Econometrics Final Quizzes Flashcards
(42 cards)
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.
In the log-log model, the slope coefficient indicates:
the elasticity of Y with respect to X.
Misspecification of functional form of the regression function:
Results in Omitted Variable Bias
Which of the the following are different causes of potential model misspecification
- choice of variables
- functional form
- error structure
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
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
When testing for heteroskedasticity, you will reject the null hypothesis of homoscedasticity if the t-statistic is greater than the critical t-value.
False; If LM> LM*
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:f
for a binary variable model, the predicted value from the population regression is the probability that Y=1, given X.
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 R^2
An alternative method of estimating Binary Outcome Models is the
Logit Model
Provide an example of a Binary Outcome (Limited Dependent Variable).
Getting a new car or not getting a new car based on income, wealth, location, and job status.
For the polynomial regression model:
the techniques for estimation and inference developed for multiple regression can be applied.
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 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
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.
