Reading 12: Multiple Regression and Issues in Regression Analy 2011, building, returns, center

土豆妮

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

LOS l: Discuss models with qualitative dependent variables.

 

 

An analyst is building a regression model which returns a qualitative dependant variable based on a probability distribution. This is least likely a:

A) probit model.

B) logit model.

C) discriminant model.

---

 

A probit model is a qualitative dependant variable which is based on a normal distribution. A logit model is a qualitative dependant variable which is based on the logistic distribution. A discriminant model returns a qualitative dependant variable based on a linear relationship that can be used for ranking or classification into discrete states.

土豆妮

Which of the following questions is least likely answered by using a qualitative dependent variable?

A) Based on the following company-specific financial ratios, will company ABC enter bankruptcy?

B) Based on the following executive-specific and company-specific variables, how many shares will be acquired through the exercise of executive stock options?

C) Based on the following subsidiary and competition variables, will company XYZ divest itself of a subsidiary?

---

The number of shares can be a broad range of values 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) Event study.

C) Discriminant analysis.

---

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

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) logit modeling.

C) dummy variable regression.

---

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

土豆妮

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

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

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

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

---

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.

---