Internal Validity -(correct answer)the statistical inferences about causal effects are valid for the
population being studied External Validity -(correct answer)if conclusions can be generalized
to other populations and settings Population Studied -(correct answer)is the population from
which the sample was drawn Population of Interest -(correct answer)is the population to
which causal inferences from this study are to be applied An ordinary least squares regression
of Y onto X will be internally inconsistent if X is correlated with the error term, True or False
-(correct answer)True Each of the five primary threats to internal validity implies that X is
correlated with the error term, True or False -(correct answer)True A statistical analysis is
internally valid if: -(correct answer)the statistical inferences about causal effects are valid for
the population studied Threats to internal validity lead to: -(correct answer)failures of one or
more of the least squares assumptions The question of reliability/unreliability of a multiple
regression depends on: -(correct answer)internal and external validity Internal validity is
that: -(correct answer)the estimator of the causal effect should be unbiased and consistent
What is the trade-off when including an extra variable in a regression? -(correct
answer)An extra variable could control for omitted variable bias, but it also increases the
variance of other estimated coefficients. In the case of errors-in-variables bias: -(correct
answer)the OLS estimator is consistent if the variance in the unobservable variable is
relatively large compared to the variance in the measurement error. In the case of
errors-in-variables bias, the precise size and direction of the bias depend on: -(correct
answer)the correlation between the measured variable and the measurement error. What do
subscripts i and t refer to? -(correct answer)Subscripts i and t identify the entity and time
period respectively. The fixed effects regression model: -(correct answer)has n different
intercepts. In the time fixed effects regression model, you should exclude one of the binary
variables for the time periods when an intercept is present in the equation: -(correct
answer)to avoid perfect multicollinearity. Describes a time fixed effects regression model