# Econometrics Tutor Online in New York, NY – Tutors from NYU Columbia Yale

## Private 1-on-1 online econometrics lessons in New York, NY.

Econometrics Tutor online from New York for NYU, Columbia, Princeton, Yale, UPenn

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

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