COURSE OUTLINE: As the name suggests, the subject econometrics aims to measure economic relationships. Using economic data and applying mathematical and statistical tools, it provides empirical validity of the abstract economic theory. However, the application of econometrics is not confined in the domain of economics, rather widespread application of econometrics is possible in other social science and pure science domains also. After successful completion of the course, students would be able to formulate econometric model to analyze data and then would be able to establish any cause-effect relationship in their preferred areas of interest like economics, finance, management, engineering and science. Expertise in econometrics increases the job prospect of the students significantly.
Overview
Syllabus
Introduction to Econometrics.
Introduction to econometrics and econometric analysis Part - 1.
Introduction to econometrics and econometric analysis Part - 2.
Different steps in econometric analysis Part - 1.
Different steps in econometric analysis Part - 2.
Desirable properties of the estimates of the population parameters Part - 1.
Desirable properties of the estimates of the population parameters Part - 2.
Classical Linear Regression Model Part - 1.
Classical Linear Regression Model Part - 2.
Classical Linear Regression Model Part - 3.
Classical Linear Regression Model Part - 4.
Classical Linear Regression Model Part - 5.
Goodness of fit measure, Anova and hypothesis testing Part - 1.
Goodness of fit measure, Anova and hypothesis testing Part - 2.
Goodness of fit measure, Anova and hypothesis testing Part - 3.
Goodness of fit measure, Anova and hypothesis testing Part - 4.
Goodness of fit measure, Anova and hypothesis testing Part - 5.
Application of STATA for hypothesis testing and introduction to multiple linear regression model.
Application of STATA for hypothesis testing and introduction to multiple linear regression model.
Application of STATA for hypothesis testing and introduction to multiple linear regression model.
Application of STATA for hypothesis testing and introduction to multiple linear regression model.
Application of STATA for hypothesis testing and introduction to multiple linear regression model.
Multiple linear regression model and application of F statistics Part - 1.
Multiple linear regression model and application of F statistics Part - 2.
Multiple linear regression model and application of F statistics Part - 3.
Multiple linear regression model and application of F statistics Part - 4.
Multiple linear regression model and application of F statistics Part - 5.
Multiple linear regression model and application of F statistics Part - 6.
Structural break analysis using Chow test Part - 1.
Structural break analysis using Chow test Part - 2.
Structural break analysis using Chow test Part - 3.
Structural break analysis using Chow test Part - 4.
Structural break analysis using Chow test Part - 5.
Dummy Variable analysis and Application of Difference-inDifference for impact evaluation Part - 1.
Dummy Variable analysis and Application of Difference-inDifference for impact evaluation Part - 2.
Dummy Variable analysis and Application of Difference-inDifference for impact evaluation Part - 3.
Dummy Variable analysis and Application of Difference-inDifference for impact evaluation Part - 4.
Dummy Variable analysis and Application of Difference-inDifference for impact evaluation Part - 5.
Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations Part - 1.
Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations Part - 2.
Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations Part - 3.
Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations Part - 4.
Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations Part - 5.
Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations Part - 6.
Relaxing the assumptions of CLRM-Multicollinearity and Autocorrelation Part - 1.
Relaxing the assumptions of CLRM-Multicollinearity and Autocorrelation Part - 2.
Relaxing the assumptions of CLRM-Multicollinearity and Autocorrelation Part - 3.
Relaxing the assumptions of CLRM-Multicollinearity and Autocorrelation Part - 4.
Relaxing the assumptions of CLRM-Multicollinearity and Autocorrelation Part - 5.
Relaxing the assumptions of CLRM-Multicollinearity and Autocorrelation Part - 6.
Relaxing the assumptions of CLRM-Autocorrelation and Heteroscedasticity Part - 1.
Relaxing the assumptions of CLRM-Autocorrelation and Heteroscedasticity Part - 2.
Relaxing the assumptions of CLRM-Autocorrelation and Heteroscedasticity Part - 3.
Relaxing the assumptions of CLRM-Autocorrelation and Heteroscedasticity Part - 4.
Relaxing the assumptions of CLRM-Autocorrelation and Heteroscedasticity Part - 5.
Relaxing the assumptions of CLRM-Autocorrelation and Heteroscedasticity Part - 6.
Qualitative Response Models- Linear Probability Model, Logit and Probit Models Part - 1.
Qualitative Response Models- Linear Probability Model, Logit and Probit Models Part - 2.
Qualitative Response Models- Linear Probability Model, Logit and Probit Models Part - 3.
Qualitative Response Models- Linear Probability Model, Logit and Probit Models Part - 4.
Qualitative Response Models- Linear Probability Model, Logit and Probit Models Part - 5.
Qualitative Response Models- Probit and Tobit Models Part - 1.
Qualitative Response Models- Probit and Tobit Models Part - 2.
Qualitative Response Models- Probit and Tobit Models Part - 3.
Qualitative Response Models- Probit and Tobit Models Part - 4.
Qualitative Response Models- Probit and Tobit Models Part - 5.
Taught by
NPTEL-NOC IITM
