Question 1

In the equation,   is a(n) _____.

Accepted Answer

A) independent variable 
B) dependent variable 
C)  slope parameter 
D)  intercept parameter 
A) independent variable 
B) dependent variable 
C)  slope parameter 
D)  intercept parameter C

Question 2

Which of the following is true of BLUE?​

Accepted Answer

A) ​It is a rule that can be applied to any one value of the data to produce an estimate. 
B) ​An estimator   is an unbiased estimator of   if   for any   . 
C)  ​An estimator is linear if and only if it can be expressed as a linear function of the data on the dependent variable. 
D)  ​It is the best linear uniform estimator. 
A) ​It is a rule that can be applied to any one value of the data to produce an estimate. 
B) ​An estimator   is an unbiased estimator of   if   for any   . 
C)  ​An estimator is linear if and only if it can be expressed as a linear function of the data on the dependent variable. 
D)  ​It is the best linear uniform estimator. C

Question 3

A larger error variance makes it difficult to estimate the partial effect of any of the independent variables on the dependent variable.

Accepted Answer

A) True 
 B)False  True

Question 4

Suppose the variable   has been omitted from the following regression equation,     is the estimator obtained when   is omitted from the equation. The bias in   is negative if _____.

Accepted Answer

A) >0 and and are positively correlated B) <0 and and are positively correlated C) =0 and and are negatively correlated D) =0 and and are negatively correlated A) >0 and and are positively correlated B) <0 and and are positively correlated C) =0 and and are negatively correlated D) =0 and and are negatively correlated

Question 5

The term "linear" in a multiple linear regression model means that the equation is linear in parameters.

Accepted Answer

A) True 
 B)False

Question 6

High (but not perfect) correlation between two or more independent variables is called _____.

Accepted Answer

A) heteroskedasticty 
B) homoskedasticty 
C)  multicollinearity 
D)  micronumerosity 
A) heteroskedasticty 
B) homoskedasticty 
C)  multicollinearity 
D)  micronumerosity

Question 7

​When one randomly samples from a population, the total sample variation in xj decreases without bound as the sample size increases.

Accepted Answer

A) True 
 B)False

Question 8

The Gauss-Markov theorem will not hold if _____.

Accepted Answer

A) the error term has the same variance given any values of the explanatory variables 
B) the error term has an expected value of zero given any values of the independent variables 
C)  the independent variables have exact linear relationships among them 
D)  the regression model relies on the method of random sampling for collection of data 
A) the error term has the same variance given any values of the explanatory variables 
B) the error term has an expected value of zero given any values of the independent variables 
C)  the independent variables have exact linear relationships among them 
D)  the regression model relies on the method of random sampling for collection of data

Question 9

The value of R2 always _____.

Accepted Answer

A) lies below 0 
B) lies above 1 
C)  lies between 0 and 1 
D)  lies between 1 and 1.5 
A) lies below 0 
B) lies above 1 
C)  lies between 0 and 1 
D)  lies between 1 and 1.5

Question 10

If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model suffers from the problem of _____.

Accepted Answer

A) perfect collinearity 
B) homoskedasticity 
C)  heteroskedasticty 
D)  omitted variable bias 
A) perfect collinearity 
B) homoskedasticity 
C)  heteroskedasticty 
D)  omitted variable bias

Question 11

An explanatory variable is said to be exogenous if it is correlated with the error term.

Accepted Answer

A) True 
 B)False

Question 12

Exclusion of a relevant variable from a multiple linear regression model leads to the problem of _____.

Accepted Answer

A) misspecification of the model 
B) multicollinearity 
C)  perfect collinearity 
D)  homoskedasticity 
A) misspecification of the model 
B) multicollinearity 
C)  perfect collinearity 
D)  homoskedasticity

Question 13

Consider the following regression equation:   . What does   imply?

Accepted Answer

A)    measures the ceteris paribus effect of   on   . 
B)    measures the ceteris paribus effect of   on   . 
C) 
  measures the ceteris paribus effect of
  on
  . 
D) 
  measures the ceteris paribus effect of
  on
  . 
A)    measures the ceteris paribus effect of   on   . 
B)    measures the ceteris paribus effect of   on   . 
C) 
  measures the ceteris paribus effect of
  on
  . 
D) 
  measures the ceteris paribus effect of
  on
  .

Question 14

Suppose the variable   has been omitted from the following regression equation,     is the estimator obtained when   is omitted from the equation. If   is said to _____.

Accepted Answer

A) have an upward bias 
B) have a downward bias 
C)  be unbiased 
D)  be biased toward zero 
A) have an upward bias 
B) have a downward bias 
C)  be unbiased 
D)  be biased toward zero

Question 15

The key assumption for the general multiple regression model is that all factors in the unobserved error term be correlated with the explanatory variables.

Accepted Answer

A) True 
 B)False

Question 16

The coefficient of determination (R2) decreases when an independent variable is added to a multiple regression model.

Accepted Answer

A) True 
 B)False

Question 17

Find the degrees of freedom in a regression model that has 10 observations and 7 independent variables.

Accepted Answer

A) 17 
B) 2 
C)  3 
D)  4 
A) 17 
B) 2 
C)  3 
D)  4

Question 18

Suppose the variable x2 has been omitted from the following regression equation,     is the estimator obtained when   is omitted from the equation. The bias in   is positive if _____.

Accepted Answer

A)    >0 and   and    are positively correlated 
B)    0 and
  and
  are negatively correlated 
D) 
  = 0 and
  and
  are negatively correlated 
A)    >0 and   and    are positively correlated 
B)    0 and
  and
  are negatively correlated 
D) 
  = 0 and
  and
  are negatively correlated

Question 19

Which of the following is true of R2?

Accepted Answer

A) R2 is also called the standard error of regression. 
B) A low R2 indicates that the Ordinary Least Squares line fits the data well. 
C)  R2 usually decreases with an increase in the number of independent variables in a regression. 
D)  R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables. 
A) R2 is also called the standard error of regression. 
B) A low R2 indicates that the Ordinary Least Squares line fits the data well. 
C)  R2 usually decreases with an increase in the number of independent variables in a regression. 
D)  R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables.

Question 20

The assumption that there are no exact linear relationships among the independent variables in a multiple linear regression model fails if _____, where n is the sample size and k is the number of parameters.

Accepted Answer

A) n > 2 B) n = k + 1 C) n > k D) n < k + 1 A) n > 2 B) n = k + 1 C) n > k D) n < k + 1

In the equation, is a(n) _____.

Which of the following is true of BLUE?

A larger error variance makes it difficult to estimate the partial effect of any of the independent variables on the dependent variable.

Suppose the variable has been omitted from the following regression equation, is the estimator obtained when is omitted from the equation. The bias in is negative if _____.

The term "linear" in a multiple linear regression model means that the equation is linear in parameters.

High (but not perfect) correlation between two or more independent variables is called _____.

When one randomly samples from a population, the total sample variation in x_j decreases without bound as the sample size increases.

The Gauss-Markov theorem will not hold if _____.

The value of R² always _____.

If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model suffers from the problem of _____.

An explanatory variable is said to be exogenous if it is correlated with the error term.

Exclusion of a relevant variable from a multiple linear regression model leads to the problem of _____.

Consider the following regression equation: . What does imply?

Suppose the variable has been omitted from the following regression equation, is the estimator obtained when is omitted from the equation. If is said to _____.

The key assumption for the general multiple regression model is that all factors in the unobserved error term be correlated with the explanatory variables.

The coefficient of determination (R²) decreases when an independent variable is added to a multiple regression model.

Find the degrees of freedom in a regression model that has 10 observations and 7 independent variables.

Suppose the variable x2 has been omitted from the following regression equation, is the estimator obtained when is omitted from the equation. The bias in is positive if _____.

Which of the following is true of R²?

The assumption that there are no exact linear relationships among the independent variables in a multiple linear regression model fails if _____, where n is the sample size and k is the number of parameters.

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Inference

Multiple Regression Analysis: Ols Asymptotics

Multiple Regression Analysis: Further Issues

Multiple Regression Analysis With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time: Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 3: Multiple Regression Analysis: Estimation

In the equation, is a(n) _____.

Which of the following is true of BLUE?​

A larger error variance makes it difficult to estimate the partial effect of any of the independent variables on the dependent variable.

Suppose the variable has been omitted from the following regression equation, is the estimator obtained when is omitted from the equation. The bias in is negative if _____.

The term "linear" in a multiple linear regression model means that the equation is linear in parameters.

High (but not perfect) correlation between two or more independent variables is called _____.

​When one randomly samples from a population, the total sample variation in xj decreases without bound as the sample size increases.

The Gauss-Markov theorem will not hold if _____.

The value of R2 always _____.

If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model suffers from the problem of _____.

An explanatory variable is said to be exogenous if it is correlated with the error term.

Exclusion of a relevant variable from a multiple linear regression model leads to the problem of _____.

Consider the following regression equation: . What does imply?

Suppose the variable has been omitted from the following regression equation, is the estimator obtained when is omitted from the equation. If is said to _____.

The key assumption for the general multiple regression model is that all factors in the unobserved error term be correlated with the explanatory variables.

The coefficient of determination (R2) decreases when an independent variable is added to a multiple regression model.

Find the degrees of freedom in a regression model that has 10 observations and 7 independent variables.

Suppose the variable x2 has been omitted from the following regression equation, is the estimator obtained when is omitted from the equation. The bias in is positive if _____.

Which of the following is true of R2?

The assumption that there are no exact linear relationships among the independent variables in a multiple linear regression model fails if _____, where n is the sample size and k is the number of parameters.

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Inference

Multiple Regression Analysis: Ols Asymptotics

Multiple Regression Analysis: Further Issues

Multiple Regression Analysis With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time: Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Which of the following is true of BLUE?

When one randomly samples from a population, the total sample variation in x_j decreases without bound as the sample size increases.

The value of R² always _____.

The coefficient of determination (R²) decreases when an independent variable is added to a multiple regression model.

Which of the following is true of R²?