Homework Answers
The coefficient on treatment in the regression would be
which is the
slope differential .i.e. when treatment dummy is 1 the slope is
compared to when the treatment dummy takes value zero; the slope
is
.
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Question 7 1 pts You could also estimate a regression with arrest likelihood as an outcome...
QUESTION 10 Sometimes including independent variables in a regression serve as a "data sanity check," in...
QUESTION 10 Sometimes including independent variables in a regression serve as a "data sanity check," in so much as they facilitate a: O more unbiased estimate of the treatment effect O comparison between the estimated coefficient for that variable and the value for that coefficient as predicted by theory O more efficient estimate of the treatment effect in question O secondary measure of the standard error of the treatment effect of interest. QUESTION 11 What are the two primary criteria...
Question 1 2 pts The difference between the mean outcome of those who are treated and...
Question 1 2 pts The difference between the mean outcome of those who are treated and those who are untreated when the assignment of treatments is random is a good estimate of the Average treatment effect (ATE) but not the Treatment on the treated (TT) Selection bias Treatment on the treated but not the average treatment effect Average treatment effect which is equal to the treatment on the treated Sum of treatment on the treated and selection bias Question 2...
Question 4 5 pts A researcher studies the causal effect of education on earnings for male...
Question 4 5 pts A researcher studies the causal effect of education on earnings for male adults using data from a random sample of U.S. adult male workers. She plans to regress individual male workers' earnings (Y) on the number of years of education (X1) the workers have, controlling for work experience and other characteristics of the individuals (Z, e.g. experience). Assume that the unemployment rate is low and the unemployment distribution is random. The researcher is worried that the...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: O multicollinearity. omitted variable bias. O serial correlation. spurious regression. 3 pts Question 9
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently...
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, the coefficients on the included variables will always be biased. the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: spurious regression. omitted variable bias. multicollinearity. serial correlation.
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently...
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients on the included variables will always be biased. the coefficients...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: omitted variable bias. o serial correlation. spurious regression. o multicollinearity.
Question 18 3 pts Consider the following OLS multiple regression results from Table 2 of “The...
Question 18 3 pts Consider the following OLS multiple regression results from Table 2 of “The Impact of Light Skin on Prison Time for Black Female Offenders" (The Social Science Journal 48 (2011), p. 256]. The dependent variable is the natural logarithm of time served, where time served is measured as the number of days served in prison. At the time of admission to prison, correctional officers noted whether female African- American inmates had light skin tones or not, and...
Question 7 3 pts Suppose that you have 50 observations on the variables Y and X....
Question 7 3 pts Suppose that you have 50 observations on the variables Y and X. If the sample correlation coefficient is 0.5 (r=0.5), and you want to test the null hypothesis that the true population correlation coefficient (rho) is equal to zero, then the test statistic associated with this null hypothesis is: o 5 04 O2 O 3 Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high...
QUESTION 10 Sometimes including independent variables in a regression serve as a "data sanity check," in so much as they facilitate a: O more unbiased estimate of the treatment effect O comparison between the estimated coefficient for that variable and the value for that coefficient as predicted by theory O more efficient estimate of the treatment effect in question O secondary measure of the standard error of the treatment effect of interest. QUESTION 11 What are the two primary criteria...
Question 1 2 pts The difference between the mean outcome of those who are treated and those who are untreated when the assignment of treatments is random is a good estimate of the Average treatment effect (ATE) but not the Treatment on the treated (TT) Selection bias Treatment on the treated but not the average treatment effect Average treatment effect which is equal to the treatment on the treated Sum of treatment on the treated and selection bias Question 2...
Question 4 5 pts A researcher studies the causal effect of education on earnings for male adults using data from a random sample of U.S. adult male workers. She plans to regress individual male workers' earnings (Y) on the number of years of education (X1) the workers have, controlling for work experience and other characteristics of the individuals (Z, e.g. experience). Assume that the unemployment rate is low and the unemployment distribution is random. The researcher is worried that the...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: O multicollinearity. omitted variable bias. O serial correlation. spurious regression. 3 pts Question 9
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, the coefficients on the included variables will always be biased. the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: spurious regression. omitted variable bias. multicollinearity. serial correlation.
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients on the included variables will always be biased. the coefficients...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: omitted variable bias. o serial correlation. spurious regression. o multicollinearity.
Question 18 3 pts Consider the following OLS multiple regression results from Table 2 of “The Impact of Light Skin on Prison Time for Black Female Offenders" (The Social Science Journal 48 (2011), p. 256]. The dependent variable is the natural logarithm of time served, where time served is measured as the number of days served in prison. At the time of admission to prison, correctional officers noted whether female African- American inmates had light skin tones or not, and...
Question 7 3 pts Suppose that you have 50 observations on the variables Y and X. If the sample correlation coefficient is 0.5 (r=0.5), and you want to test the null hypothesis that the true population correlation coefficient (rho) is equal to zero, then the test statistic associated with this null hypothesis is: o 5 04 O2 O 3 Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high...
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