The aim of this course is to equip the students with the skills required to undertake independent applied research using modern econometric methods. The course begins with revision of some of the fundamental concepts and aims to extend students’ understanding of the subject to an advanced level as each part progresses. The course attempts to provide a balance between theory and applied research.



1. Introduction

Brief introduction to course; Why study econometrics? What is an econometric model? Methodology of econometric model; Terminology and notation; The nature and sources of data.



2. The Classical Linear Regression Model

Two Variable Regression Analysis: Some Basic Ideas

• A Hypothetical Example

• Concept of Population Regression Function

• The Meaning of the Term “Linear”

• Stochastic Specification of PRF

•The Significance of the Stochastic Disturbance Term

•The Sample Regression Function

Two Variable Regression Analysis: The Problem of Estimation

• The method of ordinary least squares

• Assumption underlying the Method of Least Squares

• Precision of Least Square Estimates

• The properties of the least squares estimates of an econometric model

• The Coefficient of Determination r2: A Measure of Goodness of Fit

• Numerical Examples and interpretation of the results

Interval estimation and hypothesis testing

• Interval Estimation

• Confidence Interval for Regression Coefficient

• Hypothesis Testing

Multiple Regression Analysis: The Problem of Estimation

• The econometric model with more than one independent variable

• Interpretation of the Multiple Regression equation

• Ordinary least squares estimation of the Partial regression coefficients

• The Multiple Coefficient of Determination R2

• R2 and Adjusted R2

• Examples and interpretation of the Results

Multiple Regression Analysis: The Problem of Inferences

• Hypothesis testing in Multiple Regression

• Hypothesis testing about individual Partial regression coefficient

• Testing the Overall Significance of the Sample regression

• Testing the equality of two regression Coefficients

• Comparing the Two Regression



3. Violations of Assumptions of the CLRM

• Multicolinearity

• Autocorrelation

• Heteroscedasticity



4. Computer Application