Module Code and Title:        QME201 Introductory Econometrics

Programme:                           BA in Development Economics

Credit Value:                          12

Module Tutor:                        Sonal Mehta

General objective: This module provides an introduction to the basic econometric concepts and techniques. It covers estimation and diagnostic testing of multiple regression models with its various forms. Statistical software SPSS will be used for data analysis.

Learning outcomes – On completion of the module, students will be able to:

1. Arrange data in SPSS.
2. Conduct transformation of data in SPSS.
3. Create outputs in SPSS.
4. Design a relevant multiple regression model.
5. Interpret the regression output.
6. Identify the major assumptions of classical linear regression model.
7. Describe the properties of the estimators with the Gauss Markov Theorem.
8. Determine appropriate functional forms of a regression model.
9. Interpret the outputs of various regression model based on different functional forms.
10. Apply caution in the use of dummy variables.
11. Interpret the output of dummy variables.
12. Detect the violation of the major assumptions of classical linear regression functions.

Learning and Teaching Approach: 

Assessment Approach:

1. Lab work: 20 Marks

1. Students will undertake individual lab work, before mid-term covering Unit I using SPSS for data analysis (10 marks each) 

4    Arrangement of data in SPSS 

3    Compute and Transform variables

3    Graph

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

One written test will be conducted of 45 min duration and covering Unit II. The tests will contain 5 questions (3 on the conceptual understanding and 2 on problem solving).

Students will individually develop a multiple regression model, estimate the parameters and forecast. The assignment should have a maximum limit of 300-500 words. 

2    Description of the regression model 

2    Identify variables

6    Estimation of the parameters and forecast

Groups will complete a report based on an activity involving data analysis on regression using dummy variables. Report word limit: 500-750 words.

1    Modelling the regression with dummy variables 

2    Transforming the data to carry out the test in SPSS

4    Interpretation of the output 

1    Structure of the report (organisation and language)

2    Presentation (individually marked) 

Students will take a written exam of 1.5-hr duration covering topics up to the mid-point of the semester. The exam will comprise structured questions like MCQ, fill-in-the-blanks, matching, definition, as well as open-ended essay questions.

Students will take a written exam of 2.5-hr duration encompassing all the subject matter covered in the semester. This assessment is comprehensive and summative in nature, and will comprise structured questions like MCQ, fill-in-the-blanks, matching, definition, as well as open-ended essay questions.

Overview of assessment approaches and marks

Pre-requisites: STT202 Inferential Statistics for Economics 

Subject matter:

1. Unit I: Introduction to SPSS
   1. Layout, Menus and Icons
   2. Data – Compute and Transform
   3. Analyse
   4. Graph and Utilities 
2. Unit II: Multiple Linear Regression Model
   1. Steps for designing econometric models
   2. Basic concepts- Population regression function (PRF) and sample regression function (SRF), stochastic specification of PRF, significance of stochastic disturbance, coefficient of determination- R square, adjusted R square
   3. Classical Linear Regression Model (CLRM) and its main assumptions
   4. The Gauss-Markov theorem- properties of estimators
   5. Estimation of parameters and its interpretation (standardised and non-standardised beta coefficients)
   6. Reporting the results of regression analysis
   7. Forecasting
3. Unit III: Functional forms 
   1. Transforming data into logarithmic forms in SPSS, interpretation of outputs
   2. Log linear model
   3. Semi Log Models: log-lin model, lin-log model
   4. Reciprocal model
4. Unit IV: Dummy variable regression models
   1. Transforming data into dummy variables in SPSS, interpretation of outputs
   2. The nature of dummy variables
   3. Caution in the use of dummy variable
   4. Dummy variable with two qualitative explanatory variables
   5. Mixture of quantitative and qualitative explanatory variables
   6. Interaction effects using dummy variables, structural break analysis 
5. Unit V: Violation of CLRM
   1. Use SPSS for detection
   2. Multicollinearity: definition, detection with graph and variance inflation factor, remedies
   3. Heteroscedasticity: definition, detection with graph
   4. Autocorrelation: definition, Durbin Watson Test 

Reading List:

Essential Reading

Dougherty, C. (2011). Introduction to Econometrics. Oxford University Press, Indian Edition.

Gujarati, D. (2012). Econometrics: By Example. Palgrave Macmillan.

Gujarati, D.N., Porter, D.C. & Gunasekaran S. (2017). Basic Econometrics. Special Indian edition. McGraw Hill Education.

Kmenta, J. (2008). Elements of Econometrics. Indian Reprint, Khosla Publishing House.

Additional Reading

Greene, W.H. (2018). Econometrics Analysis.Pearson India

Kennedy,P (2008). A Guide to Econometrics. Wiley Blackwell

Date: June, 2022