Lecture 1 - Introduction to econometrics and econometric analysis - Part 1

Lecture 2 - Introduction to econometrics and econometric analysis - Part 2

Lecture 3 - Different steps in econometric analysis - Part 1

Lecture 4 - Different steps in econometric analysis - Part 2

Lecture 5 - Desirable properties of the estimates of the population parameters - Part 1

Lecture 6 - Desirable properties of the estimates of the population parameters - Part 2

Lecture 7 - Classical Linear Regression Model - Part 1

Lecture 8 - Classical Linear Regression Model - Part 2

Lecture 9 - Classical Linear Regression Model - Part 3

Lecture 10 - Classical Linear Regression Model - Part 4

Lecture 11 - Classical Linear Regression Model - Part 5

Lecture 12 - Goodness of fit measure, Anova and hypothesis testing - Part 1

Lecture 13 - Goodness of fit measure, Anova and hypothesis testing - Part 2

Lecture 14 - Goodness of fit measure, Anova and hypothesis testing - Part 3

Lecture 15 - Goodness of fit measure, Anova and hypothesis testing - Part 4

Lecture 16 - Goodness of fit measure, Anova and hypothesis testing - Part 5

Lecture 17 - Application of STATA for hypothesis testing and introduction to multiple linear regression model - Part 1

Lecture 18 - Application of STATA for hypothesis testing and introduction to multiple linear regression model - Part 2

Lecture 19 - Application of STATA for hypothesis testing and introduction to multiple linear regression model - Part 3

Lecture 20 - Application of STATA for hypothesis testing and introduction to multiple linear regression model - Part 4

Lecture 21 - Application of STATA for hypothesis testing and introduction to multiple linear regression model - Part 5

Lecture 22 - Multiple linear regression model and application of F statistics - Part 1

Lecture 23 - Multiple linear regression model and application of F statistics - Part 2

Lecture 24 - Multiple linear regression model and application of F statistics - Part 3

Lecture 25 - Multiple linear regression model and application of F statistics - Part 4

Lecture 26 - Multiple linear regression model and application of F statistics - Part 5

Lecture 27 - Multiple linear regression model and application of F statistics - Part 6

Lecture 28 - Structural break analysis using Chow test - Part 1

Lecture 29 - Structural break analysis using Chow test - Part 2

Lecture 30 - Structural break analysis using Chow test - Part 3

Lecture 31 - Structural break analysis using Chow test - Part 4

Lecture 32 - Structural break analysis using Chow test - Part 5

Lecture 33 - Dummy Variable analysis and Application of Difference-inDifference for impact evaluation - Part 1

Lecture 34 - Dummy Variable analysis and Application of Difference-inDifference for impact evaluation - Part 2

Lecture 35 - Dummy Variable analysis and Application of Difference-inDifference for impact evaluation - Part 3

Lecture 36 - Dummy Variable analysis and Application of Difference-inDifference for impact evaluation - Part 4

Lecture 37 - Dummy Variable analysis and Application of Difference-inDifference for impact evaluation - Part 5

Lecture 38 - Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations - Part 1

Lecture 39 - Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations - Part 2

Lecture 40 - Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations - Part 3

Lecture 41 - Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations - Part 4

Lecture 42 - Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations - Part 5

Lecture 43 - Statistical analysis of Dummy Variable models and Testing for seasonal fluctuations - Part 6

Lecture 44 - Relaxing the assumptions of CLRM - Multicollinearity and Autocorrelation - Part 1

Lecture 45 - Relaxing the assumptions of CLRM - Multicollinearity and Autocorrelation - Part 2

Lecture 46 - Relaxing the assumptions of CLRM - Multicollinearity and Autocorrelation - Part 3

Lecture 47 - Relaxing the assumptions of CLRM - Multicollinearity and Autocorrelation - Part 4

Lecture 48 - Relaxing the assumptions of CLRM - Multicollinearity and Autocorrelation - Part 5

Lecture 49 - Relaxing the assumptions of CLRM - Multicollinearity and Autocorrelation - Part 6

Lecture 50 - Relaxing the assumptions of CLRM - Autocorrelation and Heteroscedasticity - Part 1

Lecture 51 - Relaxing the assumptions of CLRM - Autocorrelation and Heteroscedasticity - Part 2

Lecture 52 - Relaxing the assumptions of CLRM - Autocorrelation and Heteroscedasticity - Part 3

Lecture 53 - Relaxing the assumptions of CLRM - Autocorrelation and Heteroscedasticity - Part 4

Lecture 54 - Relaxing the assumptions of CLRM - Autocorrelation and Heteroscedasticity - Part 5

Lecture 55 - Relaxing the assumptions of CLRM - Autocorrelation and Heteroscedasticity - Part 6

Lecture 56 - Qualitative Response Models - Linear Probability Model, Logit and Probit Models - Part 1

Lecture 57 - Qualitative Response Models - Linear Probability Model, Logit and Probit Models - Part 2

Lecture 58 - Qualitative Response Models - Linear Probability Model, Logit and Probit Models - Part 3

Lecture 59 - Qualitative Response Models - Linear Probability Model, Logit and Probit Models - Part 4

Lecture 60 - Qualitative Response Models - Linear Probability Model, Logit and Probit Models - Part 5

Lecture 61 - Qualitative Response Models - Probit and Tobit Models - Part 1

Lecture 62 - Qualitative Response Models - Probit and Tobit Models - Part 2

Lecture 63 - Qualitative Response Models - Probit and Tobit Models - Part 3

Lecture 64 - Qualitative Response Models - Probit and Tobit Models - Part 4

Lecture 65 - Qualitative Response Models - Probit and Tobit Models - Part 5