Econometrics

Question 1

Dominant Variable

Answer 1

Variable so highly correlated with the dependent variable that it completely mask the effects of all other independent variable in the equation

Question 2

Variable so highly correlated with the dependent variable that it completely mask the effects of all other independent variable in the equation

Answer 2

Dominant Variable

Question 3

Imperfect Multi-Collinearity

Answer 3

Linear functional relationship between two or more independent variable that is so strong that it can significantly affect the estimation of the coefficient of the variables.

Question 4

Linear functional relationship between two or more independent variable that is so strong that it can significantly affect the estimation of the coefficient of the variables.

Answer 4

Imperfect Multi-Collinearity

Question 5

Perfect Multicollinearity

Answer 5

Violates the classical assumption 6. No explanatory variable is a perfect linear function of any other explanatory variable - each explanatory variable perfectly explains another explanatory variable

Question 6

Violates the classical assumption 6. No explanatory variable is a perfect linear function of any other explanatory variable - each explanatory variable perfectly explains another explanatory variable

Answer 6

Perfect Multicollinearity

Question 7

First-Order Auto-Correlation Coefficient

Answer 7

measures the functional relationship between the value of an observation of the error term and value of previous observation of the error term.

Question 8

measures the functional relationship between the value of an observation of the error term and value of previous observation of the error term.

Answer 8

First-Order Auto-Correlation Coefficient

Question 9

Positive-Serial Correlation

Answer 9

A positive value for p implies error term tend to have the same sign from one time period to the next

Question 10

A positive value for p implies error term tend to have the same sign from one time period to the next

Answer 10

Positive-Serial Correlation

Question 11

Classical Assumption IV

Answer 11

If expected value of sample correlation coefficient between any two observations of the error term is not equal to zero, then error term said to be serially correlated.

Question 12

If expected value of sample correlation coefficient between any two observations of the error term is not equal to zero, then error term said to be serially correlated.

Answer 12

Classical Assumption IV

Question 13

Pure-Serial Correlation

Answer 13

Classical Assumption IV (which assumes uncorrelated observation of the error term in a correctly specified equation) is violated.

Question 14

Impure-Serial Correlation

Answer 14

serial correlation that is caused by a specification error as an omitted variable or an incorrect functional form

Question 15

serial correlation that is caused by a specification error as an omitted variable or an incorrect functional form

Answer 15

Impure-Serial Correlation

Question 16

Durbin-Watson d Statistic

Answer 16

test for serial correlation - (if there is a first order serial correlation in the error term of an equation by examining residuals of a particular estimation of that equation)

Question 17

test for serial correlation - (if there is a first order serial correlation in the error term of an equation by examining residuals of a particular estimation of that equation)

Answer 17

Durbin-Watson d Statistic

Question 18

Generalized Least Square

Answer 18

method of ridding an equation of pure first order serial correlation and in the process restoring the minimum variance property to its estimation

Question 19

method of ridding an equation of pure first order serial correlation and in the process restoring the minimum variance property to its estimation

Answer 19

Generalized Least Square

Question 20

Heteroskedascity

Answer 20

Violation of Classical Assumption V ( observation of the error term are drawn from a distribution that has a constant variance) => non constant variance

Question 21

Violation of Classical Assumption V ( observation of the error term are drawn from a distribution that has a constant variance) => non constant variance

Answer 21

Heteroskedascity

Question 22

Variance Inflation Factor (VIF) process

Answer 22

Step 1.) Run OLS regression as a function of Xi
Step 2.) Xi = a1 + a2(X(i+1)) + a3(X(i+2))….
Step 3.) Use VIF formula for each auxiliary regression

Question 23

Step 1.) Run OLS regression as a function of Xi
Step 2.) Xi = a1 + a2(X(i+1)) + a3(X(i+2))….
Step 3.) Use VIF formula for each auxiliary regression

Answer 23

Variance Inflation Factor (VIF) process