NOC:Applied Econometrics

Lecture 1 - Overview of Module 1 and Introduction of Causality

Lecture 2 - Correlation and Causality

Lecture 3 - Correlation and Causality (Continued...)

Lecture 4 - Correlation and Causality (Continued...)

Lecture 5 - Probability Theory

Lecture 6 - Probability Theory (Continued...)

Lecture 7 - Probability Theory (Continued...)

Lecture 8 - Probability Theory (Continued...)

Lecture 9 - Posterior Probability

Lecture 10 - Bayesian Theorem

Lecture 11 - Bayesian Theorem (Continued...): Repeated Trial

Lecture 12 - Bayesian Theorem (Continued...): Example of Diamond Identification

Lecture 13 - Probability Distribution

Lecture 14 - Double Structure of Variable

Lecture 15 - Probability Distribution (Discrete/Continuous Variable) Random Variable

Lecture 16 - Probability Mass Function (PMF) Probability Density Function (PDF)

Lecture 17 - Expectation, Variance, Covariance

Lecture 18 - Expectation, Variance, Covariance (Continued...)

Lecture 19 - Covariance Rule

Lecture 20 - Bernoulli Distribution

Lecture 21 - Bernoulli Distribution (Continued...)

Lecture 22 - Normal Approximation of Bernoulli Distribution

Lecture 23 - Sampling

Lecture 24 - Sampling (Continued...)

Lecture 25 - Central Limit Theorem

Lecture 26 - Law of Large Numbers LLN

Lecture 27 - Properties of Estimator

Lecture 28 - Conflict Between Unbiasedness and Min Variance

Lecture 29 - T-Distribution

Lecture 30 - Normal Distribution

Lecture 31 - Normal Distribution (Continued...)

Lecture 32 - Hypothesis Testing

Lecture 33 - Decision Rules

Lecture 34 - Level of Significance

Lecture 35 - P Value

Lecture 36 - Power of a Test

Lecture 37 - Confidence Interval

Lecture 38 - Confidence Interval Example

Lecture 39 - Properties of Power of a Test

Lecture 40 - Introduction to Module II

Lecture 41 - Error Term, Coefficient of Determination, Regression Coefficient

Lecture 42 - Error Term, Coefficient of Determination, Regression Coefficient (Continued...)

Lecture 43 - Error Term, Coefficient of Determination, Regression Coefficient (Continued...)

Lecture 44 - Definition : Variable, Parameter and Coefficient

Lecture 45 - Introduction to Regression: Recapitulating Correlation and Causal Thinking

Lecture 46 - Adjusted R-Squared

Lecture 47 - Degrees of Freedom

Lecture 48 - Multiple Regression

Lecture 49 - Multiple Regression (Continued...)

Lecture 50 - Regression Table

Lecture 51 - Regression Table (Continued...)

Lecture 52 - Multicollinearity

Lecture 53 - Multicollinearity (Continued...)

Lecture 54 - Multicollinearity (Continued...)

Lecture 55 - Multicollinearity (Continued...)

Lecture 56 - Multicollinearity (Continued...)

Lecture 57 - Dummy Variable

Lecture 58 - Dummy variable (Continued...)

Lecture 59 - Dummy variable (Continued...)

Lecture 60 - Dummy variable (Continued...)

Lecture 61 - Dummy variable (Continued...)

Lecture 62 - Dummy variable (Continued...)

Lecture 63 - Dummy variable (Continued...)

Lecture 64 - Heteroscedasticity

Lecture 65 - Heteroscedasticity (Continued...)

Lecture 66 - Heteroscedasticity (Continued...)

Lecture 67 - Heteroscedasticity (Continued...)

Lecture 68 - Heteroscedasticity (Continued...)

Lecture 69 - Heteroscedasticity (Continued...)

Lecture 70 - Autocorrelation

Lecture 71 - Autocorrelation (Continued...)

Lecture 72 - Autocorrelation (Continued...)

Lecture 73 - Autocorrelation (Continued...)

Lecture 74 - Autocorrelation (Continued...)

Lecture 75 - Autocorrelation (Continued...)

Lecture 76 - Autocorrelation (Continued...)

Lecture 77 - Autocorrelation (Continued...)

Lecture 78 - Autocorrelation (Continued...)

Lecture 79 - Autocorrelation (Continued...)

Lecture 80 - Autocorrelation (Continued...)

Lecture 81 - Autocorrelation (Continued...)

Lecture 82 - Remedy for Autocorrelation

Lecture 83 - Model Specification

Lecture 84 - Model Specification (Continued...)

Lecture 85 - Model Specification (Continued...)

Lecture 86 - Model Specification (Continued...)

Lecture 87 - Model Specification (Continued...)

Lecture 88 - Model Specification (Continued...)

Lecture 89 - Model Specification (Continued...)

Lecture 90 - Model Specification (Continued...)

Lecture 91 - Continuation with Proxy Variable

Lecture 92 - Ramsey Reset Test

Lecture 93 - Introduction to Module III

Lecture 94 - Non Stochastic Regressor

Lecture 95 - Stochastic Regressor

Lecture 96 - Assumptions for Regression Models with Non-Stochastic Regressor

Lecture 97 - Assumptions for Regression Model with Stochastic Regressor

Lecture 98 - Instrumental Variable

Lecture 99 - Instrumental Variable (Continued...)

Lecture 100 - Asymptotic Property

Lecture 101 - Problem of Endogeneity

Lecture 102 - Simultaneous Equation Model

Lecture 103 - Instrumental Variable for Endogeneity Bias Problem

Lecture 104 - Good Bad and Weak Instrumental Variable

Lecture 105 - Overidentification Underidentification Exact Identification - Instrumental Variable

Lecture 106 - Two Stage Least Square and Instrumental Variable

Lecture 107 - 2SLS and IV with Stata