Econometrics

Paper Code: 
24DSTT601(B)
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

This course is meant for training the students in econometric methods and their applications. Also familiarize the students with the concept of statistical inference. This course would enable the students to understand economic phenomena through statistical tools and economics principles.

Course

Course Outcomes

Learning and teaching strategies

Assessment Strategies

Course Code

Course Title

24DSTT 601 (B)

Econometrics

(Theory)

CO 78: Demonstrate a comprehensive understanding of demand theory and its practical application in various real-world scenarios

CO 79: Use the least squares method in evaluating the relationship of one and more explanatory variables to the dependent variable.

CO 80: Construct, test, analyze and interpret econometric models.

CO 81: Mitigate and resolve challenges commonly encountered in econometrics, through different techniques and methodologies.

CO 82: Identify and evaluate the significance of autocorrelation in theory of econometrics and explain the effects of violations of the classical assumptions and construct a perfect model.

CO 83: Contribute effectively in course-specific interaction.

Approach in teaching:

Interactive Lectures,

Group Discussion,

Classroom Assignment,

Problem Solving Sessions.

 

Learning activities for the students:

Assignments,

Seminar,

Presentation,

Subject based  Activities.

Classroom Quiz,

Assignments,

Class Test,

Individual Presentation.

 

 

12.00
Unit I: 
Demand and Supply

Demand and supply, law of demand and supply. Elasticity of demand: Price, Income and Cross elasticity. Engel’s curve and Engel’s law, Pareto’s law of income.

 

12.00
Unit II: 
Basics of Econometrics

Econometrics: goals, types, methodology, limitations, properties, relationship among economic variables, the general linear model and its extensions, assumptions.

 

12.00
Unit III: 
Methods of Estimation

Ordinary least squares estimation and prediction. Gauss-Markov theorem. Generalized least square estimation and prediction. Properties of least square estimators. Goodness of fit - R2 and testing of hypothesis on parameters. 

 

12.00
Unit IV: 
Multicollinearity and Heteroscedasticity

Multicollinearity- Concept, Consequences, Detection and Remedies. Heteroscedasticity– Concept and Consequences.

 

12.00
Unit V: 
Auto- Correlation

Auto-correlation: its consequences, Detection and Remedies and tests (Durbin Watson test), Identification problem, Conditions of Identification.

 

Essential Readings: 
  • Croxton, F. E . & Cowden, DJ. (1979): Applied General Statistics, Prentice Hallof India.
  • Johnston, J. (1984): Econometric Methods. McGraw Hill.
  • Judge, G.C., Hill, R.C., Griffiths, W.E., Lutkepohl, H. & Lee, T.C. (1988): Introduction to the Theory and Practice of Econometrics, 2nd Ed.John Wiley.
  • Kmenta, J. (1986): Elements of Econometrics, 2nd Ed. University of Michigan Press.
  • Koop, G. (2007): Introduction to Econometrics, John Wiley.

 

SUGGESTED READINGS:

  • Maddala, G.S. (2017): Introduction to Econometrics, 3rd Ed. John Wiley.
  • Pindyck, R.S. & Rubinfeld, D.L. (1998): Econometric Models and Economic Forecasts, 4th Ed. McGraw Hill.
  • Verbeek, M. (2008): A Guide to Modern Econometrics, 3rd Ed. John Wiley.
  • Judge, G.C., Hill, R, C. Griffiths, W.E., Lutkepohl, H. and Lee, T-C. (1988): Introduction to the Theory and Practice of Econometrics, Second Edition, John Wiley & Sons.
  • Kendall, M.G. and Stuart, A. (1968): The Advanced Theory of Statistics (Vol. III), Second Edition, Charles Griffin.
  • Gujarati, D. and Sangeetha, S. (2007): Basic Econometrics, 4 th Edition, McGraw Hill Companies.
  • Koutsoyiannis, A. (2004): Theory of Econometrics, 2nd Edition, Palgrave Macmillan Limited.

 

  e-RESOURCES: 

 

 JOURNALS: 

  • Sankhya The Indian Journal of Statistics, Indian Statistical Institute
  • Aligarh Journal of Statistics, Department of Statistics and Operations Research, Aligarh Muslim University
  • Afrika Statistika, Saint-Louis Senega University
  • International Journal of Statistics and Reliability Engineering, Indian Association for Reliability and Statistic
  • Journal of the Indian Society for Probability and Statistics, Indian Society for Probability and Statistics
  • Journal of the Indian Statistical Association, Indian Statistical Association
  • Statistica, Department of Statistical Sciences Paolo Fortunato, University of Bologna
  • Statistics and Applications, Society of Statistics, Computer and Applications

 

Academic Year: 
2024-2025
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