4332301
Descripción
Test por aykutwashere, actualizado hace más de 1 año
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Creado por aykutwashere
hace más de 8 años
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Pregunta 1
Pregunta
Binary variables
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are generally used to control for outliers in your sample.
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can take on more than two values.
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exclude certain individuals from your sample.
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can take on only two values.
Pregunta 2
Pregunta
In the simple linear regression model, the regression slope
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indicates by how many percent Y increases, given a one percent increase in X.
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when multiplied with the explanatory variable will give you the predicted Y.
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indicates by how many units Y increases, given a one unit increase in X.
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represents the elasticity of Y on X.
Pregunta 3
Pregunta
In which of the following relationships does the intercept have a real-world interpretation?
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the relationship between the change in the unemployment rate and the growth rate of real GDP (“Okun’s Law”)
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the demand for coffee and its price
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test scores and class-size
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weight and height of individuals
Pregunta 4
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The OLS residuals, ˆi u , are sample counterparts of the population
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regression function slope
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errors
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regression function’s predicted values
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regression function intercept
Pregunta 5
Pregunta
Changing the units of measurement, e.g. measuring test scores in 100s, will do all of the following EXCEPT for changing the
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residuals
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numerical value of the slope estimate
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interpretation of the effect that a change in X has on the change in Y
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numerical value of the intercept
Pregunta 6
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To decide whether the slope coefficient indicates a “large” effect of X on Y, you look at the
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size of the slope coefficient
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regression R2
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economic importance implied by the slope coefficient
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value of the intercept
Pregunta 7
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The t-statistic is calculated by dividing
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the OLS estimator by its standard error.
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the slope by the standard deviation of the explanatory variable.
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the estimator minus its hypothesized value by the standard error of the estimator.
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the slope by 1.96.
Pregunta 8
Pregunta
A binary variable is often called a
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dummy variable
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dependent variable
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residual
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power of a test
Pregunta 9
Pregunta
If the errors are heteroskedastic, then
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OLS is BLUE
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WLS is BLUE if the conditional variance of the errors is known up to a constant factor of proportionality
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LAD is BLUE if the conditional variance of the errors is known up to a constant factor of proportionality
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OLS is efficient
Pregunta 10
Pregunta
Using the textbook example of 420 California school districts and the regression of test scores on the student-teacher ratio, you find that the standard error on the slope coefficient is 0.51 when using the heteroskedasticity robust formula, while it is 0.48 when employing the homoskedasticity only formula. When calculating the t-statistic, the recommended procedure is to
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use the homoskedasticity only formula because the t-statistic becomes larger
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first test for homoskedasticity of the errors and then make a decision
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use the heteroskedasticity robust formula
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make a decision depending on how much different the estimate of the slope is under the two procedures
Pregunta 11
Pregunta
Using 143 observations, assume that you had estimated a simple regression function and that your estimate for the slope was 0.04, with a standard error of 0.01. You want to test whether or not the estimate is statistically significant. Which of the following decisions is the only correct one:
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you decide that the coefficient is small and hence most likely is zero in the population
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the slope is statistically significant since it is four standard errors away from zero
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the response of Y given a change in X must be economically important since it is statistically significant
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since the slope is very small, so must be the regression R2(square).
Pregunta 12
Pregunta
In the multiple regression model, the adjusted R2, R2
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cannot be negative.
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will never be greater than the regression R2(square).
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equals the square of the correlation coefficient r.
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cannot decrease when an additional explanatory variable is added.
Pregunta 13
Pregunta
If you had a two regressor regression model, then omitting one variable which is relevant
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will have no effect on the coefficient of the included variable if the correlation between the excluded and the included variable is negative.
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will always bias the coefficient of the included variable upwards.
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can result in a negative value for the coefficient of the included variable, even though the coefficient will have a significant positive effect on Y if the omitted variable were included.
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makes the sum of the product between the included variable and the residuals different from 0.
Pregunta 14
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Under the least squares assumptions for the multiple regression problem (zero conditional mean for the error term, all Xi and Yi being i.i.d., all Xi and ui having finite fourth moments, no perfect multicollinearity), the OLS estimators for the slopes and intercept
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have an exact normal distribution for n > 25.
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are BLUE.
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have a normal distribution in small samples as long as the errors are homoskedastic.
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are unbiased and consistent.
Pregunta 15
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The following OLS assumption is most likely violated by omitted variables bias:
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there is heteroskedasticity
Pregunta 16
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The dummy variable trap is an example of
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imperfect multicollinearity
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something that is of theoretical interest only
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perfect multicollinearity
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something that does not happen to university or college students
Pregunta 17
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Imperfect multicollinearity
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is not relevant to the field of economics and business administration
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only occurs in the study of finance
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means that the least squares estimator of the slope is biased
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means that two or more of the regressors are highly correlated
Pregunta 18
Pregunta
Consider the multiple regression model with two regressors X1 and X2, where both variables are determinants of the dependent variable. You first regress Y on X1 only and find no relationship. However when regressing Y on X1 and X2, the slope coefficient 1ˆ changes by a large amount. This suggests that your first regression suffers from
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heteroskedasticity
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perfect multicollinearity
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omitted variable bias
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dummy variable trap
Pregunta 19
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Imperfect multicollinearity
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implies that it will be difficult to estimate precisely one or more of the partial effects using the data at hand
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violates one of the four Least Squares assumptions in the multiple regression model
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means that you cannot estimate the effect of at least one of the Xs on Y
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suggests that a standard spreadsheet program does not have enough power to estimate the multiple regression model
Pregunta 20
Pregunta
When testing joint hypothesis, you should
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use t-statistics for each hypothesis and reject the null hypothesis is all of the restrictions fail
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use the F-statistic and reject all the hypothesis if the statistic exceeds the critical value
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use t-statistics for each hypothesis and reject the null hypothesis once the statistic exceeds the critical value for a single hypothesis
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use the F-statistics and reject at least one of the hypothesis if the statistic exceeds the critical value
Pregunta 21
Pregunta
In the multiple regression model, the t-statistic for testing that the slope is significantly different from zero is calculated
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by dividing the estimate by its standard error.
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from the square root of the F-statistic.
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by multiplying the p-value by 1.96.
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using the adjusted R2(square) and the confidence interval.
Pregunta 22
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If you wanted to test, using a 5% significance level, whether or not a specific slope coefficient is equal to one, then you should
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subtract 1 from the estimated coefficient, divide the difference by the standard error, and check if the resulting ratio is larger than 1.96.
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add and subtract 1.96 from the slope and check if that interval includes 1.
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see if the slope coefficient is between 0.95 and 1.05.
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check if the adjusted R2 is close to 1.
Pregunta 23
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When there are two coefficients, the resulting confidence sets are
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rectangles
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ellipses
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squares
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trapezoids
Pregunta 24
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The homoskedasticity-only F-statistic and the heteroskedasticity-robust F-statistic typically are
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the same
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different
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related by a linear function
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a multiple of each other (the heteroskedasticity-robust F-statistic is 1.96 times the homoskedasticity-only F-statistic)
Pregunta 25
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A nonlinear function
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makes little sense, because variables in the real world are related linearly.
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can be adequately described by a straight line between the dependent variable and one of the explanatory variables.
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is a concept that only applies to the case of a single or two explanatory variables since you cannot draw a line in four dimensions.
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is a function with a slope that is not constant.
Pregunta 26
Pregunta
The best way to interpret polynomial regressions is to
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take a derivative of Y with respect to the relevant X.
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plot the estimated regression function and to calculate the estimated effect on Y associated with a change in X for one or more values of X.
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look at the t-statistics for the relevant coefficients.
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analyze the standard error of estimated effect.
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