kasinkei

An analyst runs a regression of portfolio returns on three independent variables.  These independent variables are price-to-sales (P/S), price-to-cash flow (P/CF), and price-to-book (P/B).  The analyst discovers that the p-values for each independent variable are relatively high.  However, the F-test has a very small p-value.  The analyst is puzzled and tries to figure out how the F-test can be statistically significant when the individual independent variables are not significant.  What violation of regression analysis has occurred?

A) conditional heteroskedasticity.

B) serial correlation.

C) multicollinearity.

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An indication of multicollinearity is when the independent variables individually are not statistically significant but the F-test suggests that the variables as a whole do an excellent job of explaining the variation in the dependent variable.

kasinkei

An analyst further studies the independent variables of a study she recently completed. The correlation matrix shown below is the result. Which statement best reflects possible problems with a multivariate regression?

	Age	Education	Experience	Income
Age	1.00
Education	0.50	1.00
Experience	0.95	0.55	1.00
Income	0.60	0.65	0.89	1.00

A) Experience may be a redundant variable.

B) Age should be excluded from the regression.

C) Education may be unnecessary.

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The correlation coefficient of experience with age and income, respectively, is close to +1.00. This indicates a problem of multicollinearity and should be addressed by excluding experience as an independent variable.

kasinkei

When two or more of the independent variables in a multiple regression are correlated with each other, the condition is called:

A) serial correlation.

B) multicollinearity.

C) conditional heteroskedasticity.

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Multicollinearity refers to the condition when two or more of the independent variables, or linear combinations of the independent variables, in a multiple regression are highly correlated with each other. This condition distorts the standard error of estimate and the coefficient standard errors, leading to problems when conducting t-tests for statistical significance of parameters.

kasinkei

When utilizing a proxy for one or more independent variables in a multiple regression model, which of the following errors is most likely to occur?

A) Multicollinearity.

B) Heteroskedasticity.

C) Model misspecification.

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By using a proxy for an independent variable in a multiple regression analysis, there is some degree of error in the measurement of the variable.

kasinkei

When constructing a regression model to predict portfolio returns, an analyst runs a regression for the past five year period. After examining the results, she determines that an increase in interest rates two years ago had a significant impact on portfolio results for the time of the increase until the present. By performing a regression over two separate time periods, the analyst would be attempting to prevent which type of misspecification?

A) Incorrectly pooling data.

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

C) Forecasting the past.

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The relationship between returns and the dependent variables can change over time, so it is critical that the data be pooled correctly. Running the regression for multiple sub-periods (in this case two) rather than one time period can produce more accurate results.

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.