kasinkei

Which of the following is least likely to result in misspecification of a regression model?

A) Transforming a variable.

B) Using a lagged dependent variable as an independent variable.

C) Measuring independent variables with errors.

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A basic assumption of regression is that the dependent variable is linearly related to each of the independent variables. Frequently, they are not linearly related and the independent variable must be transformed or the model is misspecified. Therefore, transforming an independent variable is a potential solution to a misspecification. Methods used to transform independent variables include squaring the variable or taking the square root.

kasinkei

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) discriminant model.

C) logit model.

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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.

kasinkei

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 subsidiary and competition variables, will company XYZ divest itself of a subsidiary?

C) 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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The number of shares can be a broad range of values and is, therefore, not considered a qualitative dependent variable.

kasinkei

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

A) Logit.

B) Event study.

C) Discriminant analysis.

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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.

kasinkei

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.

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

kasinkei

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

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.

kasinkei

An analyst has run several regressions hoping to predict stock returns, and wants to translate this into an economic interpretation for his clients.

Return = 3.0 + 2.0Beta – 0.0001MarketCap (in billions) + ε

A correct interpretation of the regression most likely includes:

A) a billion dollar increase in market capitalization will drive returns down by 0.01%.

B) a stock with zero beta and zero market capitalization will return precisely 3.0%.

C) prediction errors are always on the positive side.

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The coefficient of MarketCap is 0.01%, indicating that larger companies have slightly smaller returns. Note that a company with no market capitalization would not be expected to have a return at all. Error terms are typically assumed to be normally distributed with a mean of zero

kasinkei

Mary Steen estimated that if she purchased shares of companies who announced restructuring plans at the announcement and held them for five days, she would earn returns in excess of those expected from the market model of 0.9%. These returns are statistically significantly different from zero. The model was estimated without transactions costs, and in reality these would approximate 1% if the strategy were effected. This is an example of:

A) statistical significance, but not economic significance.

B) statistical and economic significance.

C) a market inefficiency.

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The abnormal returns are not sufficient to cover transactions costs, so there is no economic significance to this trading strategy. This is not an example of market inefficiency because excess returns are not available after covering transactions costs.