NOC:Introduction to Econometrics

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130106001



Media Storage Type : 32 GB USB Stick

NPTEL Subject Matter Expert : Prof. Sabuj Kumar Mandal

NPTEL Co-ordinating Institute : IIT Madras

NPTEL Lecture Count : 65

NPTEL Course Size : 5.5 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)


Lecture Titles:

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

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