Econometrics

Paper Code: 
24STT323
Credits: 
6
Contact Hours: 
90.00
Max. Marks: 
100.00
Objective: 

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

 

Course Outcomes: 

Course

Course Outcomes

Learning and teaching strategies

Assessment Strategies

Course Code

Course Title

24STT323

Econometrics

(Theory)

CO 79: Identify and explain the significance of classical assumptions in the field of econometrics.

CO 80: Analyze and remove the problems of heteroscedasticity, autocorrelation and multicolinearity in econometrics.

CO 81: Use the least squares method in evaluating the relationship of one and more explanatory variables to the dependent variable.

CO 82: Construct, test, analyze and interpret econometric models using variables and relationships of economic theory.

CO 83: Determine the Correlated variable and learn how to minimize the complexity of the econometric model.

CO 84: 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.

 

18.00
Unit I: 
Introduction to Economic Phenomenon and Linear Models

Representation of Economic phenomenon, relationship among economic variables, the general linear model and its extensions, basic assumptions, Ordinary least squares estimation and prediction, generalized least square estimation and prediction.

 

18.00
Unit II: 
Problems in Linear Regression

Heteroscedasticity, Auto-correlation: its consequences and tests (Durbin Watson test), Multicollinearity: problem, its implications and tools for handling the problem.

 

18.00
Unit III: 
Advanced Regression Techniques

Linear regression and stochastic regression, instrumental variable estimation, autoregressive linear model, lagged variables, Distributed Lag models: Koyck’s Geometric Lag model.

 

18.00
Unit IV: 
Simultaneous Equation Models

Simultaneous equation model: Basic rationale, Consequences of simultaneous relations, Identification problem, Conditions of Identification, Indirect Least Squares, Two-stage least squares, K-class estimators, Limited Information and Full Information Maximum Likelihood Methods.

 

18.00
Unit V: 
Advanced Estimation Techniques

Three stage least squares, generalized least squares, Recursive models, SURE Models. Mixed Estimation Methods, use of instrumental variables, pooling of cross-section and time series data, Principal Component Methods.

 

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.

 

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: 
2024-2025
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