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Our tutors will prepare you for a complete course in econometrics.

 

Probability and Stats Review for Econometrics

• Understanding mean, variance, standard deviation, expected value
• Understanding of hypothesis tests: setting up null vs alternate
• Understanding Z-score, t-tests, and p-value
• Application of alpha level and confidence intervals
• Understanding of Chi squared distribution and F-distribution

 

Regression Analysis

• Simple Regression Model
• Nature of data and the need for best fit line/regression line
• Terminology and notation
• Concept of population regression function
• Introduction to the idea of residuals and residual sum of squares (RSS)
• Least Squares Simple Regression and minimizing RSS
• Conceptual understanding and interpretation of regression slope and intercept coefficients
• Gauss Markov properties of Best Linear Unbiased Estimator
• Coefficient of Determination and R squared

 

Classical Normal Linear Regression Assumptions

• Probability distribution of the disturbance term
• Normality of residuals
• Zero covariance between residuals and explanatory variables
• Assumption of constant variance of residuals or homoscedasticity

 

Classical Normal Linear Regression Assumptions

• Probability distribution of the disturbance term
• Normality of residuals
• Zero covariance between residuals and explanatory variables
• Assumption of constant variance of residuals or homoscedasticity

 

Regression Diagnostics

• Checking the goodness of fit or R-squared
• F-test as the joint hypothesis test for the regression model
• Individual t-tests or hypothesis tests for statistical significance of coefficients
• Normality of residuals
• Checking for heteroscedasticity

 

Multiple linear regression

• Meaning and interpretation of partial regression coefficients
• R squared meaning and interpretation with multiple explanatory variables
• Issue of multicollinearity
• Variance of regression coefficients and the impact on statistical significance

 

Dummy Variables

• Nature of binary/dummy variables
• Dummy variable trap
• Use of dummy variables in Chow test for structural change
• Piecewise linear regression with dummy variables
• Interaction variables and intercept dummies

 

Heteroskedasticity

• Issue of heteroscedasticity
• OLS remains unbiased but the variance is no longer the minimum variance
• Detection of heteroscedasticity with graphical method using scatterplots as well as formal tests
  1. Park’s Test
  2. Glesjer test
  3. Breusch-Pagan hettest
  4. White’s test
• Fixing for heteroscedasticity using weighted least squares or using White’s robust standard errors

 

Autocorrelation

• Issue of autocorrelation in residuals
• OLS is still unbiased but minimum is no longer minimum variance
• Graphical detection through scatterplot of residuals
• Runs test
• Durbin Watson Test and its limitations
• Breusch-Godfrey test for autocorrelation
• Lagrange multiplier test
• Newey west standard errors to fix the issue of autocorrelation

 

Binary Dependent Variable models

• Logit
  Probit
• Panel Data
• Instrumental Variables
• Time Series Data

 

Financial Econometrics

• Intermediate Microeconomics tutors
• Intermediate Macroeconomics tutors
• Econometrics Tutors
• Economics for business tutors
• Economics for MBA tutors
• Math for Economics I tutors
• Math for Economists II tutors
• Urban Economics tutors
• Public Economics tutors
• Statistics Tutors
• Finance Tutors 
• Reviews

 

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