Course Contents

1. Simultaneous equation models  Types of simultaneous equation system, the identification problem, simultaneous equation model: ILS, 2SLS, 3SLS. 2. Dynamic Econometric Model  Autoregressive and Distributive lag Models  The Granger Causality Test and VAR model 3. Time-series Econometrics  Stationary and non-stationary time series: Introduction, tests of stationarity, graphical analysis, unit root test (Dickey -fuller and augmented Dickey-fuller test)  The phenomenon of spurious regression  Transforming non stationary time series to stationary time series.  Co-integration: co-integrating regression, test for co-integration(Engle-Granger test and co-integration regression Durbin Watson test) 4. Panel data Regression Models  Why Panel data  Estimation of Panel data Regression Models: Fixed effect and Random effect model.

Course Synopsis

The objective of this course is to develop skill for empirical analysis of the real world economic inter-relationships. This course covers the Econometric techniques required to understand empirical economic research and to plan and execute independent research projects. Topics include simultaneous equation models, dynamic econometric model, time series econometrics, and panel data regression analysis. This course introduces the use of econometrics to explore and estimate economic relationships using simultaneous equations models. The course will give students a basic understanding of dynamic econometric models and the techniques to estimate these models. The fundamental knowledge to understand and analyze the nonstationary time series data and panel data will also be imparted by this course. The prerequisite courses include Econometrics-I (Maj/Eco-305) and Econometrics-II (Maj/Eco-401) or equivalent courses.

Course Learning Outcomes

By the end of this course it is expected that the student will be able:  to develop and estimate the simultaneous equation models and dynamic econometric models.  to understand and test the stationarity and cointegration of time series data.  to comprehend pooled or panel data and to learn the related estimation techniques.