Question 1

Imperfect multicollinearity is when:

Accepted Answer

A)  two or more independent variables have nearly an exact linear relationship. 
B)  two independent variables are nearly orthogonal. 
C)  the r-squared coefficient is nearly zero. 
D)  the coefficients on two independent variables are nearly equal. 
A)  two or more independent variables have nearly an exact linear relationship. 
B)  two independent variables are nearly orthogonal. 
C)  the r-squared coefficient is nearly zero. 
D)  the coefficients on two independent variables are nearly equal.

Question 2

Determining whether a parameter is identified exclusively involves an argument about the data-generating process of the:

Accepted Answer

A)  observations in the sample. 
B)  observations in a representative sample. 
C)  total population. 
D)  control group. 
A)  observations in the sample. 
B)  observations in a representative sample. 
C)  total population. 
D)  control group.

Question 3

Which of the following functional forms will be the most restrictive in terms of the shape of the resulting interpolation of data gaps in a sample data set?

Accepted Answer

A)  Linear 
B)  Quadratic 
C)  Cubic 
D)  Piecewise linear 
A)  Linear 
B)  Quadratic 
C)  Cubic 
D)  Piecewise linear

Question 4

For a parameter that is identified, what should happen to the 95% confidence interval as the sample size gets larger?

Accepted Answer

A)  The right tail should get larger. 
B)  The left tail should get larger. 
C)  The width of the interval should get smaller. 
D)  The width of the interval should get wider. 
A)  The right tail should get larger. 
B)  The left tail should get larger. 
C)  The width of the interval should get smaller. 
D)  The width of the interval should get wider.

Question 5

The two critical elements required to sign the bias of an omitted variable include the sign of the:

Accepted Answer

A)  effect of the omitted variable on the outcome and the sign of the effect of the treatment variable on the outcome. 
B)  effect of the omitted variable on the outcome and the sign of the correlation between the omitted variable and the outcome. 
C)  effect of the omitted variable on the outcome and the sign of the correlation between the omitted variable and the treatment. 
D)  correlation between the omitted variable and the outcome and the sign of the correlation between the treatment variable and the outcome. 
A)  effect of the omitted variable on the outcome and the sign of the effect of the treatment variable on the outcome. 
B)  effect of the omitted variable on the outcome and the sign of the correlation between the omitted variable and the outcome. 
C)  effect of the omitted variable on the outcome and the sign of the correlation between the omitted variable and the treatment. 
D)  correlation between the omitted variable and the outcome and the sign of the correlation between the treatment variable and the outcome.

Question 6

Suppose you have many observations of the price of refrigerators, with other characteristics including their energy cost as well as whether or not they are stainless steel. Now, suppose you ran the standard linear regression of price on energy cost: Pricei = β0 + β1 Energy Costi + Ui and got an estimate of β1. Now, suppose you know that stainless steel refrigerators are more popular (i.e., sell for higher prices holding all other characteristics constant) and tend to be in more energy efficient (i.e., lower energy cost) refrigerator models. What should you expect your estimate of β1 in the following regression Pricei = β0* + β1*Energy Costi + β2* Stainless Steeli + Ui to be?

Accepted Answer

A)  β1* 1 
B)  β1* > β1 
C)  β1* = β1 
D)  You cannot tell from the information given. 
A)  β1* 1 
B)  β1* > β1 
C)  β1* = β1 
D)  You cannot tell from the information given.

Question 7

In the event that you are collecting data on how heights relate to income in the U.S. population and you notice that in your sample of 1,500 individuals you have a data gap at 5'6", will this cause an identification problem?

Accepted Answer

A)  No, because this data gap does not exist in the population. 
B)  Yes, but you can still interpolate the data gap. 
C)  Yes, but you can still extrapolate the data gap. 
D)  No, because data gaps cannot cause identification problems. 
A)  No, because this data gap does not exist in the population. 
B)  Yes, but you can still interpolate the data gap. 
C)  Yes, but you can still extrapolate the data gap. 
D)  No, because data gaps cannot cause identification problems.

Question 8

In the event that there is not an acceptable model of the data-generating process within which the treatment effect is identified using samples from the population, the analyst should do what?

Accepted Answer

A)  Collect more data. 
B)  Re-run the experiment. 
C)  Consider alternative data populations (e.g., additional variables) before attempting to estimate the effect. 
D)  Run a probit/logit model. 
A)  Collect more data. 
B)  Re-run the experiment. 
C)  Consider alternative data populations (e.g., additional variables) before attempting to estimate the effect. 
D)  Run a probit/logit model.

Question 9

How does collecting more/alternative data put less burden on the modeling choices of the analyst?

Accepted Answer

A)  By collecting more data, an analyst can completely avoid characterizing a determining function. 
B)  By collecting more data, an analyst can avoid heteroscedasticity. 
C)  By collecting more data, an analyst can avoid having to make functional form choices to fill in data gaps/extent of the data. 
D)  By collecting more data, an analyst can implement a fixed effects design. 
A)  By collecting more data, an analyst can completely avoid characterizing a determining function. 
B)  By collecting more data, an analyst can avoid heteroscedasticity. 
C)  By collecting more data, an analyst can avoid having to make functional form choices to fill in data gaps/extent of the data. 
D)  By collecting more data, an analyst can implement a fixed effects design.

Question 10

In the event that your treatment variable is imperfectly multicollinear with one of your control variables, a possible remedy would be to:

Accepted Answer

A)  re-orient your base group. 
B)  use the within estimator. 
C)  gather more data, in hopes of getting more independent variation of the treatment. 
D)  use data reduction methods. 
A)  re-orient your base group. 
B)  use the within estimator. 
C)  gather more data, in hopes of getting more independent variation of the treatment. 
D)  use data reduction methods.

Question 11

Potential remedies for when your model suffers from the endogeneity problem include all of the following except what?

Accepted Answer

A)  Gather (additional) data on a possible instrumental variable. 
B)  Gather (additional) longitudinal data to allow for a fixed effect approach. 
C)  Gather (additional) control variables that would limit the endogeneity concern. 
D)  Check if the residuals from your regression are uncorrelated with your treatment. 
A)  Gather (additional) data on a possible instrumental variable. 
B)  Gather (additional) longitudinal data to allow for a fixed effect approach. 
C)  Gather (additional) control variables that would limit the endogeneity concern. 
D)  Check if the residuals from your regression are uncorrelated with your treatment.

Question 12

Which of the following settings constitutes an extrapolation?

Accepted Answer

A)  Using past sales and price information to make a forecast for next year's sales growth. 
B)  Using past sales and price information to estimate last year's price elasticity of demand. 
C)  Using 2014 and 2016 data on GDP to estimate GDP in the third quarter of 2015, which is missing. 
D)  Using a randomized control trial to estimate the causal effect of the use of a cancer treatment on health outcomes. 
A)  Using past sales and price information to make a forecast for next year's sales growth. 
B)  Using past sales and price information to estimate last year's price elasticity of demand. 
C)  Using 2014 and 2016 data on GDP to estimate GDP in the third quarter of 2015, which is missing. 
D)  Using a randomized control trial to estimate the causal effect of the use of a cancer treatment on health outcomes.

Question 13

The key distinction between extrapolation and interpolation is that interpolation:

Accepted Answer

A)  involves causal estimates, while extrapolation involves correlations. 
B)  involves linear functional forms, while extrapolation involves non-linear function forms. 
C)  fills data gaps, while extrapolation fills beyond the extent of the data. 
D)  can be used with control variables, while extrapolation must be used in conjunction with instrumental variables. 
A)  involves causal estimates, while extrapolation involves correlations. 
B)  involves linear functional forms, while extrapolation involves non-linear function forms. 
C)  fills data gaps, while extrapolation fills beyond the extent of the data. 
D)  can be used with control variables, while extrapolation must be used in conjunction with instrumental variables.

Question 14

In the event that there is an acceptable model of the data-generating process within which the treatment effect is identified using samples from the population, but the 95% confidence level of the treatment effect contains both large negative and large positive values, what might be an appropriate step for the analyst to do?

Accepted Answer

A)  Collect more data. 
B)  Re-run the experiment. 
C)  Consider alternative data populations (e.g., additional variables) before attempting to estimate the effect. 
D)  Run a probit/logit model. 
A)  Collect more data. 
B)  Re-run the experiment. 
C)  Consider alternative data populations (e.g., additional variables) before attempting to estimate the effect. 
D)  Run a probit/logit model.

Question 15

When might imperfect multicollinearity not require the collecting of more data to remedy the likely imprecise estimates of the affected variables?

Accepted Answer

A)  When the model suffers from heteroscedasticity as well 
B)  When you are only conducting hypothesis tests 
C)  When the imperfect multicollinearity is confined to control variables only 
D)  When the treatment effect is positive 
A)  When the model suffers from heteroscedasticity as well 
B)  When you are only conducting hypothesis tests 
C)  When the imperfect multicollinearity is confined to control variables only 
D)  When the treatment effect is positive

Question 16

A data gap is any place where:

Accepted Answer

A)  data is measured with error. 
B)  the data is heteroscedastic. 
C)  there are missing data for a variable over an interval of values, but data are not missing for at least some values on both ends of the interval. 
D)  there is missing data. 
A)  data is measured with error. 
B)  the data is heteroscedastic. 
C)  there are missing data for a variable over an interval of values, but data are not missing for at least some values on both ends of the interval. 
D)  there is missing data.

Question 17

Suppose you have many observations of the scores students got on an exam, with other characteristics of the students including how many hours they studied for the exam that week and what is their current major GPA. Now, suppose you ran the standard linear regression of the exam grade on the number of hours studied: Gradei = β0 + β1 Hours Studiedi + Ui and got an estimate of β1, call it b1. Now, suppose a colleague tells you that the students with above average GPAs were the students who studied more for the exam. How does your estimate of β2* in the following regression, Gradei = β0* + β1 Hours Studiedi + β2* Major GPAi + Ui, inform you of what your estimate of what β1* will be for that same regression?

Accepted Answer

A)  If β2* > 0, then β1* > b1 
B)  If β2* > 0, then β1* 1 
C)  If β2* 1* > 0 
D)  If β2* > 0, then β1* > 0 
A)  If β2* > 0, then β1* > b1 
B)  If β2* > 0, then β1* 1 
C)  If β2* 1* > 0 
D)  If β2* > 0, then β1* > 0

Question 18

For a parameter that is identified, what should happen to the 99% confidence interval as the sample size gets larger?

Accepted Answer

A)  The right tail should get larger. 
B)  The left tail should get larger. 
C)  The width of the interval should get smaller. 
D)  The width of the interval should get wider. 
A)  The right tail should get larger. 
B)  The left tail should get larger. 
C)  The width of the interval should get smaller. 
D)  The width of the interval should get wider.

Question 19

The endogeneity problem that results from when a variable correlated with the treatment is not included as a control variable is known as the:

Accepted Answer

A)  instrumental variable. 
B)  omitted variable bias. 
C)  weak instruments problem. 
D)  least efficient estimator. 
A)  instrumental variable. 
B)  omitted variable bias. 
C)  weak instruments problem. 
D)  least efficient estimator.

Question 20

All else equal, the theoretical justifications for a particular function form should be stronger in the cases in which you are:

Accepted Answer

A)  attempting to identify correlations only. 
B)  running fixed effects regressions. 
C)  using an instrumental variable. 
D)  extrapolating further off the support of the data. 
A)  attempting to identify correlations only. 
B)  running fixed effects regressions. 
C)  using an instrumental variable. 
D)  extrapolating further off the support of the data.

Imperfect multicollinearity is when:

Determining whether a parameter is identified exclusively involves an argument about the data-generating process of the:

Which of the following functional forms will be the most restrictive in terms of the shape of the resulting interpolation of data gaps in a sample data set?

For a parameter that is identified, what should happen to the 95% confidence interval as the sample size gets larger?

The two critical elements required to sign the bias of an omitted variable include the sign of the:

In the event that you are collecting data on how heights relate to income in the U.S. population and you notice that in your sample of 1,500 individuals you have a data gap at 5'6", will this cause an identification problem?

In the event that there is not an acceptable model of the data-generating process within which the treatment effect is identified using samples from the population, the analyst should do what?

How does collecting more/alternative data put less burden on the modeling choices of the analyst?

In the event that your treatment variable is imperfectly multicollinear with one of your control variables, a possible remedy would be to:

Potential remedies for when your model suffers from the endogeneity problem include all of the following except what?

Which of the following settings constitutes an extrapolation?

The key distinction between extrapolation and interpolation is that interpolation:

When might imperfect multicollinearity not require the collecting of more data to remedy the likely imprecise estimates of the affected variables?

A data gap is any place where:

For a parameter that is identified, what should happen to the 99% confidence interval as the sample size gets larger?

The endogeneity problem that results from when a variable correlated with the treatment is not included as a control variable is known as the:

All else equal, the theoretical justifications for a particular function form should be stronger in the cases in which you are:

The Roles of Data and Predictive Analytics in Business

Reasoning With Data

Reasoning From Sample to Population

The Scientific Method: The Gold Standard for Establishing Causality

Linear Regression As a Fundamental Descriptive Tool

Correlation Vs Causality in Regression Analysis

Basic Methods for Establishing Causal Inference

Advanced Methods for Establishing Causal Inference

Prediction for a Dichotomous Variable

Filters

Exam 10: Identification and Data Assessment

Imperfect multicollinearity is when:

Determining whether a parameter is identified exclusively involves an argument about the data-generating process of the:

Which of the following functional forms will be the most restrictive in terms of the shape of the resulting interpolation of data gaps in a sample data set?

For a parameter that is identified, what should happen to the 95% confidence interval as the sample size gets larger?

The two critical elements required to sign the bias of an omitted variable include the sign of the:

In the event that you are collecting data on how heights relate to income in the U.S. population and you notice that in your sample of 1,500 individuals you have a data gap at 5'6", will this cause an identification problem?

In the event that there is not an acceptable model of the data-generating process within which the treatment effect is identified using samples from the population, the analyst should do what?

How does collecting more/alternative data put less burden on the modeling choices of the analyst?

In the event that your treatment variable is imperfectly multicollinear with one of your control variables, a possible remedy would be to:

Potential remedies for when your model suffers from the endogeneity problem include all of the following except what?

Which of the following settings constitutes an extrapolation?

The key distinction between extrapolation and interpolation is that interpolation:

When might imperfect multicollinearity not require the collecting of more data to remedy the likely imprecise estimates of the affected variables?

A data gap is any place where:

For a parameter that is identified, what should happen to the 99% confidence interval as the sample size gets larger?

The endogeneity problem that results from when a variable correlated with the treatment is not included as a control variable is known as the:

All else equal, the theoretical justifications for a particular function form should be stronger in the cases in which you are:

The Roles of Data and Predictive Analytics in Business

Reasoning With Data

Reasoning From Sample to Population

The Scientific Method: The Gold Standard for Establishing Causality

Linear Regression As a Fundamental Descriptive Tool

Correlation Vs Causality in Regression Analysis

Basic Methods for Establishing Causal Inference

Advanced Methods for Establishing Causal Inference

Prediction for a Dichotomous Variable

Filters