Econometrics I

Paper Code: 
ECO 324(A-T)
Credits: 
4
Contact Hours: 
60.00
Objective: 

This course provides a comprehensive introduction to basic econometric concepts and techniques. It covers statistical concepts of hypothesis testing, estimation and diagnostic testing of simple and multiple regression models. The course also covers the consequences of and tests for violation of classical assumptions of regression model.

12.00
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, outliers.

12.00
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.

12.00
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.

12.00
Unit IV: 
Autocorrelation

Definition, sources and consequences, 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.

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.

Academic Session: 
2018-19
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