Econometrics

Paper Code: 
STT 323
Credits: 
5
Contact Hours: 
75.00
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: 

Students will able to

Course

Course Outcomes

Learning and teaching strategies

Assessment Strategies

 

Paper Code

Paper Title

STT 323

Econometrics

 

CO 68: 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 69: Remove the problems of econometrics such as heteroscedasticity, autocorrelation, multicollinearity.

 

CO 70: 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 71: Construct, test, and analyse and interpretate econometric models, using variables and relationships commonly found in studies of economic theory.

 

CO 72: Make use of econometric models in your own academic work.

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

 

15.00
Unit I: 

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.

 

15.00
Unit II: 

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

 

15.00
Unit III: 

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

 

15.00
Unit IV: 

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

 

15.00
Unit V: 

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: 
2023-2024
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