Module Code and Title:       QME304         Intermediate Econometrics

Programme:                          BA in Development Economics

Credit Value:                         12

Module Tutor:                       Sanjeev Mehta

General objective: This module extends students' knowledge and understanding to more sophisticated econometric methods and techniques: simultaneous equations, binary regression models, time series analysis and panel models. The focus of this module will be on promoting understanding of the concepts and methods involved, as well as interpretation of computational (e.g., Stata) output, and not on abstract calculations.

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

1. Develop relevant econometric models.
2. Evaluate the adequacy of a given model using a range of diagnostic tests.
3. Present results and provide clear interpretation of the results.
4. Make panel estimates and undertake diagnostic testing.
5. Critically analyse and evaluate the results of studies that have been undertaken by others using econometric techniques.
6. Engage in collaborative processes (formal or informal group works) to complete econometric modelling inquiry.
7. Apply econometric tools for empirical research.
8. Identify the strengths and weakness of the tools of econometric analysis.
9. Analyse sample data using econometric software.

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 appropriate models and make correct interpretations. It is also expected that students will spend additional hours on reading, problem solving and econometric estimation each week. The computer laboratory classes will complement the lecture material by giving students the opportunity to perform a wide variety of econometric computations using a real-life economic data set. Laboratory exercises will lead up to an overall evaluation of both a particular model and choice of estimation method. Students are required, firstly, to consider what each exercise requires prior to attendance at the laboratory class and, secondly, to reflect afterwards for the tutorial on the results obtained.

Assessment Approach:

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

Before mid-semester examination, students will complete an assignment to assess the understanding of the simultaneous equation model/binary regression model. Each assignment should have a maximum limit of 400 words.

• 1%       Description of the model
• 1%       Clearly identifying merits and limitation of the model
• 3%       Selection of appropriate procedure to handle the model
• 3%       Present result
• 2%       Provide clear interpretation

B. Class Tests (2): Portion of Final Marks: 20%

Two written tests will be conducted that will comprise 45 min duration, 10% each, before and after the midterm examination. Students will be tested on their ability to make correct interpretations of panel or time series data / identify strength and weakness of any analysis of a given econometrics problem. Each test will consist of 5 questions.

C. Practical exercises (2): Portion of Final Mark: 20%

Two practical exercises (laboratory work) will be conducted-one before and another after midterm exams, worth 10% each. These will involve working on raw data using Stata, and application of a chosen econometrics model for analysis.

• 1%       Loading data in Stata
• 1%       Creation of project directory
• 4%       Run econometric models along with required diagnostic tests
• 2%       Convert Stata output into presentation format
• 2%       Clear interpretation

D. Midterm Examination: Portion of Final Mark: 20%

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

Pre-requisites: QME103 Introductory Econometrics

Subject matter:

(Note: abstract calculation is not required and focus should be on effective use of software and interpretation of results)

1. Simultaneous Equations Estimation
   • Inconsistency of OLS
   • Use of instrumental variables
   • Exact identification, under-identification and over-identification
   • Two-stage least squares (TSLS)
   • Order condition for identification
   • Application of the Durbin–Wu–Hausman test
2. Binary Choice Models
   • Logit, Probit and Tobit models
   • Selection bias model
   • Maximum likelihood estimation of the population mean and variance of a random variable
   • Maximum likelihood estimation of regression coefficients
   • Likelihood ratio tests
3. Time Series
   • Static demand functions fitted using aggregate time series data
   • Lagged variables
   • Autoregressive distributed lag (ADL) models
   • Error correction model
   • Use of simulation to investigate the finite sample properties of parameter estimators for the ADL(1,0) model
   • Use of predetermined variables as instruments in simultaneous equations models using time series data
4. Autocorrelation
   • Autocorrelation; Breusch–Godfrey Lagrange multiplier
   • Durbin–Watson d, and Durbin h tests for autocorrelation
   • AR(1) nonlinear regression
   • Cochrane–Orcutt iterative process
   • Autocorrelation with a lagged dependent variable
   • Common factor test and implications for model selection
5. Panel Data Models
   • Pooled OLS model
   • Within-groups fixed effects model
   • First differences fixed-effects model
   • Least squares dummy variable model
   • Random effects model
   • Durbin–Wu–Hausman test

Reading List:

1. Essential Reading
   • Dougherty, C. (2007). Introduction to Econometrics. Oxford University Press.
   • Gujarati, D. N. & Porter, D. C. (2009). Essentials of Econometrics. McGraw Hill.
2. Additional Reading
   • Kmenta, J. (2008). Elements of Econometrics. Indian Reprint, Khosla Publishing House.

Date: January 15, 2016