Econometrics

Paper Code: 
DSTT 601(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.

 

Students will be able to:

Course

Learning outcomes (at course level

Learning and teaching strategies

Assessment Strategies

Paper Code

Paper Title

DSTT 601 (B)

Econometrics 

CO 69: Construct, test, and analyse and interpretate econometric models, using variables and relationships commonly found in studies of economic theory.

 

 CO 70: Identify key classical assumptions in the field of econometrics, explain their significance, and describe the effects that violations of the classical assumptions can have.

 

CO 71: Use the least squares method in evaluating the relationship of one explanatory variable to the dependent variable and the relationships of multiple explanatory variable to the dependent variable.

 

CO 72: Mitigate and resolve challenges commonly encountered in econometrics, including heteroscedasticity, autocorrelation, and multicollinearity, through different techniques and methodologies.

 

 CO 73: Demonstrate a comprehensive understanding of demand theory and its practical application in various real-world scenarios

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, 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: 

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

12.00
Unit III: 

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-Concept, Consequences, Detection and Remedies. Heteroscedasticity and serial correlation– Concept and Consequences.

12.00
Unit V: 

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.
References: 

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
  • Stochastic Modeling and Applications, MUK Publications and Distributions
Academic Year: 
2023-2024
  • Printer-friendly version
  • PDF version