Quantitative Methods for Finance Homework.

Is the question true or false. Add a short explanation

True or False Are the following statements true or false? Explain your answers briefly. a. Bootstrap might be ineffective for linear regressions when there are extreme outliers in the data. b. In order to identify the key determinants of the credit rating issued by Moody's, one would need to estimate a multinomial logit model. c. One of the reasons of the conditional heteroskedasticity in error terms is that the regression model omits an important variable which is persistent. d. The goodness-of-fit of a probit model can be measured using the percentage of cor- rectly predicted outcomes of the dependent variable y. e. We can use the Goldfeld-Quandt test of heteroskedasticity when we suspect the error variance depends on one of the explanatory variables. f. If adding or removing an explanatory variables does not influence other regression estimates, then we may deal with near multicollinearity. g. The model y e*xellt cannot be estimated using OLS. Here, yx, x4 are data series and a and are parameters to be estimated. h. If an important explanatory variable is omitted from the regression model, the OLS estimator will be inefficient. True or False Are the following statements true or false? Explain your answers briefly. a. Bootstrap might be ineffective for linear regressions when there are extreme outliers in the data. b. In order to identify the key determinants of the credit rating issued by Moody's, one would need to estimate a multinomial logit model. c. One of the reasons of the conditional heteroskedasticity in error terms is that the regression model omits an important variable which is persistent. d. The goodness-of-fit of a probit model can be measured using the percentage of cor- rectly predicted outcomes of the dependent variable y. e. We can use the Goldfeld-Quandt test of heteroskedasticity when we suspect the error variance depends on one of the explanatory variables. f. If adding or removing an explanatory variables does not influence other regression estimates, then we may deal with near multicollinearity. g. The model y e*xellt cannot be estimated using OLS. Here, yx, x4 are data series and a and are parameters to be estimated. h. If an important explanatory variable is omitted from the regression model, the OLS estimator will be inefficient