Coming soon
Econometrics
This course is meant for training the students in econometric methods and their applications. This course would enable the students in understanding the economic phenomena through statistical tools and economics principles.
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Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies
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Paper Code |
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STT421 |
Econometrics (Theory)
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The students will be able to –
CO82: Construct, test, and analyse econometric models, using variables and relationships commonly found in studies of economic theory; CO83: Collect, organise, and analyse economic data, and interpret results from statistical analyses. CO84: Identify key classical assumptions in the field of econometrics, explain their significance, and describe the effects that violations of the classical assumptions can have; CO85: Use the least squares method in evaluating the relationship of one explanatory variable to the dependent variable and the relationships of multiple explanatory variable to the dependent variable CO86: Remove the problems of econometrics such as heteroscedasticity, auto correlation, multicollineraity. CO87: be a qualified user of econometric methods. CO88: be able to make use of econometric models in your own academic work |
Approach in teaching: Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students: Assignments Seminar Presentation Subject based Activities
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Classroom Quiz Assignments Class Test Individual Presentation |
Representation of Economic phenomenon, relationship among economic variables, the general linear model and its extensions, basic assumptions, Ordinary least squares estimation and prediction, generalized least square estimation and prediction
Heteroscedasticity, Auto-correlation: its consequences and tests (Durbin Watson test), Multicollinearity: problem, its implications and tools for handling the problem
Linear regression and stochastic regression, instrumental variable estimation, autoregressive linear model, lagged variables, Distributed Lag models: Koyck’s Geometric Lag model
Simultaneous equation model: Basic rationale, Consequences of simultaneous relations, Identification problem, Conditions of Identification, Indirect Least Squares, Two-stage least squares, K-class estimators, Limited Information and Full Information Maximum Likelihood Methods
Three stage least squares, Generalized least squares, Recursive models, SURE Models. Mixed Estimation Methods, use of instrumental variables, pooling of cross-section and time series data, Principal Component Methods.
- Croxton, F.E. & Cowden, DJ. (1979): Applied General Statistics, Prentice Hall of India.
- Johnston, J. (1984): Econometric Methods. McGraw Hill.
- Judge, G.C., Hill, R.C., Griffiths, W.E., Lutkepohl, H. & Lee, T.C. (1988): Introduction to the Theory and Practice of Econometrics, 2nd Ed. John Wiley.
- Kmenta, J. (1986): Elements of Econometrics, 2nd Ed. University of Michigan Press.
- Koop, G. (2007): Introduction to Econometrics, John Wiley.
- Maddala, G.S. (2017): Introduction to Econometrics, 3rd Ed. John Wiley.
- Pindyck, R.S. & Rubinfeld, D.L. (1998): Econometric Models and Economic Forecasts, 4th Ed. McGraw Hill.
- Verbeek, M. (2008): A Guide to Modern Econometrics, 3rd Ed. John Wiley.
- Judge, G.C., Hill, R,C. Griffiths, W.E., Lutkepohl, H. and Lee, T-C. (1988): Introduction to the Theory and Practice of Econometrics, Second Edition, John Wiley & Sons.
- Kendall, M.G. and Stuart, A. (1968): The Advanced Theory of Statistics (Vol. III), Second Edition, Charles Griffin.
