Econometrics- I

Paper Code: 
24CECO 512
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

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 of regression models

 

Course Outcomes: 

Course

Code

Course Title

Learning outcome (at course level)

Learning and teaching strategies

Assessment Strategies 
   

24CECO 512

 Econometrics-I

(Theory)

Students will:

CO55: analyse the fundamentals of econometrics and important statistical concepts.

CO56: analyzesimple regression estimation and interpretation.

CO57: apply hypothesis testing in case of simple regression.

CO58: analyze the problems related with different functional forms and their estimation.

CO59: estimate and interpret the results of multiple regression

CO60: contribute effectively in course-specific interaction.

Approach in teaching: Interactive Lectures, Discussion,  Case studies.

 

Learning activities for the students:

Presentations, Assignments and Group discussions.

Class activity, Assignments and  Semester end examinations.

 

12.00
Unit I: 
Nature of Econometrics and Statistical Concepts
  • Nature, scope and methodology of econometrics, types of econometrics
  • Statistical concepts- Normal distribution; chi-square, t- and F-distributions; estimation of parameters; properties of estimators; testing of hypotheses: defining statistical hypotheses, confidence interval and test of significance approaches, Type I and Type II errors; power of a test. Time series, cross-section, pooled and panel data

 

12.00
Unit II: 
Simple Linear Regression Model: Two Variable Case-I
  • Nature of regression analysis - PRF and SRF
  • Assumptions of Classical Linear Regression Model
  • Significance of the stochastic disturbance term
  • Estimation of model by method of ordinary least squares
  • Reporting and interpretation of regression results
  • Properties of least square estimators, Gauss-Markov theorem

 

12.00
Unit III: 
Simple Linear Regression Model: Two Variable Case-II

·       Goodness of fit- R2 and adjusted R 2

·       Tests of hypotheses

·       Scaling and units of measurement

·       Confidence intervals

.       Chow Test; Prediction

12.00
Unit IV: 
Functional forms of regression models
  • Log-linear model
  • Semilog models – lin-log model and log-lin model
  • Reciprocal models
  • Logarithmic reciprocal model
  • Choice of functional form
  • Regression through origin

 

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. Gujarati, D. N. and Porter, D.C., Essentials of Econometrics, McGraw Hill, International Edition. 4th Edition, 2010.
  2. Dougherty, C., Introduction to Econometrics, Oxford University Press, 5th Edition, 2016.

      3.    Koutsoyiannis, A., Theory of Econometrics, Palgrave Macmillan,                          2nd Edition, 2001.

 

References: 

Suggested Readings:

  1. Smith, A. and Taylor, J. E., Essentials of Applied Econometrics, University of       California Press, 2017.
  2. Kmenta, J., Elements of Econometrics, Indian Reprint, Khosla Publishing House,           2nd Edition, 2018.

E- Resources:

  1. Econometrics Academy--------sites.google.com/site/econometricsacademy
  2. MIT Open Courseware---------ocw.mit.edu
  3. Econometrics: Methods and Applications-Coursera-------coursera.org
  4. Crunch Econometrics----------cruncheconometrix.com
  5. Explaining the Core Theories of Econometrics------udemy.com

Journals:

  1. The Journal of Econometrics
  2. Academic.oup.com/ectj
  3. Journal of Applied Econometrics
  4. Onlinelibrary.wiley.com/journal/18735924

 

Academic Session: 
2024-25
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