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Quantitative Analysis and Econometrics Flashcards

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Read the first 7 Quantitative Analysis and Econometrics flashcards as text

  1. In a multiple regression model, what does heteroscedasticity specifically refer to?

    Answer: Non-constant variance of the error term across observations

    Heteroscedasticity means the variance of the regression error term is not constant across all levels of the independent variables.

  2. A researcher finds that the Durbin-Watson statistic is approximately 0.5. This most likely indicates:

    Answer: Positive serial correlation in residuals

    A Durbin-Watson value close to 0 indicates strong positive autocorrelation, while values near 4 suggest negative autocorrelation, and values near 2 suggest no autocorrelation.

  3. Which transformation is most commonly used to linearize an exponential relationship of the form Y = ae^(bX)?

    Answer: Natural log transformation of Y

    Taking the natural log of both sides converts ln(Y) = ln(a) + bX, which is a linear relationship suitable for OLS estimation.

  4. In instrumental variable (IV) regression, a valid instrument must satisfy which two conditions?

    Answer: Relevance and exogeneity

    A valid instrument must be correlated with the endogenous regressor (relevance) and uncorrelated with the error term (exogeneity).

  5. The Variance Inflation Factor (VIF) of 10 for a predictor variable suggests:

    Answer: Severe multicollinearity affecting that predictor's coefficient estimate

    A VIF of 10 indicates that 90% of the variance of that predictor is explained by other predictors, signaling severe multicollinearity.

  6. When performing a Chow test in econometrics, the null hypothesis states that:

    Answer: The regression coefficients are equal across two subgroups

    The Chow test examines structural stability by testing whether the same regression coefficients apply to two different data subsets or time periods.

  7. In logistic regression, a log-odds coefficient of 0.693 for a binary predictor implies an odds ratio of approximately:

    Answer: 2.00

    The odds ratio equals e^(coefficient) = e^(0.693) = 2.00, meaning the event is twice as likely when the predictor equals 1 versus 0.