This course provides a comprehensive introduction to basic econometric concepts and techniques. It covers statistical concepts of hypothesis testing, estimation and diagnostic testing of simple and multiple regression models. The course also covers the violations of the assumptions of OLS, the consequences of and tests for misspecification of regression models along with errors in variables. The specific objectives of the course are:• To analyse the nature and scope of Econometrics• To define hypothesis and process of hypothesis testing.• To define the implications of the assumptions of OLS• To discuss the violations of assumptions.• To discuss specification bias and errors in variables
Overview
Syllabus
Week 1:
Module 1: Nature and Scope of Econometrics Module 2: Models, Aims and Methodology of Econometrics Module 3: Basic Statistical Concepts Module 4: Estimate and Estimator, Point vs. Interval Estimation
Week 2:
Module 5: Properties of Estimators
Module 6: Probability Distributions
Module 7: Uses of Probability Distributions in Econometrics
Week 3:
Module 8: Hypothesis I
Module 9: Hypothesis II
Module 10: Type I and Type II Errors
Week 4:
Module 11: Power of a Test
Module 12: Tests for Comparing Parameters from two Samples
Week 5:
Module 13: Simple Linear Regression Model
Module 14: Stochastic Specification
Week 6:
Module 15: Ordinary Least Squares
Module 16: BLUE, The Gauss Markov Theorem, Goodness of fit
Module 17: k-variable linear regression model, Dummy variable
Week 7:
Module 18: Violations of Classical Assumptions
Module 19: Heteroscedasticity, Problem and Consequences
Week 8:
Module 20: Heteroscedasticity- Detection, Alternative Methods of Estimation.
Module 21: Autocorrelation, Sources and Consequences
Module 22: Tests of Autocorrelation
Week 9:
Module 23: Autocorrelation-Remedial Measures
Module 24: Multicollinearity-Problem and Consequences
Module 25: Detection of multicollinearity
Week 10:
Module 26: Multicollinearity- Remedial Measures
Module 27: Multicollinearity- Remedial Measures (contd.)
Module 28: Specification bias
Week 11:
Module 29: Omission of Relevant Variables
Module 30: Inclusion of Irrelevant Variables
Module 31: Test for Specification Bias
Week 12:
Module 32: Errors in Variable
Module 1: Nature and Scope of Econometrics Module 2: Models, Aims and Methodology of Econometrics Module 3: Basic Statistical Concepts Module 4: Estimate and Estimator, Point vs. Interval Estimation
Week 2:
Module 5: Properties of Estimators
Module 6: Probability Distributions
Module 7: Uses of Probability Distributions in Econometrics
Week 3:
Module 8: Hypothesis I
Module 9: Hypothesis II
Module 10: Type I and Type II Errors
Week 4:
Module 11: Power of a Test
Module 12: Tests for Comparing Parameters from two Samples
Week 5:
Module 13: Simple Linear Regression Model
Module 14: Stochastic Specification
Week 6:
Module 15: Ordinary Least Squares
Module 16: BLUE, The Gauss Markov Theorem, Goodness of fit
Module 17: k-variable linear regression model, Dummy variable
Week 7:
Module 18: Violations of Classical Assumptions
Module 19: Heteroscedasticity, Problem and Consequences
Week 8:
Module 20: Heteroscedasticity- Detection, Alternative Methods of Estimation.
Module 21: Autocorrelation, Sources and Consequences
Module 22: Tests of Autocorrelation
Week 9:
Module 23: Autocorrelation-Remedial Measures
Module 24: Multicollinearity-Problem and Consequences
Module 25: Detection of multicollinearity
Week 10:
Module 26: Multicollinearity- Remedial Measures
Module 27: Multicollinearity- Remedial Measures (contd.)
Module 28: Specification bias
Week 11:
Module 29: Omission of Relevant Variables
Module 30: Inclusion of Irrelevant Variables
Module 31: Test for Specification Bias
Week 12:
Module 32: Errors in Variable
Taught by
Prof. Deb Kumar Chakraborty
