COURSE OBJECTIVES:
 To make students acquaint with econometric techniques that are widely used in empirical work in Economics and other related disciplines
 To make students learn the art of performing estimation, analysis and interpretation of the estimated econometric model
LEARNING OUTCOMES:
After the completion of the course, students will be able to –
 Understand the econometric theories and techniques
 Demonstrate their understanding of the appropriate econometric models for analyzing data
 Interpret the computer output for the estimation and testing of econometric methods for analyzing data.
 Interpret and discuss the results
REFERENCES:
 Gujarati, Porter and Gunasekhar, Basic Econometrics, Fifth Edition
 James H Stock and Mark W. Watson, Introduction to Econometrics, Pearson Education; 3rd edition
 RamuRamanathan, Introductory Econometrics with Applications, S.Chand & Company Ltd; 5th Revised edition
 Christopher Dougherty, Introduction to Econometrics. NewDelhi: Oxford University Press
 Dominick Salvatore , Derrick Reagle, Schaum’s Outline of Statistics and Econometrics, Second Edition, McGrawHill Education
 A Koutsoyiannis, Theory of Econometrics, Second Edition, Palgrave Macmillan

Module 1
Classical Liner Regression Model—Meaning and methodology—Modern interpretation of econometrics—Population regression function (PRF) —The concept of linearity in econometrics—stochastic Variable –interpretation and its significance — Sample regression function (SRF)
 Meaning of Econometrics
 Introduction to Econometrics
 Terminology
 Types of Data
 Measurement Scales of Variables
 Causation and Correlation
 Methodology of Econometrics
 Methodology of Econometrics in details part 1
 Methodology of Econometrics in details part 2
 Historical and Modern Interpretation of Econometrics
 Population Regression Function
 PRF Part 1
 Introduction to SRF
 PRF and SRF

Module 2
Estimation of PRF—The method of OLS—Advantages of OLS—Numerical Properties of OLS estimators— Statistical properties of OLS— Gauss  Markov Theorem and the assumptions of Classical Linear Regression Model

Module 3
Evaluation of SRF—Goodness of the Fit—R Square—Reliability and Precision of OLS estimators—Standard Error of the OLS Estimator and the Estimate

Module 4
Hypothesis testing and estimation—Hypothesis testing of OLS estimators—t test—Point and interval estimation (Basics)—Introduction to Multiple Regression

Module 5
Relaxing the assumptions of Classical Linear Regression Model Heteroscedasticity— nature, estimation in its presence—detection and remedial measures— Autocorrelation— nature and estimation in its presence—detection and remedial measures— Multicollinearity—nature, estimation in its presence—detection and remedial measures
 Multiple Linear Regression Model
 MLRM Derivation of Beta two and three part 1
 MLRM Derivation of Beta two and three part 2
 Matrix notations of MLRM
 Assumptions of MLRM
 Partial Regression coefficients MLRM
 Variance and Standard Error of OLS in MLRM
 Logit Model
 Probit Model
 ANOVA ANCOVA Dummy Variable Trap
 Simultaneous Equation Model 1
 Simultaneous Equation Model 2
 Simultaneous Equation Identification Problem
 Simultaneous Equation 2SLS Method
 Dynamic Model Adhoc Estimation
 Koyck Model and its Rationalization
 Partial Stock Adjustment Model
 Almon Approach
 Estimation of AR Model and Instrumental Variables
 The Granger Causality Test and SIM test