Introduction to Econometrics
The course of "Introduction to Econometrics" was covered in Spring 2016 semester.
Following are the notes from the lectures with the main lecture topics written with "#"
Following are the notes from the lectures with the main lecture topics written with "#"
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# Hypothesis Testing
# Population Regression Function # Components of the OLS # Derivation of the OLS | ||
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# Expected Value of the OLS Coefficients
# Gauss-Markov Theorem (with proof) | ||
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# Advantages/Disadvantages of the OLS
# Interpreting 'log' and 'linear' models # Regression without intercept | ||
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# Multiple Regression Model
# Multicolinearity # Solutions to Multicolinearity # Omitted Variable Bias | ||
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# Omitted Variable Bias
# Standard Errors # Adjusted R-Squared | ||
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# Omitted Variable Bias
# Multiple Regression | ||
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# Limits of R-Squared & Adj. R-Squared
# Reporting Regression Results | ||
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# Hedonic Regression
# Standardised Beta Coefficients # Residual Analysis | ||
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# Chow Test
# Difference-in-Difference # Interaction b/w Dummy Variables | ||
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# Binary Dependent Variable
# Linear Probability Model # Odd Ratio # Predicted Probability | ||
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# Logit Model
# Lagrange Multiplier (L. M.) Test # B. Pagan Test # White Test # Goldfield-Quandt Test # Feasible Generalised Least Squares | ||
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# Heteroskedasticity
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# Linear Probability Model
# Functional Form Misspecification # RESET Test | ||
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# Measurement Error
# Instrumental Variable # Problem Endogeneity | ||
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# Endogeneity
# IV Estimator # Two-Stage Least Squares # Weak Instruments # Multiple Endogenous Variables | ||
| Full Course Summary |
