ECONOMETRICS-II

Paper Code: 
GECO 404
Credits: 
4
Periods/week: 
4
Max. Marks: 
100.00
Objective: 

The course will enable the students to

  1. To understand the meaning, reasons and consequences of the violation of assumptions of the regression model.
  2. To acquaint the students with the detection and remedial methods of multicollinearity, heteroscedasticity and autocorrelation. 
  3. To appraise the students with the estimation and interpretation of models with dummy independent variables.

Course Outcomes (COs).

Course Outcome (at course level)

Learning and teaching strategies

Assessment Strategies 

 
 

On completion of this course, the students will:

CO201. understand the meaning, reasons and consequences of the violation of assumptions of OLS method.

CO202. examine the different methods of detecting the violations and their remedial measures.

CO203. understand the applications of dummy variables in regression models.

CO204. interpret different types of error to be made while doing research study.

CO205. know about the techniques to be used in optimization of the empirical results.

Approach in teaching. Interactive Lectures and Discussions.

 

Learning activities for the students.

Practice Modules and

Assignments.

Class activity, Assignments and Semester end examinations.

 

 

12.00
Unit I: 
 
Multicollinearity
Meaning, reasons, consequences, detection and remedies of the problem of multicolliearity.
 
 
12.00
Unit II: 
Heteroscedasticity
Meaning, reasons, consequences, detection and remedies of the problem of heteroscedasticity.                                                                                                        
 
 
12.00
Unit III: 
Serial correlation
Meaning, reasons, consequences, detection and remedies of the problem of serial correlation.
 
 
12.00
Unit IV: 
Specification error Meaning of model specification, Specification error- omission of a relevant variable; inclusion of irrelevant variable; errors of measurement, tests of specification errors.
 
 
12.00
Unit V: 
Qualitative (dummy) independent variables Nature of dummy variables; ANOVA and ANCOVA models; caution in the use of dummy variable-the dummy variable trap; the dummy variable alternative to the chow test; Interaction effects using dummy variables.
 
ESSENTIAL READINGS: 
  1. Gujarati, D.N. and Porter, D.C., Essentials of Econometrics, McGraw

      Hill,4th Edition, 2010.

  1. Dougherty, C.,Introduction to Econometrics, Oxford University

 

REFERENCES: 
  1. Klein, L. R. , An Introduction to Econometrics, Printice-Hall, Englewood   Cliffs, NJ, 1962.
  2. Walters, A. A., An Introduction to Econometrics., Macmillan, 1968.
  3. Smith, A. and Taylor, J. Edward Essentials of Applied Econometrics, University of California Press,          2017.
  4. Kmenta, J.,  Elements of Econometrics, Indian Reprint, Khosla Publishing  House, 2nd Edition, 1997.

E RESOURCES

  • Chow, G.,’Use of Dummy Variables in Testing for Equality between Sets of Coefficients in Two Linear Regressions. A Note’, American Statistician, 24(1), 50-52, 1970.
  • Mason, R. L. Gunst, R. F. and Webster, J. T., ‘Regression Analysis and Problems of Multicollinearity’, Communications in Statistics A, 4(3),  277-292, 1975.
  • Kumar, T. k., ‘Multicollinearity in regression Analysis,’ Review of Economics and Statistics, 57, 365-366, 1975.
  • Park, R. E., ‘Estimation with Heteroscedastic Error Term’, Econometrica, 34(4), 888, 1966.
  • Glejser, ‘A new Test for Heteroscedasticity,’ Journal of the American Statistical Association, 64, 316-323, 1969.
  • Breusch, T. and Pagan, A., ‘A Simple test for Heteroscedasticity and Random Coefficient Variation,’Econometrica, 47, 1287-1294, 1979.
  • Durbin, J. and Watson, G. S., ‘Testing for Serial Correlation in Least Squares Regression,’Biometrika, 38, 159-171, 1951.

JOURNAL

  •   Theoretic and Applied Econometrics: http://www.ectap.ro/

 

Academic Year: 
2023-24
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