Indira Gandhi Institute of Development Research
Econometrics – II
Semester: / January – May, 2014Instructor: / Dr. A. Ganesh-Kumar
Teaching assistant: / Ms. Tirtha Chatterjee & Ms. Runu Bhakta
Class hours: / 9:30 a.m. – 11:00 a.m., Monday & Tuesday
Office hours: / By appointment
Important dates:
i. Mid-term examination: / 3rd March, 2014
ii. Computer assignment – problem fixation: / On or before 7th April, 2014
iii. Computer assignment – submission: / On or before 2nd May, 2014
iv. Computer assignment – presentation: / On or before 9th May, 2014
v. Final examination: / To be announced by SOFFICE
Course assumptions
This course assumes you have a good understanding of the topics listed below. It is up to you to satisfy these assumptions.
· Classical linear regression model (CLRM), its assumptions, the least squares estimator, properties of the estimator, violations of the assumptions of the CLRM (refresh your Econometric-1 course)
· Statistical prerequisites of the CLRM
· Matrix algebra
Course grading
· Grading for the course will be based on one mid-term examination (30%), one computer assignment (30%), class-room performance (10%) and a final examination (30%). Weights in the overall course grade are shown in parentheses.
· The mid-term exam will cover Topics 1 (panel data) and 2 (seemingly unrelated regressions) below. No retake will be allowed under any circumstance. If a student is unable to appear for the exam for a valid reason (to be decided by the institute), then the corresponding weight will be transferred to the final exam.
· The final exam will cover the entire course material. Institute rules regarding appearance in final exams apply.
Course Outline
1. Panel Data
1.1. Introduction
1.1.1 Advantages of panel data
1.1.2 Issues involved in utilizing panel data
1.2. Fixed effects – Dummy variable models
1.2.1 Models with intercepts that vary over individuals
1.2.2 Models with intercepts that vary over individuals and time
1.3. Random effects – Error component models
1.3.1 Models with intercepts that vary over individuals
1.3.2 Models with intercepts that vary over individuals and time
1.4. Choosing between fixed and random effects models
2. Seemingly Unrelated Regressions (SUR)
2.1. SUR with contemporaneously correlated disturbances
2.1.1 Specification and interpretation
2.1.2 Estimation – The simple case
2.1.3 Estimation – The general case
Pure GLS estimator
OLS estimator
FGLS estimator
Iterated FGLS estimator
2.1.4 Inference and testing
Testing for structural change
Testing for equality of behaviour
Testing for aggregation bias
2.2. SUR with unequal number of observations
2.3. SUR with first-order autoregressive disturbances
3. Simultaneous Equations Models
3.1. Specification and interpretation
3.1.1 The structural form
3.1.2 The reduced form
3.2. Identification by reduced form method
3.3. Identification by structural form method
3.4. Estimation of the complete structural model
3.4.1 Indirect least squares, two stage least squares (2SLS), instrumental variables (IV) method
3.4.2 Three stage least squares (3SLS)
3.4.3 Limited information maximum likelihood
3.4.4 Full information maximum likelihood
3.4.5 Properties of the estimates
4. Qualitative and Limited Dependent Variables Models
4.1. Binary choice models
4.1.1 Linear probability models
4.1.2 Probit and Logit models
4.2. Polychotomous choice models
4.2.1 Unordered response models
4.2.2 Ordered response models
4.2.3 Sequential response models
4.3. Limited dependent variables models
4.3.1 Censored (Tobit) regression models
4.3.2 Truncated regression models
4.3.3 Mixture of truncated and censored regression models
4.4. Switching regressions and sample selectivity models
4.5. Count data models
Course books (use latest edition as far as possible – earlier editions are fine too)
Greene W. H. “Econometric Analysis, 5th Ed.”, Prentice Hall, New Jersey.
J. Johnston and J. Dinardo. “Econometric Methods”, McGraw-Hill Book Company, New York.
Maddala G. S. “Limited Dependent and Qualitative Variables in Econometrics”, Cambridge University Press, Cambridge.
More books
Baltagi, B. H. “Econometric Analysis of Panel Data”, Chichester, John Wiley, New York.
Cameron, A. C. and P. K. Trivedi. Microeconometrics: Methods and Applications, Cambridge University Press, Cambridge.
Cameron, A. C. and P. K. Trivedi. Microeconometrics using STATA.
Hsiao C. “Analysis of Panel Data”, Cambridge University Press, Cambridge.
Judge G. G, Griffiths W. E., Hill R. C., Lutkepohl H. and Lee T-C. “The Theory and Practice of Econometrics, 2nd Ed.”, John Wiley, New York.
Kmenta, J. “Elements of Econometrics”, 2nd Ed., Maxwell Macmillan, New York.
Maddala G. S. “Econometrics of Panel Data, Vols. I and II”, Edward Elgar, England.
Srivastava V. and Giles A. E. D. “Seemingly Unrelated Regression Equations Models: Estimation and Inference”, Marcel Dekker, New York.
Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data. MIT Press.
Wooldridge, J. M. Econometrics, Cengage Learning.
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