ECONOMETRICS-II

Paper Code: 
ECO 424(A-T)
Credits: 
2
Contact Hours: 
30.00
Objective: 

Course Objectives:

The objectives of this course are-

  1. To acquaint the students with the use of dummy variable and panel data in econometric research and estimation of autoregressive and distributed lag models.
  2. To develop an understanding of the use of testing of stationarity, granger causality tests and forecasting with ARIMA models. 
  3. To acquaint the students with the construction, identification, estimation and interpreting of simultaneous equations models.

 

Course Outcomes (COs):

Course

Outcome (at course level)

Learning and teaching strategies

Assessment Strategies 

Paper Code

Paper Title

ECO 424(A-T)

Econometrics-II

CO63: Understand the meaning and applications of dummy variables.

CO64: Acquire knowledge about estimation of panel data regression models.

CO65: Analyse time series data and the estimation of various models based on time series data.

Approach in teaching: Interactive Lectures and Discussions.

 

Learning activities for the students:

Practice Modules and

Assignments.

Class activity, Assignments and Semester end examinations.

 

6.00
Unit I: 

Dummy Variables

Definition, Simple Regression Model with Dummy  independent variable(s) - ANOVA models, dummy variable trap, ANOVA model with  interaction dummy, ANCOVA models, comparing two regressions - Chow test and dummy variable approach.

Models with Dummy Dependent Variable

Linear Probability Model, Logit Model, Probit Model, Measuring goodness of fit- Effron’s R2 and McFadden’s Pseudo R2, Examining the overall significance of regression. 

6.00
Unit II: 
Distributed Lag Models

Definition and Specification, Geometric Lag Approach, Estimation of Geometric Lag Model- The Koyck Method, Adaptive Expectations Model, Partial Adjustment Model, Almon’s Polynomial Lag Model. 

6.00
Unit III: 

Panel Data Regression Models

Definition, usefulness and types, Panel Data Models- Constant Coefficients Model, Fixed-Effects Model, Random Effects Model, The Hausman Test.

Time Series Econometrics-Important concepts

Stochastic process, stationary stochastic process, white noise stochastic process, random walk(with and without drift), unit root stochastic process, sources of non-stationarity, spurious regression problem.

 

6.00
Unit IV: 

Time Series Econometrics -Tests for stationarity

Graphical approach, Autocorrelation function and Correlogram, Unit root test, Dickey-Fuller test, Augmented Dickey-Fuller test, Phillips-Perron test.

Cointegration and Error Correction Mechanism

 Engle-Granger cointegration test and ECM.

 ARIMA Forecasting- Box-Jenkins Methodology, Vector Autoregressive Model- Specification, estimation, forecasting, Vector Error Correction Model.

Causality tests

Granger Causality test and Sims Causality test.

6.00
Unit V: 
Simultaneous Equations System

Meaning , features and examples, OLS estimation ignoring simultaneity, Structural and reduced form equations, Identification problem- Rules and application, Estimation of Simultaneous Equations System- Indirect Least Squares and Two-Stage Least Squares.

Essential Readings: 

Bhaumik, S.K., Principles of Econometrics- A Modern Approach using EViews, Oxford University Press, 2015.

 

Academic Session: 
2020-21
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