Lecture 4: Violations of the CLRM Assumptions (Part II)
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Questions and Answers

What are the consequences of violating CLRM assumptions?

  • Problems with coefficient estimates, standard errors, and test statistics (correct)
  • Violations of CLRM assumptions do not occur
  • No impact on coefficient estimates, standard errors, and test statistics
  • Only a minor impact on coefficient estimates

    • What is the consequence of violating the CLRM assumptions?

  • Problems with coefficient estimates, standard errors, and test statistics (correct)
  • Increased precision of coefficient estimates
  • No effect on coefficient estimates
  • Improved coefficient estimates

    • What are the consequences of violating CLRM assumptions?

  • Problems with coefficient estimates, standard errors, and test statistics (correct)
  • Violations of CLRM assumptions do not occur
  • No impact on coefficient estimates, standard errors, and test statistics
  • Only a minor impact on coefficient estimates

    • Which statistical distributions are used to test for violations of CLRM assumptions?

    <p>F and χ2</p> Signup and view all the answers

    What statistical distributions are used to test for violations of CLRM assumptions?

    <p>F and χ2 distributions</p> Signup and view all the answers

    Which statistical distributions are used to test for violations of CLRM assumptions?

    <p>F and χ2</p> Signup and view all the answers

    What is heteroscedasticity?

    <p>The assumption that the variance of the errors is not constant</p> Signup and view all the answers

    What is heteroscedasticity?

    <p>A violation of the assumption that the variance of the errors is constant</p> Signup and view all the answers

    What is heteroscedasticity?

    <p>A violation of the assumption that the variance of the errors is constant</p> Signup and view all the answers

    What method can be used to account for heteroscedasticity?

    <p>GLS</p> Signup and view all the answers

    What method can be used to account for heteroscedasticity?

    <p>GLS</p> Signup and view all the answers

    What method can be used to account for heteroscedasticity?

    <p>GLS</p> Signup and view all the answers

    What is autocorrelation?

    <p>A violation of the assumption that the errors are independent</p> Signup and view all the answers

    What is autocorrelation?

    <p>A violation of the assumption that the errors are independent</p> Signup and view all the answers

    What is multicollinearity?

    <p>The presence of highly correlated explanatory variables</p> Signup and view all the answers

    What test can be used to detect autocorrelation?

    <p>Breusch-Godfrey test</p> Signup and view all the answers

    What test can be used to detect autocorrelation?

    <p>Breusch-Godfrey test</p> Signup and view all the answers

    What is the consequence of perfect multicollinearity?

    <p>Inability to estimate all coefficients</p> Signup and view all the answers

    What are the consequences of ignoring autocorrelation?

    <p>Inefficient coefficient estimates and incorrect inferences</p> Signup and view all the answers

    What is the Ramsey's RESET test used for?

    <p>Testing for mis-specification of functional form</p> Signup and view all the answers

    What are the consequences of ignoring autocorrelation?

    <p>Inefficient coefficient estimates and incorrect inferences</p> Signup and view all the answers

    What is multicollinearity?

    <p>Explanatory variables that are highly correlated with each other</p> Signup and view all the answers

    What is multicollinearity?

    <p>Explanatory variables that are highly correlated with each other</p> Signup and view all the answers

    What is the Bera Jarque test used for?

    <p>Testing residuals for normality</p> Signup and view all the answers

    What test is used to test for mis-specification of functional form?

    <p>Ramsey's RESET test</p> Signup and view all the answers

    What test is used to test for mis-specification of functional form?

    <p>Ramsey's RESET test</p> Signup and view all the answers

    What is the consequence of omitting an important variable or including an irrelevant variable?

    <p>Biased and inconsistent estimates</p> Signup and view all the answers

    What are the consequences of omitting an important variable or including an irrelevant variable?

    <p>Biased and inconsistent estimates</p> Signup and view all the answers

    What is the recommended textbook for learning about introductory econometrics for finance?

    <p>&quot;Introductory Econometrics for Finance, 4th Edition&quot; by Chris Brooks</p> Signup and view all the answers

    What are the consequences of omitting an important variable or including an irrelevant variable?

    <p>Biased and inconsistent estimates</p> Signup and view all the answers

    Study Notes

    1. Lecture 4 focuses on violations of the CLRM assumptions.
    2. There are five assumptions of the CLRM disturbance terms.
    3. Violations of these assumptions can cause problems with coefficient estimates, standard errors, and test statistics.
    4. Tests for violations of these assumptions involve statistical distributions such as F and χ2.
    5. Heteroscedasticity is a violation of the assumption that the variance of the errors is constant.
    6. GLS is a method that can be used to account for heteroscedasticity.
    7. Autocorrelation is a violation of the assumption that there is no pattern in the errors.
    8. The Breusch-Godfrey test can be used to detect autocorrelation.
    9. Ignoring autocorrelation can lead to inefficient coefficient estimates and incorrect inferences.
    10. GLS procedures can be used to correct for autocorrelation, but they require assumptions about the form of the autocorrelation.
    11. Autocorrelation can present an opportunity to modify regression.
    12. Dynamic models can be used to account for previous values of y or x.
    13. Lags in regression can be used to measure time series as overlapping moving averages.
    14. Multicollinearity occurs when explanatory variables are highly correlated with each other.
    15. Perfect multicollinearity results in the inability to estimate all coefficients.
    16. Multicollinearity can be measured by looking at the matrix of correlations between variables.
    17. The Ramsey's RESET test is used to test for mis-specification of functional form.
    18. The Bera Jarque test is used to test residuals for normality.
    19. Omission of an important variable or inclusion of an irrelevant variable can lead to biased and inconsistent estimates.
    20. The textbook "Introductory Econometrics for Finance, 4th Edition" by Chris Brooks is essential reading.

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