Course110105153107 lectures
Applied Econometrics
Prof. Tutan Ahmed · IIT Kharagpur
About
Unique value added by this present course is that it will teach the students from data collection to conclusion of a study using econometric techniques. Second, it will delve deep into experiments in the field of Economics. Experimental Economics is a thriving area in the field of Economics.
INTENDED AUDIENCE : Management, Humanities, Economics, Industrial Engineering
PREREQUISITES : Higher Secondary Level Mathematics Knowledge
INDUSTRY SUPPORT : Banking, Policy making/ Development Organization, Consulting Firms
Lectures
Week 01
- Lecture 01: Overview of Module 01 & Introduction of Causality
- Lecture 02: Correlation and Causality
- Lecture 03: Correlation and Causality (Contd.)
- Lecture 04: Correlation and Causality (Contd.)
- Lecture 05: Probability Theory
- Lecture 06: Probability Theory (Contd.)
- Lecture 07: Probability Theory (Contd.)
- Lecture 08: Probability Theory (Contd.)
- Lecture 09: Posterior Probability
- Lecture 10: Bayesian Theorem
- Lecture 11 : Bayesian Theorem (Contd.): Repeated Trial
- Lecture 12 : Bayesian Theorem (Contd.): Example of Diamond Identification
Week 02
- 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 (Contd.)
- Lecture 19: Covariance Rule
- Lecture 20: Bernoulli Distribution
- Lecture 21: Bernoulli Distribution (Contd.)
- Lecture 22: Normal Approximation of Bernoulli Distribution
Week 03
- Lecture 23 : Sampling
- Lecture 24 : Sampling (Contd.)
- 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 (Contd.)
Week 04
- Lecture 32: Hypothesis Testing
- Lecture 33: Decision Rules
- Lecture 34: Level of Significance
- Lecture 35: P Value
- Lecture 36: Power of a Test
Week 05 :
- 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 (Contd.)
- Lecture 43: Error Term, Coefficient of Determination, Regression Coefficient (Contd.)
- 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
Week 06
- Lecture 48: Multiple Regression
- Lecture 49: Multiple Regression (Contd.)
- Lecture 50: Regression Table
- Lecture 51: Regression Table (Contd.)
Week 07
- Lecture 52: Multicollinearity
- Lecture 53: Multicollinearity (Contd.)
- Lecture 54: Multicollinearity (Contd.)
- Lecture 55: Multicollinearity (Contd.)
- Lecture 56: Multicollinearity (Contd.)
- Lecture 57: Dummy Variable
- Lecture 58: Dummy variable (Contd.)
- Lecture 59: Dummy variable (Contd.)
Week 08
- Lecture 60 : Dummy variable (Contd.)
- Lecture 61 : Dummy variable (Contd.)
- Lecture 62 : Dummy variable (Contd.)
- Lecture 63 : Dummy variable (Contd.)
- Lecture 64 : Heteroscedasticity
- Lecture 65 : Heteroscedasticity (Contd.)
- Lecture 66 : Heteroscedasticity (Contd.)
- Lecture 67 : Heteroscedasticity (Contd.)
- Lecture 68 : Heteroscedasticity (Contd.)
- Lecture 69 : Heteroscedasticity (Contd.)
Week 09
- Lecture 70: Autocorrelation
- Lecture 71: Autocorrelation (Contd.)
- Lecture 73: Autocorrelation (Contd.)
- Lecture 72: Autocorrelation (Contd.)
- Lecture 75: Autocorrelation (Contd.)
- Lecture 77: Autocorrelation (Contd.)
- Lecture 76: Autocorrelation (Contd.)
- Lecture 78: Autocorrelation (Contd.)
- Lecture 79: Autocorrelation (Contd.)
- Lecture 80: Autocorrelation (Contd.)
- Lecture 81: Autocorrelation (Contd.)
- Lecture 74: Autocorrelation (Contd.)
Week 10
- Lecture 82 : Remedy for Autocorrelation
- Lecture 83 : Model Specification
- Lecture 84 : Model Specification (Contd.)
- Lecture 85 : Model Specification (Contd.)
- Lecture 86 : Model Specification (Contd.)
- Lecture 87 : Model Specification (Contd.)
- Lecture 88 : Model Specification (Contd.)
- Lecture 89 : Model Specification (Contd.)
- Lecture 90 : Model Specification (Contd.)
- Lecture 91 : Continuation with Proxy Variable
- Lecture 92 : Ramsey Reset Test
Week 11
- 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 (Contd.)
Week 12
- 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
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Kharagpur
- Department
- Management
- Teacher
- Prof. Tutan Ahmed
- Level
- Graduate
- Language
- English
- Subjects
- Business and management
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed
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