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.
Students will able to
Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies
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Paper Code |
Paper Title |
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STT-421 |
Econometrics
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CO 84: Construct, test, and analyse and interpretate econometric models, using variables and relationships commonly found in studies of economic theory;
CO 85: Identify key classical assumptions in the field of econometrics, explain their significance, and describe the effects that violations of the classical assumptions can have.
CO 86: 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
CO 87: Remove the problems of econometrics such as heteroscedasticity, auto correlation, multicollineraity.
CO 88: 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.