28 Questions
The interpretation of the slope coefficient in the model Yi = β0 + β1 ln(Xi) + ui is as follows:
a 1% change in X is associated with a change in Y of 0.01 β1.
An example of a quadratic regression model is:
Yi = β0 + β1X + β2X2 + ui.
For the polynomial regression model:
the techniques for estimation and inference developed for multiple regression can be applied.
In the log-log model, the slope coefficient indicates:
the elasticity of Y with respect to X.
Assume that you had estimated the following quadratic regression model testscore^ = 607.3 + 3.85 Income - 0.0423 Income2. If income increased from 10 to 11 ($10,000 to $11,000), then the predicted effect on test scores would be:
2.96
Consider the following least squares specification between test scores and the student-teacher ratio:
testscores^ = 557.8 + 36.42 ln (Income). According to this equation, a 1% increase income is associated with an increase in test scores of:
0.36 points
Misspecification of the functional form of the regression function:
results in omitted variable bias.
Which of the the following are different causes of potential model misspecification except
all of these are potential causes
Consider the following regression model: savingsi = β0 + β1age + β2age2 + ui. The overall change in savings caused by a one-year change in age is equal to β1.
False
In nonlinear models, the expected change in the dependent variable for a change in one of the explanatory variables is given by:
△Y = f(X1 + △X1, X2,..., Xk) - f(X1, X2,...Xk).
True
Heteroskedasticity means that:
the variance of the error term is not constant.
When a model has heteroskedastic errors, you can use OLS with heteroskedasticity-robust standard errors because:
the exact structure of the heteroscedasticity is rarely know.
In the presence of heteroskedasticity, if using White's estimation the OLS estimator is:
unbiased and more precise
You estimate a model of student test scores on student-teacher ratio using a sample of 420 California school districts. Using OLS the estimated standard error on the slope coefficient is 0.51, but when using when using the heteroskedasticity robust estimation (White's estimation) it is 0.48. The t-statistic is:
use White's estimation because the t-statistic will be smaller than with OLS
Which of the following is a difference between the White test and the Breusch-Pagan test?
The Breusch-Pagan test assumes that we have knowledge of the variables appearing in the variance function of heterosckedasticity.
A simple way to visually inspect whether the results are likely to be heteroskedastic is to:
examine a scatterplot of the residuals (error terms) and X plot.
Which of the following statements related to heteroskedasticity are correct?
The OLS estimator is still linear in parameters with unbiased estimates of the betas but is no longer the best.
The Harvey-Godfrey tests assumes that the heteroskedasticity has a linear functional form with a specific X.
False
The White's Test is a very general heteroskedasticity test that test for several different structures of heteroskedasticity.
True
When testing for heteroskedasticity, you will reject the null hypothesis of homoscedasticity if the t-statistic is greater than the critical t-value.
False
The binary dependent variable model is an example of a:
limited dependent variable model.
In the binary dependent variable model, a predicted value of 0.6 means that:
given the values for the explanatory variables, there is a 60 percent probability that the dependent variable will equal one.
E(Y|X1,..., Xk) = Pr(Y = 1| X1,..., Xk) means that:
for a binary variable model, the predicted value from the population regression is the probability that Y=1, given X.
The linear probability model is:
the application of the linear multiple regression model to a binary dependent variable.
In the linear probability model, the interpretation of the slope coefficient is:
the change in probability that Y=1 associated with a unit change in X, holding others regressors constant.
The following tools from multiple regression analysis carry over in a meaningful manner to the linear probability model, with the exception of the:
regression R2.
The major flaw of the linear probability model is that:
the predicted values can lie above 1 and below 0.
An alternative method of estimating Binary Outcome Models is the Logit Model.
True
Test your understanding of regression models and interpretation of slope coefficients with this quiz. Explore concepts like log-log models, quadratic regression, and the effects of income on test scores.
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