标题: Reading 12: Multiple Regression and Issues in Regression Analy [打印本页]
作者: 土豆妮 时间: 2011-3-3 14:36 标题: [2011]Session 3Reading 12: Multiple Regression and Issues in Regression Analy
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 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.
作者: 土豆妮 时间: 2011-3-3 14:36
Which of the following questions is least likely answered by using a qualitative dependent variable?
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Based on the following company-specific financial ratios, will company ABC enter bankruptcy? | |
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B) |
Based on the following executive-specific and company-specific variables, how many shares will be acquired through the exercise of executive stock options? | |
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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.
作者: 土豆妮 时间: 2011-3-3 14:37
Which of the following is NOT a model that has a qualitative dependent variable?
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C) |
Discriminant analysis. | |
An event study is the estimation of the abnormal returns--generally associated with an informational event—that take on quantitative values.
作者: 土豆妮 时间: 2011-3-3 14:37
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:
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A) |
multiple linear regression adjusting for heteroskedasticity. | |
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C) |
dummy variable regression. | |
The only one of the possible answers that estimates a probability of a discrete outcome is logit modeling.
作者: 土豆妮 时间: 2011-3-3 14:37
What is the main difference between probit models and typical dummy variable models?
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A) |
There is no difference--a probit model is simply a special case of a dummy variable regression. | |
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B) |
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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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.
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