The proposed course on Econometrics is based on the template of the CBCS Curriculum of the University Grants Commission of India. It is a four credit course and can be taken up by undergraduate students of any discipline under the CBCS Curriculum with the requisite mathematical background at the intermediate level.The course will consist of 40 modules, spread over fifteen weeks. It will give a thorough exposition of the fundamental concepts, tools and techniques that are used in measurement of economic relationships.Any student with knowledge of mathematics at the intermediate level and keen on quantitative research in economics, social sciences and related areas will find the course to be very useful..This is a self-learning mode of study, Students will have to devote time for self-study of each module and will have to adhere to timelines set by the resource persons in respect of submitting assignments and other material for evaluation. The self-study will include studying the reference material mentioned by the resource persons.In order to be eligible for Certificate of Achievement, the student will have to take a proctored examination at the nearest center where the examination would be held.
Econometrics
CEC and St. Xavier's College (Autonomous), Kolkata via Swayam
This course may be unavailable.
Overview
Syllabus
Week 1
MOD-1 Normal distribution
MOD-2 Elements of Sampling Distribution, chi-square distribution and its uses.
Week 2
MOD 3 t and F-distributions and their uses MOD 4 Introduction to Statistical Inference – Concepts of Point Estimation, Interval Estimation and Hypothesis testing. Week 3
MOD 5 Criteria of a good estimator MOD 6 Testing of hypotheses – different notions and concepts. Week 4
MOD 7 Numerical Illustrations based on different notions and concepts of hypothesis testing. MOD 8 Tests concerning Mean and Variance of a univariate normal population. Week 5
MOD 9 Tests concerning Mean and Variance of two univariate normal populations. MOD 10 Numerical illustrations based on the tests for a univariate and two univariate normal populations. Week 6
MOD 11 Estimation of model by method of ordinary least squares MOD 12 Properties of estimators MOD 13 Goodness of fit
Week 7
MOD 14 Confidence intervals MOD 15 Tests of hypotheses – Part-I MOD 16 Tests of hypotheses – Part-II
Week 8
MOD 17 Scaling and units of measurement MOD 18 Gauss-Markov theorem MOD 19 Forecasting– Part-I
Week 9
MOD 20 Forecasting– Part-II MOD 21 Estimation of parameters: Part-I MOD 22 Estimation of parameters: Part-II
Week 10
MOD 23 Properties of OLS estimators: Part-I MOD 24 Properties of OLS estimators: Part-II MOD 25 Goodness of fit - R2 and adjusted R2
Week 11
MOD 26 Partial regression coefficients: Part-I MOD 27 Partial regression coefficients: Part-II MOD 28 Testing hypotheses – individual
Week 12
MOD 29 Testing hypotheses – Joint MOD 30 Functional forms of regression models: Part-I MOD 31 Functional forms of regression models: Part-II
Week 13
MOD 32 Qualitative (dummy) independent variables: Part-I MOD 33 Qualitative (dummy) independent variables: Part-II MOD 34 Multicollinearity
Week 14
MOD 35 Heteroscedasticity: Part-I MOD 36 Heteroscedasticity: Part-II MOD 37 Serial correlation: Part-I
Week 15
MOD 38 Serial correlation: Part-Ii MOD 39 Omission of a relevant variable; inclusion of irrelevant variable MOD 40 Tests of specification errors
MOD 3 t and F-distributions and their uses MOD 4 Introduction to Statistical Inference – Concepts of Point Estimation, Interval Estimation and Hypothesis testing. Week 3
MOD 5 Criteria of a good estimator MOD 6 Testing of hypotheses – different notions and concepts. Week 4
MOD 7 Numerical Illustrations based on different notions and concepts of hypothesis testing. MOD 8 Tests concerning Mean and Variance of a univariate normal population. Week 5
MOD 9 Tests concerning Mean and Variance of two univariate normal populations. MOD 10 Numerical illustrations based on the tests for a univariate and two univariate normal populations. Week 6
MOD 11 Estimation of model by method of ordinary least squares MOD 12 Properties of estimators MOD 13 Goodness of fit
Week 7
MOD 14 Confidence intervals MOD 15 Tests of hypotheses – Part-I MOD 16 Tests of hypotheses – Part-II
Week 8
MOD 17 Scaling and units of measurement MOD 18 Gauss-Markov theorem MOD 19 Forecasting– Part-I
Week 9
MOD 20 Forecasting– Part-II MOD 21 Estimation of parameters: Part-I MOD 22 Estimation of parameters: Part-II
Week 10
MOD 23 Properties of OLS estimators: Part-I MOD 24 Properties of OLS estimators: Part-II MOD 25 Goodness of fit - R2 and adjusted R2
Week 11
MOD 26 Partial regression coefficients: Part-I MOD 27 Partial regression coefficients: Part-II MOD 28 Testing hypotheses – individual
Week 12
MOD 29 Testing hypotheses – Joint MOD 30 Functional forms of regression models: Part-I MOD 31 Functional forms of regression models: Part-II
Week 13
MOD 32 Qualitative (dummy) independent variables: Part-I MOD 33 Qualitative (dummy) independent variables: Part-II MOD 34 Multicollinearity
Week 14
MOD 35 Heteroscedasticity: Part-I MOD 36 Heteroscedasticity: Part-II MOD 37 Serial correlation: Part-I
Week 15
MOD 38 Serial correlation: Part-Ii MOD 39 Omission of a relevant variable; inclusion of irrelevant variable MOD 40 Tests of specification errors
Taught by
Dr. Partha Pratim Ghosh
