Econometrics-I

Paper Code:

25ECO 324 (A-T)

Credits:

Contact Hours:

30.00

Max. Marks:

100.00

Objective:

To develop an understanding of the specification, estimation and interpretation of simple linear regression models.
To acquaint the students with the estimation and use of multiple linear regression models and various functional forms of the regression model.
To appraise the students with the detection, effects and remedial methods of multicollinearity, heteroscedasticity and autocorrelation problems.

Course Outcomes:

Course Outcomes (COs):

Course

Learning outcome (at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course Title

25ECO 324(A-T)

Econometrics-I

(Theory)

Students will

CO97: estimate simple linear regression model and related aspects

CO98: examine the inference of multiple linear regression model and related aspects

CO99: analyze heteroscedasticity and related aspects.

CO100: analyze autocorrelation and related aspects.

CO101: analyze 8multicollinearity and related aspects.

CO102: contribute effectively in course-specific interaction.

Approach in teaching: Interactive Lectures and Discussions.

Learning activities for the students:

Practice Modules and

Assignments.

Class activity, Assignments and Semester end examinations.

6.00

Unit I:

Simple Linear Regression Model

Scope and Methodology of Econometrics, types and sources of data
Assumptions for estimation
Simple Linear Regression Model- OLS estimation, properties of OLS Regression line, properties of OLS Estimators, Statistical inference of SLRM
goodness of fit, Analysis of Variance on regression
Regression without intercept term- hypothesis testing and goodness of fit
Reverse regression and outliers.

6.00

Unit II:

Multiple Linear Regression Model

Definition, specifications and assumptions, OLS estimation, properties of OLS Estimators
goodness of fit, inferences in MLRM- testing the significance of individual regression coefficients
testing the overall significance of regression, testing relevance of an additional explanatory variable, testing validity of linear equality restriction.

6.00

Unit III:

Heteroscedasticity

Definition, sources and consequences
methods of detection- Graphical, Breusch-Pagan-Godfrey test, Glejser test, Goldfeld-Quandt test, White’s test
remedial measures- Based on idea about form of heteroscedasticity, Generalised Least Squares, Weighted Least Squares, Heteroscedasticity-Consistent Estimator, general measures.

6.00

Unit IV:

Autocorrelation

Definition, sources and consequencesand specification of Autocorrelation relationship
tests for Autocorrelation- Graphical, Durbin-Watson test, Theil-Nagar correction to Durbin-Watson d-statistic, Durbin’s h-test, Breusch-Godfrey Lagrange Multiplier test
remedial measures- When value of ρ is known and when value of ρ is not known,
Heteroscedasticity and Autocorrelation Consistent Standard Errors.

6.00

Unit V:

Multicollinearity

Definition, sources and consequences (absence of multicollinearity, perfect multicollinearity and imperfect multicollinearity)
tests for Multicollinearity- Correlation Analysis, Klein’s rule of thumb, Variance-Inflation Factor, Tolerance, Condition Number
remedial measures- Increasing sample size, Transformation of variables, using extraneous estimate, dropping variables, other methods.

Essential Readings:

Bhaumik, S.K., Principles of Econometrics- A Modern Approach using EViews, Oxford University Press, 2015.

References:

Suggested Readings:

Gujarati, Damodar, N., Porter, Dawn C. and Pal, Manoranjan, Basic Econometrics, McGraw-Hill Education, 2021.
Dougherty, Christopher, Introduction to Econometrics, Oxford University Press, 2011.

E-Resource: