This course introduces the regression methods for analyzing data in economics. This is an introductory course in the theory and practice of classical econometric methods. The main components of the course deal with Single Equation Models, Dynamic Equation Models, Instrumental Variable Estimation and Multiple Equation Models.  This course emphasizes both the theoretical and the practical aspects of statistical analysis, focusing on techniques for estimating econometric models of various kinds and for conducting tests of hypotheses of interest to economists. Some basic knowledge of matrix algebra and elementary statistical theory will be assumed, but a lot of it will be re-introduced during the lectures. The goal of this course is to help the students to develop a solid theoretical background in introductory level econometrics, the ability to implement the techniques and to critique empirical studies in economics.  The computer is a fundamental tool in this course and students will be required to become familiar with some statistical software such as R, Eviews, STATA to analyze the econometric data and fitting of econometric models.

Learning outcomes

  • Conduct basic statistical and econometric analysis. Explain and interpret econometric results.
  • Explain econometric concepts and results intuitively, conduct independent data analysis and inquiry using the tools of statistics and econometrics.
  • Conduct Research with econometrics, derive econometric results mathematically


  1. Gujrati, DN. (2004). “Basic Econometrics”, John Wiley, New York.
  2. Johnston, J. and Di. Nardo, J., (1997). “Econometric Method”, 4th Edition, McGraw Hill, New York.
  3. Searle, S. R. (1971), “Linear Models”, John Wiley, New York
  4. Yan, X. and Zu, X. G. (2009) Linear Regression Analysis: Theory & Computing. World Scientific Publications.
  5. Salvatore, D. and Reagle, D. (2002). Theory and Problems of Statistics and Econometrics, 2nd Edition. McGraw-Hills, New York.  


Course Plan




Topics and Reading.



  Introduction to econometrics and its types. Econometrics data types

Basic Econometrics


  Review of the linear regression model assumptions

Basic Econometrics


 Multicollinearity: Definition, nature, consequences

Basic Econometrics


Multicollinearity:Tests and solutions.

Basic Econometrics


Heteroscedasticity: Definition, nature, consequences

Basic Econometrics


Heteroscedasticity: Tests and solutions

Basic Econometrics


Autocorrelation: Definition, nature, consequences

Basic Econometrics


Autocorrelation: Tests and solutions

Basic Econometrics


 Specifications Tests

Basic Econometrics


Error in variables problem: Reasons, Consequences, Tests and Solutions

Basic Econometrics


Autoregressive and distributed lagged models.

Basic Econometrics


Simultaneous Equation System

Basic Econometrics



Basic Econometrics.   (388-408)


Two-stage and three-stage Least Squares.

Linear Models in Matrix form. (341-348)

Basic Econometrics.   (441-447)


Econometrics modeling with eviews and R

The R book



Description of system of Evaluation

Exam: Mid (30%), Final (50%), Sessional (20%): Assignments, Presentations, Quizzes, Class Participation.

Lecture Time (Regular) :
Tuseday (8:00 AM-9:00 AM), Wensday (11:00 AM-12:00 PM), Thursday (10:00 AM-11:00 AM), Friday (10:00 AM-11:00 AM)

Course Material