Econometrics Final

The interpretation of the slope coefficient in the model Yi = β0 + β1 ln(Xi) + ui is as follows:

An example of a quadratic regression model is:

For the polynomial regression model:

In the log-log model, the slope coefficient indicates:

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:

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:

Misspecification of the functional form of the regression function:

Which of the the following are different causes of potential model misspecification except

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.

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).

Heteroskedasticity means that:

When a model has heteroskedastic errors, you can use OLS with heteroskedasticity-robust standard errors because:

In the presence of heteroskedasticity, if using White's estimation the OLS estimator is:

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:

Which of the following is a difference between the White test and the Breusch-Pagan test?

A simple way to visually inspect whether the results are likely to be heteroskedastic is to:

Which of the following statements related to heteroskedasticity are correct?

The Harvey-Godfrey tests assumes that the heteroskedasticity has a linear functional form with a specific X.

The White's Test is a very general heteroskedasticity test that test for several different structures of heteroskedasticity.

When testing for heteroskedasticity, you will reject the null hypothesis of homoscedasticity if the t-statistic is greater than the critical t-value.

The binary dependent variable model is an example of a:

In the binary dependent variable model, a predicted value of 0.6 means that:

E(Y|X1,..., Xk) = Pr(Y = 1| X1,..., Xk) means that:

The linear probability model is:

In the linear probability model, the interpretation of the slope coefficient is:

The following tools from multiple regression analysis carry over in a meaningful manner to the linear probability model, with the exception of the:

The major flaw of the linear probability model is that:

An alternative method of estimating Binary Outcome Models is the Logit Model.