12: Multiple Regression and Issues in Regression Ana 2010, statements, following, example

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Session 3: Quantitative Methods: Quantitative
Methods for Valuation
Reading 12: Multiple Regression and Issues in Regression Analysis

LOS j: Discuss models with qualitative dependent variables.

 

 

 

Which of the following statements is least likely an example of a qualitative dependent variable? The:

A) probability of bankruptcy is explained by several company-specific financial ratios.

B) likelihood that a company will divest itself of a subsidiary, explained by subsidiary and competition variables.

C) number of shares acquired through the exercise of executive stock options, explained by executive-specific and company-specific variables.

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Which of the following is NOT a model that has a qualitative dependent variable?

A) Logit.

B) Discriminant analysis.

C) Event study.

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An event study is the estimation of the abnormal returns--generally associated with an informational event—that take on quantitative values.

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A high-yield bond analyst is trying to develop an equation using financial ratios to estimate the probability of a company defaulting on its bonds. Since the analyst is using data over different economic time periods, there is concern about whether the variance is constant over time. A technique that can be used to develop this equation is:

A) multiple linear regression adjusting for heteroskedasticity.

B) dummy variable regression.

C) logit modeling.

土豆妮

A high-yield bond analyst is trying to develop an equation using financial ratios to estimate the probability of a company defaulting on its bonds. Since the analyst is using data over different economic time periods, there is concern about whether the variance is constant over time. A technique that can be used to develop this equation is:

A) multiple linear regression adjusting for heteroskedasticity.

B) dummy variable regression.

C) logit modeling.

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The only one of the possible answers that estimates a probability of a discrete outcome is logit modeling.

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What is the main difference between probit models and typical dummy variable models?

A) A dummy variable represents a qualitative independent variable, while a probit model is used for estimating the probability of a qualitative dependent variable.

B) There is no difference--a probit model is simply a special case of a dummy variable regression.

C) Dummy variable regressions attempt to create an equation to classify items into one of two categories, while probit models estimate a probability.

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What is the main difference between probit models and typical dummy variable models?

A) A dummy variable represents a qualitative independent variable, while a probit model is used for estimating the probability of a qualitative dependent variable.

B) There is no difference--a probit model is simply a special case of a dummy variable regression.

C) Dummy variable regressions attempt to create an equation to classify items into one of two categories, while probit models estimate a probability.

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Dummy variables are used to represent a qualitative independent variable. Probit models are used to estimate the probability of occurrence for a qualitative dependent variable.

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土豆妮

Which of the following statements is least likely an example of a qualitative dependent variable? The:

A) probability of bankruptcy is explained by several company-specific financial ratios.

B) likelihood that a company will divest itself of a subsidiary, explained by subsidiary and competition variables.

C) number of shares acquired through the exercise of executive stock options, explained by executive-specific and company-specific variables.

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The number of shares is a continuous variable and is, therefore, not considered a qualitative dependent variable.

土豆妮

Which of the following is NOT a model that has a qualitative dependent variable?

A) Logit.

B) Discriminant analysis.

C) Event study.