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Econometric 4-5-6-7-8

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Econometric 4-5-6-7-8
aykutwashere
Quiz by aykutwashere, updated more than 1 year ago
aykutwashere
Created by aykutwashere over 8 years ago
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Question 1

Question
Binary variables
Answer
  • are generally used to control for outliers in your sample.
  • can take on more than two values.
  • exclude certain individuals from your sample.
  • can take on only two values.

Question 2

Question
In the simple linear regression model, the regression slope
Answer
  • indicates by how many percent Y increases, given a one percent increase in X.
  • when multiplied with the explanatory variable will give you the predicted Y.
  • indicates by how many units Y increases, given a one unit increase in X.
  • represents the elasticity of Y on X.

Question 3

Question
In which of the following relationships does the intercept have a real-world interpretation?
Answer
  • the relationship between the change in the unemployment rate and the growth rate of real GDP (“Okun’s Law”)
  • the demand for coffee and its price
  • test scores and class-size
  • weight and height of individuals

Question 4

Question
The OLS residuals, ˆi u , are sample counterparts of the population
Image:
88f97b00-9a8e-4e9d-bbe2-2329e2e066df (image/jpeg)
Answer
  • regression function slope
  • errors
  • regression function’s predicted values
  • regression function intercept

Question 5

Question
Changing the units of measurement, e.g. measuring test scores in 100s, will do all of the following EXCEPT for changing the
Answer
  • residuals
  • numerical value of the slope estimate
  • interpretation of the effect that a change in X has on the change in Y
  • numerical value of the intercept

Question 6

Question
To decide whether the slope coefficient indicates a “large” effect of X on Y, you look at the
Answer

Question 7

Question
The t-statistic is calculated by dividing
Answer
  • the OLS estimator by its standard error.
  • the slope by the standard deviation of the explanatory variable.
  • the estimator minus its hypothesized value by the standard error of the estimator.
  • the slope by 1.96.

Question 8

Question
A binary variable is often called a
Answer
  • dummy variable
  • dependent variable
  • residual
  • power of a test

Question 9

Question
If the errors are heteroskedastic, then
Answer
  • OLS is BLUE
  • WLS is BLUE if the conditional variance of the errors is known up to a constant factor of proportionality
  • LAD is BLUE if the conditional variance of the errors is known up to a constant factor of proportionality
  • OLS is efficient

Question 10

Question
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
Answer
  • use the homoskedasticity only formula because the t-statistic becomes larger
  • first test for homoskedasticity of the errors and then make a decision
  • use the heteroskedasticity robust formula
  • make a decision depending on how much different the estimate of the slope is under the two procedures

Question 11

Question
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:
Answer
  • you decide that the coefficient is small and hence most likely is zero in the population
  • the slope is statistically significant since it is four standard errors away from zero
  • the response of Y given a change in X must be economically important since it is statistically significant
  • since the slope is very small, so must be the regression R2(square).

Question 12

Question
In the multiple regression model, the adjusted R2, R2
Image:
5613d520-003f-41b6-a6d4-99d214d82ba6 (image/jpeg)
Answer
  • cannot be negative.
  • will never be greater than the regression R2(square).
  • equals the square of the correlation coefficient r.
  • cannot decrease when an additional explanatory variable is added.

Question 13

Question
If you had a two regressor regression model, then omitting one variable which is relevant
Answer
  • will have no effect on the coefficient of the included variable if the correlation between the excluded and the included variable is negative.
  • will always bias the coefficient of the included variable upwards.
  • 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.
  • makes the sum of the product between the included variable and the residuals different from 0.

Question 14

Question
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
Answer
  • have an exact normal distribution for n > 25.
  • are BLUE.
  • have a normal distribution in small samples as long as the errors are homoskedastic.
  • are unbiased and consistent.

Question 15

Question
The following OLS assumption is most likely violated by omitted variables bias:
Answer

Question 16

Question
The dummy variable trap is an example of
Answer
  • imperfect multicollinearity
  • something that is of theoretical interest only
  • perfect multicollinearity
  • something that does not happen to university or college students

Question 17

Question
Imperfect multicollinearity
Answer
  • is not relevant to the field of economics and business administration
  • only occurs in the study of finance
  • means that the least squares estimator of the slope is biased
  • means that two or more of the regressors are highly correlated

Question 18

Question
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
Image:
22c1facd-6d7f-48c6-97d9-5bdc1bcc3013 (image/jpeg)
Answer
  • heteroskedasticity
  • perfect multicollinearity
  • omitted variable bias
  • dummy variable trap

Question 19

Question
Imperfect multicollinearity
Answer
  • implies that it will be difficult to estimate precisely one or more of the partial effects using the data at hand
  • violates one of the four Least Squares assumptions in the multiple regression model
  • means that you cannot estimate the effect of at least one of the Xs on Y
  • suggests that a standard spreadsheet program does not have enough power to estimate the multiple regression model

Question 20

Question
When testing joint hypothesis, you should
Answer
  • use t-statistics for each hypothesis and reject the null hypothesis is all of the restrictions fail
  • use the F-statistic and reject all the hypothesis if the statistic exceeds the critical value
  • use t-statistics for each hypothesis and reject the null hypothesis once the statistic exceeds the critical value for a single hypothesis
  • use the F-statistics and reject at least one of the hypothesis if the statistic exceeds the critical value

Question 21

Question
In the multiple regression model, the t-statistic for testing that the slope is significantly different from zero is calculated
Answer
  • by dividing the estimate by its standard error.
  • from the square root of the F-statistic.
  • by multiplying the p-value by 1.96.
  • using the adjusted R2(square) and the confidence interval.

Question 22

Question
If you wanted to test, using a 5% significance level, whether or not a specific slope coefficient is equal to one, then you should
Answer
  • subtract 1 from the estimated coefficient, divide the difference by the standard error, and check if the resulting ratio is larger than 1.96.
  • add and subtract 1.96 from the slope and check if that interval includes 1.
  • see if the slope coefficient is between 0.95 and 1.05.
  • check if the adjusted R2 is close to 1.

Question 23

Question
When there are two coefficients, the resulting confidence sets are
Answer
  • rectangles
  • ellipses
  • squares
  • trapezoids

Question 24

Question
The homoskedasticity-only F-statistic and the heteroskedasticity-robust F-statistic typically are
Answer
  • the same
  • different
  • related by a linear function
  • a multiple of each other (the heteroskedasticity-robust F-statistic is 1.96 times the homoskedasticity-only F-statistic)

Question 25

Question
A nonlinear function
Answer
  • makes little sense, because variables in the real world are related linearly.
  • can be adequately described by a straight line between the dependent variable and one of the explanatory variables.
  • 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.
  • is a function with a slope that is not constant.

Question 26

Question
The best way to interpret polynomial regressions is to
Answer
  • take a derivative of Y with respect to the relevant X.
  • 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.
  • look at the t-statistics for the relevant coefficients.
  • analyze the standard error of estimated effect.
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