Module Code and Title:       QME103         Introductory Econometrics

Programme:                          BA in Development Economics

Credit Value:                         12

Module Tutor:                       Sonal Mehta

Module Coordinator:            Sanjeev Mehta

General objective: This module provides an introduction to basic econometric concepts and techniques. It covers estimation and diagnostic testing of simple and multiple regression models. The module also covers the consequences of and tests for misspecification of regression models. Statistical software such as SPSS will be used for data analysis.

Learning outcomes – On completion of this module, learners should be able to:

1. Interpret estimates and test results.
2. State a theory or hypothesis.
3. Apply a range of basic methods of inference to practical problems in econometrics and empirical economics.
4. Use SPSS for data analyses.
5. Run regression models for making predictions.
6. Identify the assumptions that underpin the classical regression model.
7. Identify and make adjustments for a number of common regression problems.
8. Justify and apply a relevant econometric model.

Learning and Teaching Approach: This module will be taught by means of lectures, tutorials, laboratory work, classroom workshops and self-directed study. Lectures will aim at explanation of various concepts and theories. Tutorials and laboratory work will be an integral part of the module, and it is expected that much of the learning and application of econometric concepts will be achieved through these tutorials and laboratory work. The focus of learning would be enhancing students’ abilities to understand each model and its underlying assumption so that they can apply the right model and make correct interpretations. It is also expected that students will spend additional independent hours on reading, problem solving, and econometric estimation each week. Students will be expected to use SPSS during laboratory work for at least 2 hours every week (1 as a class hour, 1 on their own).

Assessment Methods:

A. Individual Assignment: Portion of Final Marks: 10%

Before the mid-term examination, students must use a given theory/hypothesis to develop a relevant regression model, and submit a written report. The assignment should have a maximum limit of 300 words.

• 2%       Clear presentation of main concepts
• 2%       Identify variables
• 6%       Description of the functional form of regression model

B. Class Tests (2): Portion of Final Marks: 10%

One exam (worth 5%) will be given before and one after the mid semester, to test students’ ability to make correct interpretations of regression outputs; 2-3 questions in each test. Time: 45 minutes.

C. Case Analysis: Portion of Final Mark: 10%

Analysis of a regression model. Students will work on analysis of a research paper provided by the tutor to analyse the methodology applied to test the data given. Report word limit: 300 words.

• 3%       Justification for the given methodology
• 3%       Provide alternative methodology
• 1%       Review possible issues with the suggested methodology
• 2%       Handling Q&A session (individually marked)

D. Practical assignments (2): Portion of Final Marks: 20%

One before and after the midterm examination (10% each): students will complete lab work using SPSS for data analysis and result interpretation using raw data.

• 2%       Arrangement of data in SPSS
• 2%       Use of appropriate econometric tool(s)
• 1%       Convert the SPSS output into required (journal) format
• 5%       Interpretation of the results

A. Midterm Examination: Portion of Final Mark: 15%

Students will take a written exam of 1.5 hr duration covering topics up to the mid-point of the semester.

Pre-requisites:

Subject matter:

1. Introduction to SPSS
   • Layout, Menus and Icons
   • Data, Transform
   • Analyse
   • Graph and Utilities
2. Introduction
   • Methodology of econometrics
   • Types of econometrics
   • Nature and scope of econometrics
3. Simple Linear Regression Model (Note: From unit 3 to unit 7, focus should be on developing conceptual framework, ability to decide when and which econometric tool needs to be applied and interpretation of SPSS output. Calculation and derivation of equations is not required.)
   • Two variable OLS model, CLRM and its main assumptions
   • Monte Carlo experiment
   • Properties of estimators
   • Goodness of fit
   • Tests of hypotheses
   • Scaling and units of measurement
   • Confidence intervals; analysis of variance
   • Gauss-Markov theorem
   • Reporting the results of regression analysis
   • Forecasting
4. Multiple Linear Regression Model
   • Estimation of parameters
   • Properties of OLS estimators
   • Goodness of fit - R2 and adjusted R2
   • Partial regression coefficients
   • Testing hypotheses – individual and joint
   • Functional forms of regression models
   • Qualitative (dummy) independent variables
5. Violations of Classical Assumptions
   • Consequences, Detection and Remedies
   • Multicollinearity
   • Heteroscedasticity
   • Serial correlation
6. Specification Analysis
   • Omission of a relevant variable
   • Inclusion of irrelevant variable
   • Tests of specification errors

Reading List:

1. Essential Reading
   • Dougherty, C. (2007). Introduction to Econometrics. Oxford University Press, Indian Edition.
   • Gujarati, D.N., Porter, D.C. & Gunasekaran S. (2012). Basic Econometrics. Special Indian edition. McGraw Hill.
2. Additional Reading
   • Gujarati, D. (2012). Econometrics: By Example. Palgrave Macmillan.
   • Kmenta, J. (2008). Elements of Econometrics. Indian Reprint, Khosla Publishing House.

Date: January 15, 2016