Question 1

Given the model Income = 10,000 + 1,000YearsofExperience + 100YearsofExperience2, a one year increase in years of experience from 10 years is expected to lead to a:

Accepted Answer

A)  1,100 increase in income 
B)  1,000 increase in income 
C)  3,000 increase in income 
D)  11,100 increase in income 
A)  1,100 increase in income 
B)  1,000 increase in income 
C)  3,000 increase in income 
D)  11,100 increase in income C

Question 2

Given the following results
Life expectancy = 1+2GDP - 0.01GDP2
where GDP is in thousands (meaning a GDP of $60 signifies a GDP of $60,000), answer the following:
a.The predicted increase in life expectancy of GDP increase by $1 from $40.
b. The predicted increase in life expectancy if GDP increase by $1 from $100.
c. The predicted life expectancy in a country with a GDP of 50.
d. Give a simple explanation for the use of a polynomial model in order to model the relationship between life expectancy and GDP.

Accepted Answer

a. (1.2)
b. (0)
c. (76)
d. (Non-linear relationship between GDP and life -expectancy)

Question 3

Describe the challenge faced when it comes to comparing the effects of variables with different units, and describe how one can deal with this challenge.

Accepted Answer

Need to standardize the variables by converting the variables to standard deviations from their means

Question 4

Which of the following models should be used if we want to estimate the relationship between years of education and income if we expect the relationship to be non-linear.

Accepted Answer

A)  Income=$\beta$0+B1Education 
B)  Income =B0+B12Education 
C)  Income =B0+B1Education + B12Education 
D)  Income =B0+B1Education +B1Education2 
A)  Income=$\beta$0+B1Education 
B)  Income =B0+B12Education 
C)  Income =B0+B1Education + B12Education 
D)  Income =B0+B1Education +B1Education2

Question 5

Explain how to conduct F-tests in both of the possible scenarios, describing both the purpose of the F-test and the criteria for rejecting the null hypothesis.

Accepted Answer

The answer of Explain how to conduct F-tests in both...

Question 6

When conducting an F-test, the unrestricted model is the one that includes all of the independent variables that are in the full model, while the restricted model include only the variables that conform with our null hypothesis.

Accepted Answer

A) True 
 B)False

Question 7

In a linear log model with only one independent variable, we interpret as a 1% increase in X1 is expected to lead to a $\beta$1 change in Y.

Accepted Answer

A) True 
 B)False

Question 8

Given log linear model that says Ln Income =B0+B1Education, we interpret the results as:

Accepted Answer

A)  A one year increase in education is expected to lead to a B1 change in income. 
B)  A one year increase in education is expected to lead to a B1% change in income. 
C)  A one year increase in education is expected to lead to a B1/100 change in income. 
D)  A one percent increase in education is expected to lead to a B1/100 change in income 
A)  A one year increase in education is expected to lead to a B1 change in income. 
B)  A one year increase in education is expected to lead to a B1% change in income. 
C)  A one year increase in education is expected to lead to a B1/100 change in income. 
D)  A one percent increase in education is expected to lead to a B1/100 change in income

Question 9

Given a log log model lnYi=B0B1lnXi, we interpret the results as:

Accepted Answer

A)  A one unit increase in X is expected to lead to a B1 change in Yi. 
B)  A one unit increase in X is expected to lead to a B1% change in Yi. 
C)  A one unit increase in X is expected to lead to a B1/100 change in Yi. 
D)  A one percent increase in X is expected to lead to a B1 percent change in Yi. 
A)  A one unit increase in X is expected to lead to a B1 change in Yi. 
B)  A one unit increase in X is expected to lead to a B1% change in Yi. 
C)  A one unit increase in X is expected to lead to a B1/100 change in Yi. 
D)  A one percent increase in X is expected to lead to a B1 percent change in Yi.

Question 10

Given a model where the variables are on a different scale, in order to make them comparable we need to:

Accepted Answer

A)  Standardize the model by dividing the difference of the variable from its average by its standard deviation. 
B)  Don't need to do anything and can run the model in its unaltered form. 
C)  Standardize the model by taking the log/ln of each variable. 
D)  Standardize the model by dividing the coefficient of each variable by its standard deviation. 
A)  Standardize the model by dividing the difference of the variable from its average by its standard deviation. 
B)  Don't need to do anything and can run the model in its unaltered form. 
C)  Standardize the model by taking the log/ln of each variable. 
D)  Standardize the model by dividing the coefficient of each variable by its standard deviation.

Question 11

Given Yi = B0 + B1X1 + B2X2 + B3X3 + B4X4 + ei, the restricted model for an F-test where H0: B1= B2= B4=0 is:

Accepted Answer

A)  Yi = B0 + B1X1 + B2X2 + B3X3 + B4X4 + ei 
B)  Yi = B0 + B1X1 + B2X2 + B4X4 + ei 
C)  Yi = B0 + B3X3 + B4X4 + ei 
D)  Yi = B0 + B3X3 + ei 
A)  Yi = B0 + B1X1 + B2X2 + B3X3 + B4X4 + ei 
B)  Yi = B0 + B1X1 + B2X2 + B4X4 + ei 
C)  Yi = B0 + B3X3 + B4X4 + ei 
D)  Yi = B0 + B3X3 + ei

Question 12

The following is an example of a polynomial OLS model.
 $$Y _ { i } = \beta _ { 0 } + \beta _ { 1 } ^ { 2 } X _ { 1 i } + \epsilon _ { i }$$

Accepted Answer

A) True 
 B)False

Question 13

Given Yi = B0 + B1X1 + B2X2 + B3X3 + B4X4 + ei, the restricted model for an F-test where H0: B1= B2= B3 is:

Accepted Answer

A)  Yi=B0+B1X1+B2X2+B3X3+B4X4+ei 
B)  Yi=B0+B1(X1+ X2+ X3) + B4X4 + ei 
C)  Yi=B0+B4X4+ei 
D)  Yi=B0+B1X1+B2X2+B3X3 +ei 
A)  Yi=B0+B1X1+B2X2+B3X3+B4X4+ei 
B)  Yi=B0+B1(X1+ X2+ X3) + B4X4 + ei 
C)  Yi=B0+B4X4+ei 
D)  Yi=B0+B1X1+B2X2+B3X3 +ei

Question 14

Given the model Income = 20,000 + 1,500YearsofExperience + 150YearsofExperience2 - 10 YearsofExperience3, a one year increase in years of experience from 10 years is expected to lead to a:

Accepted Answer

A)  1,500 increase in income 
B)  1,650 increase in income 
C)  1,800 increase in income 
D)  1,770 increase in income 
E)  None of the above, need more information 
A)  1,500 increase in income 
B)  1,650 increase in income 
C)  1,800 increase in income 
D)  1,770 increase in income 
E)  None of the above, need more information

Question 15

When variables are not on the same scale, it makes it harder to compare them with each other. To deal with this problem, we standardize the variables by logging the variables.

Accepted Answer

A) True 
 B)False

Question 16

Explain how one can use OLS in order to estimate non-linear effects, and describe what has to be done with the data in order to do so.

Accepted Answer

The answer of Explain how one can use OLS in...

Question 17

Describe the appropriate interpretation of the following log models - specify the values:
a. lnYi=0.5+0.33Xi
b. Yi=2300+450ln Xi 
c. lnYi=4.5+17 ln Xi

Accepted Answer

Question 18

Given the model Yi=20+30X1i+5X2i2, a one unit increase in X1i will lead to 40 unit increase in Yi.

Accepted Answer

A) True 
 B)False

Question 19

One of the main reasons for using a polynomial OLS model is:

Accepted Answer

A)  In order to estimate non-linear relationships. 
B)  In order to estimate linear relationships. 
C)  In order to account for measurement error. 
D)  In order to estimate a model where we suspect there is a measurement error in both the independent and dependent variables. 
A)  In order to estimate non-linear relationships. 
B)  In order to estimate linear relationships. 
C)  In order to account for measurement error. 
D)  In order to estimate a model where we suspect there is a measurement error in both the independent and dependent variables.

Given the model Income = 10,000 + 1,000YearsofExperience + 100YearsofExperience², a one year increase in years of experience from 10 years is expected to lead to a:

Describe the challenge faced when it comes to comparing the effects of variables with different units, and describe how one can deal with this challenge.

Which of the following models should be used if we want to estimate the relationship between years of education and income if we expect the relationship to be non-linear.

Explain how to conduct F-tests in both of the possible scenarios, describing both the purpose of the F-test and the criteria for rejecting the null hypothesis.

When conducting an F-test, the unrestricted model is the one that includes all of the independent variables that are in the full model, while the restricted model include only the variables that conform with our null hypothesis.

In a linear log model with only one independent variable, we interpret as a 1% increase in X₁ is expected to lead to a $\beta$ ₁ change in Y.

Given log linear model that says Ln Income =B₀₊B₁Education, we interpret the results as:

Given a log log model lnY_i=B₀B₁lnX_i, we interpret the results as:

Given a model where the variables are on a different scale, in order to make them comparable we need to:

Given Y_i= B₀+ B₁X₁+ B₂X₂+ B₃X₃+ B₄X₄+ e_i, the restricted model for an F-test where H₀: B₁= B₂= B₄=0 is:

The following is an example of a polynomial OLS model. $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } ^ { 2 } X _ { 1 i } + \epsilon _ { i }$

Given Y_i= B₀+ B₁X₁+ B₂X₂+ B₃X₃+ B₄X₄+ e_i, the restricted model for an F-test where H₀: B₁= B₂= B₃ is:

Given the model Income = 20,000 + 1,500YearsofExperience + 150YearsofExperience²- 10 YearsofExperience³, a one year increase in years of experience from 10 years is expected to lead to a:

When variables are not on the same scale, it makes it harder to compare them with each other. To deal with this problem, we standardize the variables by logging the variables.

Explain how one can use OLS in order to estimate non-linear effects, and describe what has to be done with the data in order to do so.

Describe the appropriate interpretation of the following log models - specify the values: a. lnY_i=0.5+0.33X_i b. Y_i=2300+450ln X_i c. lnY_i=4.5+17 ln X_i

Given the model Y_i=20+30X_1i+5X_2i², a one unit increase in X₁_iwill lead to 40 unit increase in Y_i.

One of the main reasons for using a polynomial OLS model is:

The Quest for Causality

Stats in the Wild: Good Data Practices

Bivariate Ols: the Foundation of Econometric Analysis

Hypothesis Testing and Interval Estimation: Answering Research Questions

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

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 7: Specifying Models

Given the model Income = 10,000 + 1,000YearsofExperience + 100YearsofExperience2, a one year increase in years of experience from 10 years is expected to lead to a:

Describe the challenge faced when it comes to comparing the effects of variables with different units, and describe how one can deal with this challenge.

Which of the following models should be used if we want to estimate the relationship between years of education and income if we expect the relationship to be non-linear.

Explain how to conduct F-tests in both of the possible scenarios, describing both the purpose of the F-test and the criteria for rejecting the null hypothesis.

When conducting an F-test, the unrestricted model is the one that includes all of the independent variables that are in the full model, while the restricted model include only the variables that conform with our null hypothesis.

In a linear log model with only one independent variable, we interpret as a 1% increase in X1 is expected to lead to a β\betaβ 1 change in Y.

Given log linear model that says Ln Income =B0+B1Education, we interpret the results as:

Given a log log model lnYi=B0B1lnXi, we interpret the results as:

Given a model where the variables are on a different scale, in order to make them comparable we need to:

Given Yi = B0 + B1X1 + B2X2 + B3X3 + B4X4 + ei, the restricted model for an F-test where H0: B1= B2= B4=0 is:

The following is an example of a polynomial OLS model. Yi=β0+β12X1i+ϵiY _ { i } = \beta _ { 0 } + \beta _ { 1 } ^ { 2 } X _ { 1 i } + \epsilon _ { i }Yi​=β0​+β12​X1i​+ϵi​

Given Yi = B0 + B1X1 + B2X2 + B3X3 + B4X4 + ei, the restricted model for an F-test where H0: B1= B2= B3 is:

Given the model Income = 20,000 + 1,500YearsofExperience + 150YearsofExperience2 - 10 YearsofExperience3, a one year increase in years of experience from 10 years is expected to lead to a:

When variables are not on the same scale, it makes it harder to compare them with each other. To deal with this problem, we standardize the variables by logging the variables.

Explain how one can use OLS in order to estimate non-linear effects, and describe what has to be done with the data in order to do so.

Describe the appropriate interpretation of the following log models - specify the values: a. lnYi=0.5+0.33Xi b. Yi=2300+450ln Xi c. lnYi=4.5+17 ln Xi

Given the model Yi=20+30X1i+5X2i2, a one unit increase in X1i will lead to 40 unit increase in Yi.

One of the main reasons for using a polynomial OLS model is:

The Quest for Causality

Stats in the Wild: Good Data Practices

Bivariate Ols: the Foundation of Econometric Analysis

Hypothesis Testing and Interval Estimation: Answering Research Questions

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

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

Given the model Income = 10,000 + 1,000YearsofExperience + 100YearsofExperience², a one year increase in years of experience from 10 years is expected to lead to a:

In a linear log model with only one independent variable, we interpret as a 1% increase in X₁ is expected to lead to a $\beta$ ₁ change in Y.

Given log linear model that says Ln Income =B₀₊B₁Education, we interpret the results as:

Given a log log model lnY_i=B₀B₁lnX_i, we interpret the results as:

Given Y_i= B₀+ B₁X₁+ B₂X₂+ B₃X₃+ B₄X₄+ e_i, the restricted model for an F-test where H₀: B₁= B₂= B₄=0 is:

The following is an example of a polynomial OLS model. $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } ^ { 2 } X _ { 1 i } + \epsilon _ { i }$

Given Y_i= B₀+ B₁X₁+ B₂X₂+ B₃X₃+ B₄X₄+ e_i, the restricted model for an F-test where H₀: B₁= B₂= B₃ is:

Given the model Income = 20,000 + 1,500YearsofExperience + 150YearsofExperience²- 10 YearsofExperience³, a one year increase in years of experience from 10 years is expected to lead to a:

Describe the appropriate interpretation of the following log models - specify the values: a. lnY_i=0.5+0.33X_i b. Y_i=2300+450ln X_i c. lnY_i=4.5+17 ln X_i

Given the model Y_i=20+30X_1i+5X_2i², a one unit increase in X₁_iwill lead to 40 unit increase in Y_i.