R5 - Quant - Multiple Regression

Question 1

Multiple Regression

Answer 1

Linear regression involving two or more independent variables.

Y = b₀ + b₁X₁ + b₂X₂ + E

Where:

Y_i = The ith Observation of the dependent variable Y

X_ji = The ith observation of the independent variable X_j, j=1,2,…,k

b_{0 =} The intercept of the equation

b₁,..,b_k = The slope coefficients for each independent var

Question 2

Adjusted R2

Answer 2

A measure of goodness-of-fit of a regression that is adjusted for degrees of freedom and hence does not automatically increase when another independent variable is added to a regression.

Question 3

Analysis of variance (ANOVA)

Answer 3

The analysis of the total variability of a dataset (such as observations on the dependent variable in a regression) into components representing different sources of variation; with reference to regression, ANOVA provides the inputs for an F-test of the significance of the regression as a whole.

Question 4

Breusch−Pagan test

Answer 4

A test for conditional heteroskedasticity in the error term of a regression.

Question 5

Categorical dependent variables

Answer 5

An alternative term for qualitative dependent variables.

Question 6

Common size statements

Answer 6

Financial statements in which all elements (accounts) are stated as a percentage of a key figure such as revenue for an income statement or total assets for a balance sheet.

Question 7

Conditional heteroskedasticity

Answer 7

Heteroskedasticity in the error variance that is correlated with the values of the independent variable(s) in the regression.

Question 8

Data mining

Answer 8

The practice of determining a model by extensive searching through a dataset for statistically significant patterns.

Question 9

Discriminant analysis

Answer 9

A multivariate classification technique used to discriminate between groups, such as companies that either will or will not become bankrupt during some time frame.

Question 10

Dummy variable

Answer 10

A type of qualitative variable that takes on a value of 1 if a particular condition is true and 0 if that condition is false.

Question 11

First-order serial correlation

Answer 11

Correlation between adjacent observations in a time series.

Question 12

Generalized least squares

Answer 12

A regression estimation technique that addresses heteroskedasticity of the error term.

Question 13

Heteroskedastic

Answer 13

With reference to the error term of regression, having a variance that differs across observations.

Question 14

Heteroskedasticity-consistent standard errors

Answer 14

Standard errors of the estimated parameters of a regression that correct for the presence of heteroskedasticity in the regression’s error term.

Question 15

Log-log regression model

Answer 15

A regression that expresses the dependent and independent variables as natural logarithms.

Question 16

Logistic regression (logit model)

Answer 16

A qualitative-dependent-variable multiple regression model based on the logistic probability distribution.

Question 17

Market timing

Answer 17

Asset allocation in which the investment in the market is increased if one forecasts that the market will outperform T-bills.

Question 18

Model specification

Answer 18

With reference to regression, the set of variables included in the regression and the regression equation’s functional form.

Question 19

Multicollinearity

Answer 19

A regression assumption violation that occurs when two or more independent variables (or combinations of independent variables) are highly but not perfectly correlated with each other.

Question 20

Multiple linear regression model

Answer 20

A linear regression model with two or more independent variables.

Question 21

Negative serial correlation

Answer 21

Serial correlation in which a positive error for one observation increases the chance of a negative error for another observation, and vice versa.

Question 22

Nonstationarity

Answer 22

With reference to a random variable, the property of having characteristics such as mean and variance that are not constant through time.

Question 23

Partial regression coefficients

Answer 23

The slope coefficients in a multiple regression.

Question 24

Partial slope coefficients

Answer 24

The slope coefficients in a multiple regression.

Question 25

Positive serial correlation

Answer 25

Serial correlation in which a positive error for one observation increases the chance of a positive error for another observation, and a negative error for one observation increases the chance of a negative error for another observation.

Question 26

Probit regression (probit model)

Answer 26

A qualitative-dependent-variable multiple regression model based on the normal distribution.

Question 27

Qualitative dependent variables

Answer 27

Dummy variables used as dependent variables rather than as independent variables.

Question 28

Random walk

Answer 28

A time series in which the value of the series in one period is the value of the series in the previous period plus an unpredictable random error.

Question 29

Regression coefficients

Answer 29

The intercept and slope coefficient(s) of a regression.

Question 30

Robust standard errors

Answer 30

Standard errors of the estimated parameters of a regression that correct for the presence of heteroskedasticity in the regression’s error term.

Question 31

Serially correlated

Answer 31

With reference to regression errors, errors that are correlated across observations.

Question 32

Unconditional heteroskedasticity

Answer 32

Heteroskedasticity of the error term that is not correlated with the values of the independent variable(s) in the regression.

Question 33

White-corrected standard errors

Answer 33

A synonym for robust standard errors.