1. To address autocorrelation a researcher should a. Use squared X variables b.
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Question
1. To address autocorrelation a researcher should
a. Use squared X variables
b. Remove X variables until the non-random residual pattern goes away
c. Find the missing X-variable that is causing the non-random residual pattern
d. Use squared Y variables
2. When two X variables in a regression are highly correlated because they are alternative measures of the same causal process, they cause a concern named
a. Interaction
b. Multicollinearity
c. Curvature
d. Non-constant variance
3. What determines the direction of causality (that is, whether X causes Y or Y causes X)?
a. The logic of the situation, not the math
b. The R2
c. The intercept
d. The f-test
Explanation / Answer
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A second problem is determining the direction of causality. A correlation between two variables does not indicate which variable is causing which. For example, Reinhart and Rogoff (2010) found a strong correlation between public debt and GDP growth. Although some have argued that public debt slows growth, most evidence supports the alternative that slow growth increases public debt.
a. The logic of the situation, not the math
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