ECONOMETRICS-I

Paper Code: 
GECO 304
Credits: 
4
Periods/week: 
4
Max. Marks: 
100.00
Objective: 

The course will enable the students to

  1. To acquaint the students with the statistical concepts used in econometrics. 
  2. To estimate and interpret the simple and multiple linear regression models.
  3. To acquaint the students with the estimation and use of various functional forms.

Course Outcomes (COs).

 Course Outcome (at course level)

Learning and teaching strategies

Assessment Strategies 

 
 

On completion of this course, the students will:

CO152. describe the nature and scope of econometrics.

CO153. examine various statistical tools like probability distributions used in econometrics.

CO154. comprehend the estimation and inference of simple and multiple linear regression models and functional forms.

CO155. know about the techniques of verification of economic theories and laws.

CO156. come to get trained about how the research is conducted.

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: 
Nature of Econometrics and Statistical Concepts Nature, scope and methodology of econometrics;
Statistical concepts: Normal distribution; chi-square, t- and F-distributions; estimation of parameters; properties of estimators; testing of hypotheses: defining statistical hypotheses Type I and Type II errors; power of a test.
 
 
12.00
Unit II: 
Simple Linear Regression Model: Two Variable Case-I Nature of regression analysis; assumptions of Classical Linear Regression Model, estimation of model by method of ordinary least squares; properties of least square estimators; Gauss-Markov theorem.
 
 
12.00
Unit III: 
Simple Linear Regression Model: Two Variable Case-II Goodness of fit; tests of hypotheses; scaling and units of measurement; confidence intervals; Chow Test, forecasting.
 
 
 
12.00
Unit IV: 

 

Functional forms of regression models Log-linear model, semilog models, reciprocal models and logarithmic reciprocal model.
 
 
Functional forms of regression models Log-linear model, semilog models, reciprocal models and logarithmic reciprocal model.
 
Multiple Linear Regression Model Estimation of parameters; properties of OLS estimators; partial regression coefficients; goodness of fit - R2 and adjusted R 2; testing hypotheses – individual and joint.
12.00
Unit V: 

Multiple Linear Regression Model Estimation of parameters; properties of OLS estimators; partial regression coefficients; goodness of fit - R2 and adjusted R 2; testing hypotheses – individual and joint.

ESSENTIAL READINGS: 
  1. D. N. Gujarati and D.C. Porter, Essentials of Econometrics, McGraw Hill,  4th edition, International Edition, .
  2. Christopher Dougherty, Introduction to Econometrics, Oxford University Press.
  3. Jan Kmenta, Elements of Econometrics, Indian Reprint, Khosla Publishing House.

 

REFERENCES: 
  1. A. Koutsoyiannis, Theory of Econometrics, Palgrave Macmillan.

E RESOURCES:

  • Eviews: www.eviews.com
  • 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.

JOURNALS:

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

 

Academic Year: 
2023-24
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