Quantitative Analysis

Question 1

Spurious Correlation

Answer 1

Used to refer to 1) correlation between two variables that reflects chance relationships in a particular data set, 2) correlation induced by a calculation that mixes each of two variables with a third, and 3) correlation between two variables arising not from a direct relation between them but from their relation to a third variable.

Question 2

Linear Regression

Answer 2

Also known as linear least squares, computes a line that best fits the observations; it chooses values for intercept b0 and b1 that minimize the sum of the squared vertical distances between the observations and the regression line.

Question 3

Standard Error of Estimate (SEE)

Answer 3

Measures uncertainty; is similar to the standard deviation for a single variable, except that it measures the standard deviation of the residual term in the regression. An indicator of the strength of the relationship between the dependent and independent variables. The SEE will be low if the relationship is strong and high if it is weak.

Question 4

Coefficient of Determination (R^2)

Answer 4

The fraction of the total variation that is explained by the regression.

Question 5

Type 1 Error

Answer 5

Rejecting the null when it is true.

Question 6

Type 2 Error

Answer 6

Failing to reject the null when it is in fact false.

Question 7

Analysis of Variance (ANOVA)

Answer 7

A statistical procedure for dividing the total variability into components that can be attributed to different sources. Used to determine usefulness of the independent variable or variables in explaining variation in the dependent variable.

Question 8

Multiple R

Answer 8

Is the correlation of the two variables, which is also the square root of the R^2 for one variable equations.

Question 9

Regression MSS

Answer 9

Explained Variation/degrees of freedom

Question 10

Multiple Linear Regression

Answer 10

Allows you to determine the effect of more than one independent variable on a particular dependent variable.

Question 11

Heteroskedastic

Answer 11

The variance of the errors differs across observations.

Question 12

Unconditional Heteroskedasticity

Answer 12

Occurs when Heteroskedasticity of the error variance is not correlated with the independent variables in the multiple regression.

Question 13

Conditional Heteroskedasticity

Answer 13

Heteroskedasticity in the error variance that is correlated with (conditional on) the values of the independent variables in the regression.

Question 14

Serial Correlation

Answer 14

When regression errors are correlated across observations. Also known as, autocorrelated.

Question 15

Positive Serial Correlation

Answer 15

Serial correlation in which a positive error for one observation increases the chance of a positive error for another observation. It also means that a negative error for one observation increases the chance of a negative error for another observation. The residual terms are correlated with one another, leading to coefficient error terms that are too small.

Question 16

Multicollinearity

Answer 16

Occurs when two or more independent variables (or combinations of independent variables) are highly (but not perfectly) correlated with each other.

Question 17

Nonstationarity

Answer 17

A variable’s properties, such as mean and variance, are not constant through time.

Question 18

P-Value of Regression

Answer 18

The smallest level of significance at which we can reject a null hypothesis that the population value of the coefficient is 0, in a two-sided test.

Question 19

Multiple R-Squared to Multiple Regression

Answer 19

The percentage of the variation in the dependent variable that is explained by the model.

Question 20

Autoregressive Model (AR)

Answer 20

A time series regressed on its own past values.

Question 21

Covariance Stationary

Answer 21

A time series’ properties, such as the mean and the variance do not change over time. Assume in an autoregressive model to conduct a valid statistical reference.

Question 22

Random Walk

Answer 22

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

Question 23

Cointegrated

Answer 23

If a long-term financial or economic relationship exists between two time-series, such that they do not diverge from each other without bound in the long run.

Question 24

Out of Sample Error Forecasts

Answer 24

Refers to the difference between the realized value and the forecasted value for the dates beyond the estimation period.

Question 25

Calculating RMSE for Out of Sample Forecast Errors

Answer 25

1) Take the difference between the actual and the forecast errors 2) Square the error 3) Sum the squared errors 4) Divide by the number of forecasts 5) take the square root of the average

Question 26

The Hansen Procedure

Answer 26

Used to solve issues with Heteroskedasticity and serial correlation.

Question 27

Unit Root

Answer 27

The presence of a unit root means that the least squares regression procedure that we have been using to estimate an AR(1) model cannot be used without transforming the data first. Most likely to occur in time series that trend over time or have a seasonal element.