FS-718 ECONOMETRICS Credits: 4 (2-1-2) Objective: The objective of this paper is to understand the different mathematical toolS that are applied to business problems to find their solutions. COURSE DESCRIPTION: Unit I: Random variable, expectation of random variable. Nature and scope of econometrics. Types of data; meaning and methodology of econometrics; meaning of causal relationship; nature of regression analysis; properties of good estimator; Gauss-Markov theorem. Unit II: Basics of two-variable regression analysis. Estimation and hypothesis testing; interpretation of results and their application. Extensions of the two-variable linear regression model. Multiple regression model, its estimation and inference; types of non-linear regression models and their applications. Matrix Approach to Linear Regression Model. Assumptions, OLS Estimation and their properties. Interpretation of Results. Comparison of ANNOVA and Regression Analysis; F-Test and T-Test. Unit III: Nature, consequences, detection and remedial measures of specification bias, heteroscedasticity, autocorrelation and multi colinearity. Regression on dummy variables and their applications. Unit IV: Autoregressive Distributed Lag Models- Adaptive Expectation Model, Partial Adjustment Model, Estimation of Autoregressive Model, Detecting auto correlation in Autoregressive Model. The Almon Approach to Distributed Lag Model and Principal Component Analysis. Systems of equations, identification and estimation methods (ILS and 2SLS) of simultaneous equation models.

Text Books: 1. D.Gujrati & Sangeetha- Basics Econometrics- Tata McGraw Hill Publication- Vth Edition. 2. Koutsoyiannis A -Theory of Econometrics E L B S/Macmilian 3. Schmidt P. Econometrics, Marcel Dekker, N.Y. 4. Maddala, G.S., Econometrics- Willey Eastern Publication 5. Rao & Miller, Applied Econometrics Prentice-Hall Course Plan

Week Unit Topics Hours Lecture Tutorial Practical

1 I Random variable, expectation of random variable. Nature and scope of econometrics. Types of data;

meaning and methodology of econometrics

2 1 2

2 I Meaning of causal relationship; nature of regression analysis

2 1 2

3 I Properties of good estimator; Gauss-Markov theorem

2 1 2

4 II Basics of two-variable regression analysis. Estimation and hypothesis testing; interpretation of results and their application

2 1 2

5 II Extensions of the two-variable linear regression model. Multiple regression model, its estimation and inference; types of non-linear regression models and their applications.

2 1 2

6 II Matrix Approach to Linear Regression Model. Assumptions, OLS Estimation and their properties. Interpretation of Results.

2 1 2

7 II Comparison of ANNOVA and Regression Analysis; F-Test and T-Test.

2 1 2

8 III Nature, consequences, detection and remedial measures of specification bias, heteroscedasticity.

2 1 2

9 III autocorrelation and multi colinearity 2 1 2 10 III Regression on dummy variables and their

applications 2 1 2

11 IV Autoregressive Distributed Lag Models- Adaptive Expectation Model, Partial Adjustment Model

2 1 2

12 IV Estimation of Autoregressive Model, Detecting auto correlation in Autoregressive Model

2 1 2

13 IV The Almon Approach to Distributed Lag Model and Principal Component Analysis.

2 1 2

14 IV Systems of equations, identification and estimation methods (ILS and 2SLS) of simultaneous equation models.

2 1 2

15 IV Case studies 2 1 2 16 IV Revision 2 1 2 TOTAL 32 16 32