Question 1

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 two cases are 1 When we suspect that multiple coefficients are equal to zero and 2 When we specify a null hypothesis that states that two or more coefficients are equal to each other Need to compare the F-statistics to the critical value to determine to reject The larger the difference between the restricted and unrestricted R values, the more likely we are to reject the null hypothesis

Question 2

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  False

Question 3

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  True

Question 4

The following is an example of a polynomial OLS model.

Accepted Answer

A) True 
 B)False

Question 5

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

There are cases when we will have a non-

Question 6

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

Question 7

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

Question 8

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.

Question 9

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 variab

Question 10

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 11

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 12

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 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 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 15

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

a. (A one unit increase in X is associat

Question 16

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 17

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 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

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

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 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.

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.

The following is an example of a polynomial OLS model.

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.

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:

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

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.

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

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

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:

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

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

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

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.

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

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.

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.

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

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

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 7: Specifying Models

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 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.

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.

The following is an example of a polynomial OLS model.

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.

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:

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

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.

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

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

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:

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

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

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

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.

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

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

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.

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

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

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

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:

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

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

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:

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

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

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.

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.