Econometrics-II

Paper Code:

25ECO 424(A-T)

Credits:

Contact Hours:

30.00

Max. Marks:

100.00

Objective:

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

Course Outcomes:

Course Outcomes (COs):

Course

Learning outcome (at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course Title

25ECO 424(A-T)

Econometrics-II

(Theory)

Students will

CO145: analyze the meaning and applications of dummy variables.

CO146: analyze the estimation of distributed lag models and related aspects.

CO147: interpret panel data regression models and fundamentals of time series data

CO148: analyze the estimation of various models based on time series data.

CO149: analyze the application of simultaneous equations model and related aspects.

CO150: contribute effectively in course-specific interaction.

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 R²and 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.

References:

Suggested Readings:

Gujarati, Damodar, N., Porter, Dawn C. and Pal, Manoranjan, Basic Econometrics, McGraw-Hill Education, 2021.
Dougherty, Christopher, Introduction to Econometrics, Oxford University Press, 2011.

E-Resources: