Question 1

Please explain the concept of consistency in OLS estimations.

Accepted Answer

An estimator is a consistent estimator if the distribution of B1hat shrinks closer and closer to the true value (B1 as the sample size increases (as we get more data, as long as the exogeneity condition holds true

Question 2

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 C

Question 3

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. B

Question 4

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 5

The higher the variance of X in our sample, the higher the variance of B1hat.
[Equation that helps understand the logic for the answer]

Accepted Answer

A) True 
 B)False

Question 6

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

Accepted Answer

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

Question 7

The residual for observation i is eihat = Yihat - Yi

Accepted Answer

A) True 
 B)False

Question 8

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

Accepted Answer

We square the residuals because we want

Question 9

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.

Question 10

The higher the correlation of the error term and the independent variable, the closer the expected value of B1hat is to the true value.
[Equation that helps understand the logic for the answer]

Accepted Answer

A) True 
 B)False

Question 11

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

Accepted Answer

A) True 
 B)False

Question 12

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

Question 13

Please look at the equation below and pick the best answer

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 14

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 15

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

Accepted Answer

Outliers are observations that are very

Question 16

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 exogeneity condition which

Question 17

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 18

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

Accepted Answer

A) True 
 B)False

Question 19

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

Accepted Answer

The two sources of r

Please explain the concept of consistency in OLS estimations.

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.

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:

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

The higher the variance of X in our sample, the higher the variance of B1hat. [Equation that helps understand the logic for the answer]

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

The residual for observation i is e_ihat = Y_ihat - Y_i

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

A residual measures:

The higher the correlation of the error term and the independent variable, the closer the expected value of B1hat is to the true value. [Equation that helps understand the logic for the answer]

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

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

Please look at the equation below and pick the best answer

An estimate of beta1 is said to be unbiased if

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

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

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

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

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

The Quest for Causality

Stats in the Wild: Good Data Practices

Hypothesis Testing and Interval Estimation: Answering Research

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects Models to Fight Endogeneity in Panel Data a

Instrumental Variables: Using Exogenous Variation to Fight Endogen

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

Please explain the concept of consistency in OLS estimations.

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.

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:

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

The higher the variance of X in our sample, the higher the variance of B1hat. [Equation that helps understand the logic for the answer]

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

The residual for observation i is eihat = Yihat - Yi

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

A residual measures:

The higher the correlation of the error term and the independent variable, the closer the expected value of B1hat is to the true value. [Equation that helps understand the logic for the answer]

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

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

Please look at the equation below and pick the best answer

An estimate of beta1 is said to be unbiased if

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

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

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

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

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

The Quest for Causality

Stats in the Wild: Good Data Practices

Hypothesis Testing and Interval Estimation: Answering Research

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects Models to Fight Endogeneity in Panel Data a

Instrumental Variables: Using Exogenous Variation to Fight Endogen

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

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.

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.