Friedman’s quantity theory of money.  
Unit3 
Teaching Hours:15 
Business Cycles, post Keynesian Macroeconomics


Measurement, Endogenous theories (Hicks, Goodwin, Kaldor), Exogenous theories  Real Business Cycle Theories  Real Business Cycle School and intertemporal substitution of labour Real Business Cycle theory technology shocks neutrality of money and flexibility of wages and prices Real Business cycle view on the great depression The modern monetarism, major postulatesKeynesian policy framework The New Classical macroeconomics Stagflation trendThe SupplySide economics major implications.  
Unit4 
Teaching Hours:15 
External Sector and Emerging Issues


Rationale and impact of reforms since 1991 on BOP, Problems of India’s international debt export policies, working and regulations of MNCs in India The issues and policies towards financial stability International reserves, Monetary integration European Monetary SystemContemporary macroeconomic debates in India and the world.  
Text Books And Reference Books: 1. William. H. Branson (2005). Macroeconomic Theory and Policy, Third Edition, All India Traveller Book Seller Publishers, New Delhi. 2. D.N. Dwivedi. (2005). Macroeconomics: Theory and Policy. 2^{nd }Edition, Tata McGraw Hill Education. 3. Levacic and Rebman. (1982). Macro Economics: An Introduction to Keynesian and Neoclassical Controversies. 2^{nd }Edition, Macmillan Publishers.  
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. 8^{th }Edition, Worth Publishers. 4. Dornbusch, Fischer, Startz. (2010). Macroeconomics. 11^{th }Edition, Tata McGraw Hill. 5. M. Maria John Kennedy (2011). Macroeconomic Theory, PHI Learning Private Limited, New Delhi. 6. H. L. Ahuja. (2012). Macroeconomics: Theory and Policy. 18^{th }Revised Edition, Sultan Chand Publishers. 7. Brain Snowdown, Howard Vane and Peter Wynarczyk. (1995). A Modern Guide to Macroeconomics: An Introduction to Competing School of Thought, Edward Elgar Publishing. 8. Edward Shapiro. (2011). Macroeconomic Analysis. 5th Edition, Galgotia Publication Ltd. 9. Ackley. G. (1978). Macroeconomics: Theory and Policy, Macmillan, New York.  
Evaluation Pattern CIA I : 20 % CIA II : 25 % (Mid Semester Examination) CIA III: 20 % Attendance: 05 % ESE: 30%  
MEA233N  ECONOMETRIC METHODS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Formulate, estimate, test and interpret suitable models for the empirical study of economic events; CO2: Acquire the ability to evaluate the performance of alternative econometric models through the appropriate use of tests. CO3: Attain knowledge reading and interpreting applied economic articles. CO4: Demonstrate the analytical and critical skills relevant to economics thinking. CO5: Demonstrate the rigorous quantitative training that analytical economics requires. CO6: Demonstrate good understanding of theoretical and applied econometrics and . be able to perform econometric analysis and estimation, by understanding their application in Economics. 
Unit1 
Teaching Hours:12 
Regression Analysis


Linear regression model, two variables and multi variables, BLUE property, general and confidence approach to hypothesis testing, partial effects and elasticity, goodness of fit, model evaluation, matrix approach to linear regression models  
Unit2 
Teaching Hours:12 
Extension of Linear Regression Models


Consequences and detection of multicollinearity, heteroskedasticity, and autocorrelation; and remedial measures  
Unit3 
Teaching Hours:12 
Dummy Variables


Regression on qualitative and quantitative variables, dummy variable trap, structural stability of regression models, Chow test, piecewise linear regression model  
Unit4 
Teaching Hours:12 
Simultaneous Equation Models


Simultaneity bias, structural versus reduced form, identification: rank versus order condition, exact and over identifications, triangular model, methods of estimation including indirect least squares, twostage least squares and threestage least squares, LIML and FIML  
Unit5 
Teaching Hours:12 
Distributed Lag Models


Formation of expectations, naïve expectation versus adaptive expectations models, partial adjustment models, distributed lag models; Koyck’s model, Almon lag, polynomial distributed lag models, end point restriction, rational expectations models  
Text Books And Reference Books: 1. Wooldridge, J., Introductory Econometrics: A Modern Approach, SouthWestern 2. Gujarati, N.D., Basic Econometrics, fourth edition, McGraw Hill, 2003  
Essential Reading / Recommended Reading 1. Johnston, J., Econometric Methods, third edition, McGraw Hill 2. Ramanathan, R., Introductory Econometrics with applications, fifth edition, Thomson Asia Private Limited, 2002 3. Brooks, C., Introductory Econometrics for Finance, first edition, Cambridge University Press, 2003  
Evaluation Pattern CIA I : 20 % CIA II : 25 % (Mid Semester Examination) CIA III: 20 % Attendance: 05 % ESE: 30%  
MEA234N  STATISTICS USING R (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:5 
Max Marks:150 
Credits:6 
Course Objectives/Course Description 

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

Learning Outcome 

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. 
Unit1 
Teaching Hours:12 
Introduction and preliminaries


The R environment, R and statistics, R commands, Data permanency and removing objects, Simple manipulations, Numbers and Vectors, Objects modes and attributes, Ordered and unordered Factors, Arrays and Matrices  
Unit2 
Teaching Hours:12 
Lists and Data Frames


Lists and Data Frames Constructing and modifying lists, Making Data frames, attach( ) and detach( ), Working with data frame, Reading data from files using read.table( ), scan( ), Grouping, Conditional execution: if statements, Repetitive execution: for loops, repeat and while loops, Functions.  
Unit3 
Teaching Hours:12 
Data Exploration for Univariate and Bivariate


Data Exploration for Univariate and Bivariate DataUnivariate Data  Handling categorical data and numerical data using R, Bivariate Data Handling bivariate categorical data using R, Categorical vs. Numerical, Numerical vs. Numerical  
Unit4 
Teaching Hours:12 
Data Exploration for Multivariate Data


Data Exploration for Multivariate DataMultivariate 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( )
 
Unit5 
Teaching Hours:12 
Correlation and Data Visualization


Pearson correlation, Spearman rank correlation lattice package in R  1D, 2D, 3D plots using lattice, ggplot2 package in R 1D, 2D, 3D plots using ggplot2
 
Unit6 
Teaching Hours:30 
Lab Programs


1. Demonstrate the usage of Numbers and Vectors in R 2. Simple manipulations on Numbers and Vectors, Objects modes and attributes, Ordered and unordered Factors 3. Implement the concepts of Arrays and Matrices 4. Demonstrate the usage of Data Frames and Lists and its attributes attach, detach, scan and importing a file 5. Implement the concept of grouping and conditional execution on Data Frames and Lists 6. Demonstrate repetitive executions on Data Frames 7. Use a Dataset to handle the Categorical and numerical data 8. Use a Dataset to handle the Bivariate categorical data 9. Use a Dataset to handle the Multivariate categorical data 10. Demonstrate the usage of apply () functions. 11. Implement the usage of dplyr package 12. Utilize a lattice package to plot 1D, 2D and 3D plots for a given dataset. 13. Utilize ggplot2 package to plot 1D, 2D and 3D plots for a given dataset. 14. Demonstrate Pearson correlation and Spearman rank correlation.
 
Text Books And Reference Books: 1.Allen B. Downey, “Think Python: How to Think Like a Computer Scientist‘‘, 2nd edition, Updated for Python 3, Shroff/O‘Reilly Publishers, 2016  
Essential Reading / Recommended Reading 1. John V Guttag, ―Introduction to Computation and Programming Using Python ‘‘, Revisedand expanded Edition, MIT Press, 2013 2. Robert Sedgewick, Kevin Wayne, Robert Dondero, ―Introduction to Programming in 3. Python: An Interdisciplinary Approach, Pearson India Education Services Pvt. Ltd., 2016. 4. Timothy A. Budd, ―Exploring Python‖, McGraw Hill Education (India) Private Ltd 2015. 5. Kenneth A. Lambert, ―Fundamentals of Python: First Programs‖, CENGAGE Learning, 2012. 6. Charles Dierbach, ―Introduction to Computer Science using Python: A Computational ProblemSolving Focus, Wiley India Edition, 2013.  
Evaluation Pattern CIAs only
 
MEA235N  RESEARCH MODELLING AND IMPLEMENTATION (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The course is designed to train and equip the students to carryout research. The course enables the students to • Gain knowledge of core research techniques which forms a basis for understanding and critical analysis of the published work in economics. • Develop the analytical skills required to conduct research in economics discipline. 

Learning Outcome 

CO1: Develop a strong theoretical background which would help the students to better understand applicability of various methods and tools in different economic contexts or scenarios. 
Unit1 
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 preprocessing in which students should carry out the following tasks and submit the document for the same before the MSE.
• Gather the datasets from various sources (like visiting websites, universities, person,creating individually, etc.) • Steps in preprocessing Part B constitutes modelling and implementation of their research work. Students should perform the following tasks: • Methodology • Evaluation and Discussion of Results • Limitations, Conclusion and Scope for future Enhancements • Plagiarism Report Week 1  Discussion and Identification of Research Domain (Updations) Week 2  Identification of Research Gap / OBJECTIVES OF RESEARCH Week 3  Research Design Phase  I Week 4  Research Design Phase  II Week 5  Research Design Phase  III Week 6  Methods of Data Collection  Processing  Analysis of Data Week 7  Methods of Data Collection  Processing  Analysis of Data Week 8  Methods of Data Collection  Processing  Analysis of Data Week 9  Methods of Data Collection – Processing Analysis of Data Week 10  Methods of Data Collection  Processing  Analysis of Data Week 11  Implementation Phase  I Week 12  Implementation Phase  I (a) Week 13  Implementation Phase  I (b) Week 14  Implementation Phase  I (c) Week 15  Implementation Phase  I (d)  
Text Books And Reference Books: Text Books 1. Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage learning. 2. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press. 3. Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2009). Handbook on impact evaluation:quantitative methods and practices. World Bank Publications. 4. Gujarati, D. N. (2021). Essentials of econometrics. SAGE Publications. 5. Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics. Princeton university press.  
Essential Reading / Recommended Reading Research articles and publication from peer reviewed journals and established government reports.  
Evaluation Pattern
 
MEA241AN  MULTIVARIATE ANALYSIS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

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

Learning Outcome 

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. 
Unit1 
Teaching Hours:12 

Bivariate Normal Distribution (BVN)


p.d.f. of BVN, properties of BVN, marginal and conditional p.d.f. of BVN. Multivariate Data: Random Vector: Probability mass/density functions, Distribution function, Mean vector & Dispersion matrix, Marginal & Conditional distributions.  
Unit2 
Teaching Hours:12 

Multivariate Normal distribution


Multivariate Normal distribution and its properties. Sampling distribution for mean vector and variance covariance matrix. Multiple and partial correlation coefficient and their properties.  
Unit3 
Teaching Hours:12 

Applications of Multivariate Analysis


Discriminant Analysis, Principal Components Analysis and Factor Analysis.  
Unit4 
Teaching Hours:12 

Nonparametric Tests


Introduction and Concept, Test for randomness based on total number of runs, Empirical distribution function.  
Unit5 
Teaching Hours:12 

Non Parametric Techniques


Kolmogrov Smirnov test for one sample, Sign tests one sample and two samples, WilcoxonMannWhitney test, KruskalWallis test.  
Text Books And Reference Books: 1. Anderson, T.W. (2003): An Introduction to Multivariate Statistical Analysis, 3rdEdn., John Wiley 2. Muirhead, R.J. (1982): Aspects of Multivariate Statistical Theory, John Wiley. 3. Kshirsagar, A.M. (1972): Multivariate Analysis, 1stEdn. Marcel Dekker.  
Essential Reading / Recommended Reading 1.Johnson, R.A. and Wichern, D.W. (2007): Applied Multivariate Analysis, 6th Edn., Pearson & Prentice Hall 2. Mukhopadhyay, P. :Mathematical Statistics. Books and Allied, January 2016 3. Gibbons, J. D. and Chakraborty, S (2003): Nonparametric Statistical Inference. 4th Edition. Marcel Dekker, CRC.  
Evaluation Pattern
 
MEA242AN  APPLIED INSTITUTIONAL ECONOMICS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 



Learning Outcome 

CO1: Identify the types and nature of institutions and their impact on economic development. Identify the reason for institutional failures and its impact on the economic prosperity of nations. CO2: Analyse of the institutional structure of society. CO3: Apply the concept of transaction costs in the explanation of institutions, business practices and contract types. CO4: Analyse the behaviour of the firm based on its property rights structure. CO5: Conduct economic analysis of the behaviour of the state. CO6: Analyse institutional changes. 
Unit1 
Teaching Hours:12 
Introduction to Institutional Economics


Institutional Economics as a departure from NeoClassical and Marxian Economics – Historic development of Institutional Economics  Core issues in New Institutional Economics. Rational choice model. Full and perfect information. Bounded rationality. Incomplete and imperfect information. Ultimate game. Beauty contexts game. Assumptions of New Institutional Economics. Incomplete specification of rules.  
Unit2 
Teaching Hours:12 
Transaction Costs and Bounded Rationality


Defining Transaction: Williamson, Ronald Coase and Hobbs meaning of Transaction  Types of Transaction Cost: Information cost, Bargaining cost, Monitoring cost Market, Managerial and Political transaction costs  Identification and measurements of transaction costs: some general principals  Modeling Transaction Costs by modeling the transaction activity.  
Unit3 
Teaching Hours:12 
Contract Theories


Incomplete contracts. GrossmanHart model. Decision rights. Principalagent framework. Asymmetric information. Adverse selection. Signaling. Screening. Moral hazard. Hidden action and information. Delegation. Agency costs. Incentive contracts. Opportunistic behavior.  
Unit4 
Teaching Hours:12 
Institutions of Property Rights


Definition of property rights. The Coase theorem and externalities. Categories of property rights. Property rights regimes. Collective property. Common property. Residual rights. Land rights. The naive theory of property rights emergence.  
Unit5 
Teaching Hours:12 
Applications of NIE


The New Institutional Economics of the Market: Market as Organisation, Price Rigidity, Market Organisation as Market Cooperation and Neoinstitutionalist view of Market Organisation – The New Institutional Economics of the Firm: The Orthodox NeoClassical Firm, The Incentives and the Limits to Integration, Ownership and Control, Institutional Models within the NeoClassical Framework, Co – Determination and Comparison of the Formal Models of the Firm.  
Text Books And Reference Books: 1. North, Douglas C. (2004). Institutional Change and Economic Performance. Cambridge University Press. 2. Eggertson, Thrainn. (1999). Economic Behaviour and Institutions. Cambridge University Press.
 
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA 120% CIA 225% CIA 3 20% End Sem30% Attendance5%  
MEA272N  PREDICTIVE ANALYTICS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

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

Learning Outcome 

CO1: Categorize which predictive models are suitable for a particular problem. CO2: Test performance and accuracy problems of predictive models as and when they arise and be able to fix them. CO3: Apply Time series, regression, classification and clustering models. 
Unit1 
Teaching Hours:12 

Introduction to Predictive Modelling and Process of Predictive Modelling


I.AIntroduction to Predictive Modelling Understanding Data Core Components of a Model – Types of Models Supervised Unsupervised Semi supervised and Reinforcement Learning Models – Parametric and Non Parametric Models Regression and Classification Models I.BProcess of Predictive Modelling Defining the Model’s Objective Collecting Data Picking a Model Preprocessing Data Exploratory Data Analysis Data TransformationsMissing Data Outliers Training and Assessing the Model Exploring Alternate models – Model Deployment
 
Unit2 
Teaching Hours:16 

Predictive Models Regression


II.A Linear Regression Introduction to Linear Regression Assumptions Estimating a Simple Linear Regression Multiple Linear Regression (MLR) Assessing Linear Models Residual Analysis Significance Tests Performance Metrics for Linear Regression Problems with MLR Multicollinearity Outliers II. B Logistic Regression Classifying with Linear ModelsIntroduction to Logistic RegressionGeneralized Linear Models AssumptionsInterpretation of Coefficients Maximum Likelihood Estimation Assessing Logistic Regression Models Model Deviance – Test Performance Introduction to Multinomial Logistic Regression  
Unit3 
Teaching Hours:12 

Time Series Analysis


Fundamental Concepts of Time Series White Noise Fitting White Noise Time Series Random Walk Fitting Random Walk Stationarity Stationary Time Series Models Moving Average Models Autoregressive Models Autoregressive Moving Average Models Non Stationary Time Series Models Autoregressive Integrated Moving Average Models Autoregressive Conditional Heteroscedasticity Models Generalized Autoregressive heteroscedasticity Models.
 
Unit4 
Teaching Hours:16 

Classification and Clustering Methods


I. Classification Introduction to classification techniquesNearest Neighbor Method K Nearest Neighbor algorithmDiscriminant AnalysisFisher’s Linear Discriminant Function Decision Trees II. Clustering Introduction to Clustering kMeans ClusteringExpectation Maximization Algorithm Hierarchical Clustering Procedures  
Unit5 
Teaching Hours:4 

Association Techniques


Introduction to Association techniquesQuick Review of Probability Bayesian Probability MethodsMarket Basket Analysis Association Rules and Lift
 
Text Books And Reference Books: Forte, R. M. (2015). Mastering Predictive Analytics with R. Packt Publishing Ltd.  
Essential Reading / Recommended Reading 1. Wu, J., & Coggeshall, S. (2012). Foundations of Predictive Analytics. Boca Raton: CRC Press, Taylor & Francis Group LLC. 2. Albright C. S., Winston Wayne L. and Zappe C. J (2009). Decision Making Using Microsoft Excel (India Edition). Cengage Learning. 3. Evans J. R (2013). Business Analytics Methods, Models and Decisions. Pearson, Upper Saddle River, New Jersey.  
Evaluation Pattern
