flashcards
(26 cards)
Analyzing the behavior of unemployment rates across U.S. states in March of 2006 is an example of using:
(a) time series data.
(b) panel data.
(c) cross-sectional data.
(d) experimental data.
C
Studying inflation in the United States from 1970 to 2006 is an example of using:
(a) randomized controlled experiments.
(b) time series data.
(c) panel data.
(d) cross-sectional data.
B
Analyzing the effect of minimum wage changes on teenage employment across the 48 contiguous U.S. states from 1980 to 2004 is an example of using:
(a) time series data.
(b) panel data.
(c) having a treatment group vs. a control group, since only teenagers receive minimum wages.
(d) cross-sectional data.
B
Econometrics can be defined as follows with the exception of:
(a) the science of testing economic theory.
(b) fitting mathematical economic models to real-world data.
(c) a set of tools used for forecasting future values of economic variables.
(d) measuring the height of economists.
D
One of the primary advantages of using econometrics over typical results from economic theory, is that:
(a) it potentially provides you with quantitative answers for a policy problem rather than simply suggesting the direction (positive/negative) of the response.
(b) teaching you how to use statistical packages
(c) learning how to invert a 4 by 4 matrix.
(d) all of the above.
A
In a randomized controlled experiment:
(a) there is a control group and a treatment group.
(b) you control for the effect that random numbers are not truly randomly generated
(c) you control for random answers
(d) the control group receives treatment on even days only.
A
The graph with vertical axis representing average real GDP growth and horizontal axis showing average trade share is an example of:
(a) cross-sectional data.
(b) experimental data.
(c) a time series.
(d) longitudinal data.
A
The accompanying graph is an example of:
(a) experimental data.
(b) cross-sectional data.
(c) a time series.
(d) longitudinal data.
C
Binary variables:
(a) are generally used to control for outliers in your sample.
(b) can take on more than two values.
(c) exclude certain individuals from your sample.
(d) can take on only two values.
D
In the simple linear regression model, the regression slope:
(a) indicates by how many percent Y increases, given a one percent increase in X.
(b) when multiplied with the explanatory variable will give you the predicted Y.
(c) indicates by how many units Y increases, given a one unit increase in X.
(d) represents the elasticity of Y on X.
C
The regression R2 is a measure of:
(a) whether or not X causes Y.
(b) the goodness of fit of your regression line.
(c) whether or not ESS > TSS.
(d) the square root of the correlation coefficient.
B
In the simple linear regression model Yi = β0 + β1Xi + ui:
(a) the intercept is typically small and unimportant.
(b) β0 + β1Xi represents the population regression function.
(c) the absolute value of the slope is typically between 0 and 1.
(d) β0 + β1Xi represents the sample regression function.
B
E(ui | Xi) = 0 says that:
(a) dividing the error by the explanatory variable results in a zero (on average).
(b) the sample regression function residuals are unrelated to the explanatory variable.
(c) the sample mean of the Xs is much larger than the sample mean of the errors.
(d) the conditional distribution of the error given the explanatory variable has a zero mean.
D
Using a regression Ci = β0 + β1Yi + ui, the estimate of β1 tells you:
(a) ∆Income/∆Consumption
(b) The amount you need to consume to survive
(c) Income/Consumption
(d) ∆Consumption/∆Income
D
The OLS residuals, û, are sample counterparts of the population:
(a) regression function slope.
(b) errors.
(c) regression function’s predicted values.
(d) regression function intercept.
B
Changing the units of measurement will do all EXCEPT:
(a) change residuals.
(b) change numerical slope estimate.
(c) change interpretation of effect of X on Y.
(d) change intercept value.
C
To decide whether the slope coefficient indicates a “large” effect of X on Y, you look at:
(a) economic importance implied by slope.
(b) regression R2.
(c) size of slope.
(d) value of intercept.
A
The t-statistic is calculated by dividing:
(a) the OLS estimator by its standard error.
(b) the slope by the standard deviation of the explanatory variable.
(c) the estimator minus its hypothesized value by the standard error of the estimator.
(d) the slope by 1.96.
C
If the t-statistic for the slope coefficient is 4.38, the units of measurement are:
(a) points of the test score
(b) number of students per teacher
(c) TestScore/STR
(d) standard deviations
D
The 95% confidence interval for β1 is:
(a) (β1 − 1.96SE, β1 + 1.96SE)
(b) (β̂1 − 1.645SE, β̂1 + 1.645SE)
(c) (β̂1 − 1.96SE, β̂1 + 1.96SE)
(d) (β̂1 − 1.96, β̂1 + 1.96)
C
A binary variable is often called a:
(a) dummy variable
(b) dependent variable
(c) residual
(d) power of a test
A
With standard errors 0.51 (robust) and 0.48 (homoskedasticity only), recommended procedure is:
(a) use homoskedastic formula
(b) test for homoskedasticity first
(c) use heteroskedasticity robust formula
(d) depends on slope estimate difference
C
t-statistic for slope in equation ̂TestScore = 698.9−2.28×STR is approximately:
(a) 4.38
(b) 67.20
(c) 0.52
(d) 1.76
A
The 95% confidence interval for slope −0.23 with SE 0.04 is:
(a) [2.57, 3.05]
(b) [−0.31, 0.15]
(c) [−0.31,−0.15]
(d) [−0.33,−0.13]
C
