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Heteroscedasticity

Welcome to your Heteroscedasticity

1. What is heteroscedasticity?

a) The condition where the error terms have constant variance

b) The condition where the error terms have increasing or decreasing variance

c) The assumption that error terms are normally distributed

d) The situation where the error terms are perfectly correlated

None

2. Which of the following is a consequence of heteroscedasticity in a regression model?

a) Unbiased estimates of coefficients

b) Increased reliability of hypothesis tests

c) Incorrect standard errors leading to unreliable confidence intervals and hypothesis tests

d) Improved R-squared value

None

3. Which test is commonly used to detect heteroscedasticity?

a) Durbin-Watson test

b) Jarque-Bera test

c) Breusch-Pagan test

d) Augmented Dickey-Fuller test

None

4. Which of the following graphical methods can be used to visually detect heteroscedasticity?

a) Q-Q Plot

b) Scatter plot of residuals versus predicted values

c) Histogram of residuals

d) Scatter plot of independent variable versus dependent variable

None

5. What is the impact of heteroscedasticity on Ordinary Least Squares (OLS) estimators?

a) They remain BLUE (Best Linear Unbiased Estimators)

b) They become biased but efficient

c) They remain unbiased but are no longer efficient

d) They become biased and inefficient

None

6. Which of the following is a method to correct heteroscedasticity in regression analysis?

a) Log transformation of the dependent variable

b) Adding more independent variables

c) Increasing the sample size

d) Running a time series analysis

None

7. What does the presence of heteroscedasticity imply about the residuals?

a) Residuals are normally distributed

b) Residuals have constant variance

c) Residuals exhibit a systematic pattern

d) Residuals are homoscedastic

None

8. Which type of heteroscedasticity occurs when variance increases with the level of an independent variable?

a) Negative heteroscedasticity

b) Positive heteroscedasticity

c) Homoscedasticity

d) Cross-sectional heteroscedasticity

None

9. Why is heteroscedasticity a problem in regression analysis?

a) It violates the assumption of multicollinearity

b) It leads to incorrect estimation of the coefficients

c) It causes the standard errors to be incorrectly estimated, leading to invalid inference

d) It makes the model non-linear

None

10. What is the effect of using robust standard errors in the presence of heteroscedasticity?

a) It increases the efficiency of the estimators

b) It corrects for bias in the coefficients

c) It adjusts the standard errors to account for heteroscedasticity

d) It makes the residuals normally distributed

None

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