CFA Level 2 – Quantitative Methods Session 3 – Reading 12 Multiple Regression and Issues in Regression Analysis

CFA Level 2 – Quantitative Methods, Session 3 – Reading 12

Multiple Regression and Issues in Regression Analysis – LOS l

(Practice Questions, Sample Questions)

1. 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

Explanation — C: 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

2. 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?

Explanation — B: The number of shares can be a broad range of values and is, therefore, not considered a qualitative dependent variable.

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

A) Logit.
B) Event study.
C) Discriminant analysis

Explanation — B: An event study is the estimation of the abnormal returns–generally associated with an informational event—that take on quantitative values

4. 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

Explanation — B: The only one of the possible answers that estimates a probability of a discrete outcome is logit modeling

5. 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

Explanation — C: 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

Remember! This is just a sample.

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