Question 1

The higher the variance of X in our sample, the higher the variance of B1hat.

Accepted Answer

A) True 
 B)False  False

Question 2

Which of the following are used to describe the goodness of fit for a model?

Accepted Answer

A)  The coefficient of the regression 
B)  R2  
C)  The standard error of the coefficient 
D)  The constant 
A)  The coefficient of the regression 
B)  R2  
C)  The standard error of the coefficient 
D)  The constant B

Question 3

A residual measures:

Accepted Answer

A)  The unobservable error term, $\varepsilon$. 
B)  The distance between the actual value and the predicted value. 
C)  The distance between $\beta$-hat and the actual value of $\beta$. 
D)  The expected value of the dependent variable. 
A)  The unobservable error term, $\varepsilon$. 
B)  The distance between the actual value and the predicted value. 
C)  The distance between $\beta$-hat and the actual value of $\beta$. 
D)  The expected value of the dependent variable. B

Question 4

Please list and give a short description of the two sources of randomness in the coefficient estimates.

Accepted Answer

The answer of Please list and give a short description...

Question 5

The residual for observation i is eihat = Yihat - Yi

Accepted Answer

A) True 
 B)False

Question 6

The probability a continuous random variable is near some value is defined by its probability density function.

Accepted Answer

A) True 
 B)False

Question 7

Please look at the equation below and pick the best answer $$\hat { \beta } _ { 1 } = \frac { \sum _ { i = 1 } ^ { N } \left( X _ { i } - \bar { X } \right) \left( Y _ { i } - \bar { Y } \right) } { \sum _ { i = 1 } ^ { N } \left( X _ { i } - \bar { X } \right) ^ { 2 } }$$

Accepted Answer

A)  B1hat will be positive if Y tends to be above its mean when X tends to be above its mean. 
B)  The sign of B1 will be the same sign as the denominator. 
C)  Xi can never equal X-bar for any observation 
D)  B1hat will be positive if Xi is always above X-bar. 
A)  B1hat will be positive if Y tends to be above its mean when X tends to be above its mean. 
B)  The sign of B1 will be the same sign as the denominator. 
C)  Xi can never equal X-bar for any observation 
D)  B1hat will be positive if Xi is always above X-bar.

Question 8

Please explain why residuals need to be squared in process of generating OLS coefficients.

Accepted Answer

The answer of Please explain why residuals need to be...

Question 9

Violating the homoscedasticity condition will cause our OLS estimates of $\beta$1hat to be biased.

Accepted Answer

A) True 
 B)False

Question 10

Outliers are a bigger problem when we have a smaller sample size than when we have a bigger sample size.

Accepted Answer

A) True 
 B)False

Question 11

Name the concept: The variance of ei is the same for every observation.

Accepted Answer

A)  Homoscedasticity 
B)  Heteroscedasticity 
C)  Consistency 
D)  Bias 
A)  Homoscedasticity 
B)  Heteroscedasticity 
C)  Consistency 
D)  Bias

Question 12

OLS does not automatically produce unbiased estimates. Please briefly explain the condition that must be satisfied for OLS to produce unbiased estimates.

Accepted Answer

The answer of OLS does not automatically produce unbiased estimates....

Question 13

Suppose we estimate a model in which salary in thousands of Euros is the dependent variable and years of education is the independent variable. We get a result that says Income-hat = 20 + 2 Years Education. Which of the following is correct:

Accepted Answer

A)  Two years of education increases income by one. 
B)  An extra year of education is expected to increase income by 2. 
C)  An individual with 10 years of education is expected to have an income of 20. 
D)  The expected income for someone with 0 years of education is 2. 
A)  Two years of education increases income by one. 
B)  An extra year of education is expected to increase income by 2. 
C)  An individual with 10 years of education is expected to have an income of 20. 
D)  The expected income for someone with 0 years of education is 2.

Question 14

Imagine we have two separate models, Model 1 and Model 2. The R2 for Model 1 is 0.8 and the R2 for Model 2 is 0.4.

Accepted Answer

A)  Model 1 is a better model than Model 2 
B)  Model 2 is a better model than Model 1 
C)  R2 is neither necessary nor sufficient for analysis to be useful 
A)  Model 1 is a better model than Model 2 
B)  Model 2 is a better model than Model 1 
C)  R2 is neither necessary nor sufficient for analysis to be useful

Question 15

Please briefly describe the concept of an outlier and explain some strategies for dealing with outliers.

Accepted Answer

The answer of Please briefly describe the concept of an...

Question 16

The higher the correlation of the error term and the independent variable, the closer the expected value of B1hat is to the true value.

Accepted Answer

A) True 
 B)False

Question 17

In OLS with a large sample, the coefficient estimates will be:

Accepted Answer

A)  Correct 
B)  Normally distributed. 
C)  Centered on zero. 
D)  Correct. 
A)  Correct 
B)  Normally distributed. 
C)  Centered on zero. 
D)  Correct.

Question 18

An estimate of beta1 is said to be unbiased if

Accepted Answer

A)  Beta1-hat is normally distributed 
B)  The coefficient distribution is narrow. 
C)  The average value of the distribution of B1-hat is equal to the true value. 
D)  The coefficient distribution is wide. 
A)  Beta1-hat is normally distributed 
B)  The coefficient distribution is narrow. 
C)  The average value of the distribution of B1-hat is equal to the true value. 
D)  The coefficient distribution is wide.

Question 19

Please explain the concept of consistency in OLS estimations.

Accepted Answer

The answer of Please explain the concept of consistency in...

The higher the variance of X in our sample, the higher the variance of B1hat.

Which of the following are used to describe the goodness of fit for a model?

A residual measures:

Please list and give a short description of the two sources of randomness in the coefficient estimates.

The residual for observation i is e_ihat = Y_ihat - Y_i

The probability a continuous random variable is near some value is defined by its probability density function.

Please look at the equation below and pick the best answer $\hat { \beta } _ { 1 } = \frac { \sum _ { i = 1 } ^ { N } \left( X _ { i } - \bar { X } \right) \left( Y _ { i } - \bar { Y } \right) } { \sum _ { i = 1 } ^ { N } \left( X _ { i } - \bar { X } \right) ^ { 2 } }$

Please explain why residuals need to be squared in process of generating OLS coefficients.

Violating the homoscedasticity condition will cause our OLS estimates of $\beta$ ₁hat to be biased.

Outliers are a bigger problem when we have a smaller sample size than when we have a bigger sample size.

Name the concept: The variance of e_iis the same for every observation.

OLS does not automatically produce unbiased estimates. Please briefly explain the condition that must be satisfied for OLS to produce unbiased estimates.

Suppose we estimate a model in which salary in thousands of Euros is the dependent variable and years of education is the independent variable. We get a result that says Income-hat = 20 + 2 Years Education. Which of the following is correct:

Imagine we have two separate models, Model 1 and Model 2. The R²for Model 1 is 0.8 and the R²for Model 2 is 0.4.

Please briefly describe the concept of an outlier and explain some strategies for dealing with outliers.

The higher the correlation of the error term and the independent variable, the closer the expected value of B1hat is to the true value.

In OLS with a large sample, the coefficient estimates will be:

An estimate of beta1 is said to be unbiased if

Please explain the concept of consistency in OLS estimations.

The Quest for Causality

Stats in the Wild: Good Data Practices

Hypothesis Testing and Interval Estimation: Answering Research Questions

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects to Fight Endogeneity in Panel Data and Difference-In-Difference Models

Instrumental Variables: Using Exogenous Variation to Fight Endogeneity

Experiments: Dealing With Real-World Challenges

Regression Discontinuity: Looking for Jumps in Data

Dummy Dependent Variables

Time Series: Dealing With Stickiness Over Time

Advanced Ols

Advanced Panel Data

Filters

Exam 3: Bivariate Ols: the Foundation of Econometric Analysis

The higher the variance of X in our sample, the higher the variance of B1hat.

Which of the following are used to describe the goodness of fit for a model?

A residual measures:

Please list and give a short description of the two sources of randomness in the coefficient estimates.

The residual for observation i is eihat = Yihat - Yi

The probability a continuous random variable is near some value is defined by its probability density function.

Please explain why residuals need to be squared in process of generating OLS coefficients.

Violating the homoscedasticity condition will cause our OLS estimates of β\betaβ 1hat to be biased.

Outliers are a bigger problem when we have a smaller sample size than when we have a bigger sample size.

Name the concept: The variance of ei is the same for every observation.

OLS does not automatically produce unbiased estimates. Please briefly explain the condition that must be satisfied for OLS to produce unbiased estimates.

Suppose we estimate a model in which salary in thousands of Euros is the dependent variable and years of education is the independent variable. We get a result that says Income-hat = 20 + 2 Years Education. Which of the following is correct:

Imagine we have two separate models, Model 1 and Model 2. The R2 for Model 1 is 0.8 and the R2 for Model 2 is 0.4.

Please briefly describe the concept of an outlier and explain some strategies for dealing with outliers.

The higher the correlation of the error term and the independent variable, the closer the expected value of B1hat is to the true value.

In OLS with a large sample, the coefficient estimates will be:

An estimate of beta1 is said to be unbiased if

Please explain the concept of consistency in OLS estimations.

The Quest for Causality

Stats in the Wild: Good Data Practices

Hypothesis Testing and Interval Estimation: Answering Research Questions

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects to Fight Endogeneity in Panel Data and Difference-In-Difference Models

Instrumental Variables: Using Exogenous Variation to Fight Endogeneity

Experiments: Dealing With Real-World Challenges

Regression Discontinuity: Looking for Jumps in Data

Dummy Dependent Variables

Time Series: Dealing With Stickiness Over Time

Advanced Ols

Advanced Panel Data

Filters

The residual for observation i is e_ihat = Y_ihat - Y_i

Violating the homoscedasticity condition will cause our OLS estimates of $\beta$ ₁hat to be biased.

Name the concept: The variance of e_iis the same for every observation.

Imagine we have two separate models, Model 1 and Model 2. The R²for Model 1 is 0.8 and the R²for Model 2 is 0.4.