Question 1

Which of the following settings constitutes an interpolation?

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. C

Question 2

Consider the regression of Salesi = α0 + α1Pricei + α2Promoi + α3Weekendi + Ui, where Promoi and Weekendi are binary variables for if the particular observation was on promo (1 if promo, 0 otherwise), or comes from a weekend day (1 if weekend day, 0 otherwise). A member of your team informs you that all the promotions run for the products in your population were run on weekends. What identification challenge might you worry about? What identification challenge might you worry about?

Accepted Answer

A)  Heteroscedasticity 
B)  Endogeneity 
C)  Perfect multicollinearity 
D)  Imperfect multicollinearity 
A)  Heteroscedasticity 
B)  Endogeneity 
C)  Perfect multicollinearity 
D)  Imperfect multicollinearity C

Question 3

Suppose you are estimating the following model: Yi = β0 + β1Xi + Ui. You believe the variance of the unobserved factors (U) varies with X. If this is true, what is the consequence?

Accepted Answer

A)  Your estimate for β1 will be biased. 
B)  Your estimate for β0 will be biased. 
C)  Your estimate for β1 and β0 will be biased. 
D)  None of the answers is correct. 
A)  Your estimate for β1 will be biased. 
B)  Your estimate for β0 will be biased. 
C)  Your estimate for β1 and β0 will be biased. 
D)  None of the answers is correct. D

Question 4

Defining and omitting a base group amongst a set of fixed effects is an attempt to remedy which identification challenge?

Accepted Answer

A)  A control variable is correlated with the error term. 
B)  The treatment variable is correlated with the error term. 
C)  Perfect multicollinearity 
D)  Imperfect multicollinearity 
A)  A control variable is correlated with the error term. 
B)  The treatment variable is correlated with the error term. 
C)  Perfect multicollinearity 
D)  Imperfect multicollinearity

Question 5

Which of the following is a step that can remedy an interpolation/extrapolation identification problem?

Accepted Answer

A)  Use a probit/logit model to alleviate the data gaps. 
B)  Collect more/alternative data to close the data gap, extent of the data. 
C)  Use two-stage least squares to interpolate/extrapolate. 
D)  Use a fixed effects model. 
A)  Use a probit/logit model to alleviate the data gaps. 
B)  Collect more/alternative data to close the data gap, extent of the data. 
C)  Use two-stage least squares to interpolate/extrapolate. 
D)  Use a fixed effects model.

Question 6

When making functional form choices to alleviate an interpolation/extrapolation identification challenge the justification will almost always take what form?

Accepted Answer

A)  A calculation of difference of means across sub-samples of the data 
B)  Theoretical argument 
C)  A hypothesis test 
D)  A p-value from a test statistic 
A)  A calculation of difference of means across sub-samples of the data 
B)  Theoretical argument 
C)  A hypothesis test 
D)  A p-value from a test statistic

Question 7

Perfect multicollinearity is when:

Accepted Answer

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

Question 8

All of the following will cause an identification challenge except for what?

Accepted Answer

A)  A control variable is correlated with the error term. 
B)  The treatment variable is correlated with the error term. 
C)  Perfect multicollinearity 
D)  Imperfect multicollinearity 
A)  A control variable is correlated with the error term. 
B)  The treatment variable is correlated with the error term. 
C)  Perfect multicollinearity 
D)  Imperfect multicollinearity

Question 9

A useful diagnostic measure for imperfect multicollinearity is the:

Accepted Answer

A)  z-score. 
B)  t-test. 
C)  variance inflation factor. 
D)  likelihood ratio test. 
A)  z-score. 
B)  t-test. 
C)  variance inflation factor. 
D)  likelihood ratio test.

Question 10

You are collecting information on prices and attributes of electrical vehicles such as the mile range from a single charge for a bunch of electric cars. Suppose your company has created an electric vehicle that will have a mile range twice as large as an existing electric vehicle on the market. Does the model you've estimated have an identification problem?

Accepted Answer

A)  No, because this data gap does not exist in the population. 
B)  Yes, the extrapolation required exists in the population of current electric vehicles, not just your sample. 
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, the extrapolation required exists in the population of current electric vehicles, not just your sample. 
C)  Yes, but you can still extrapolate the data gap. 
D)  No, because data gaps cannot cause identification problems.

Question 11

For a parameter that is not 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 eventually capture the true parameter value. 
D)  None of the answers is correct. 
A)  The right tail should get larger. 
B)  The left tail should get larger. 
C)  The width of the interval should eventually capture the true parameter value. 
D)  None of the answers is correct.

Question 12

When one conducts interpolation, one is:

Accepted Answer

A)  imputing values that have been mismeasured. 
B)  correcting values that have been mismeasured. 
C)  drawing conclusions where there are "gaps" in the data. 
D)  drawing conclusions beyond the extent of the data. 
A)  imputing values that have been mismeasured. 
B)  correcting values that have been mismeasured. 
C)  drawing conclusions where there are "gaps" in the data. 
D)  drawing conclusions beyond the extent of the data.

Question 13

A parameter is identified in the event that it can be:

Accepted Answer

A)  rejected to be zero at the 95 percent confidence level. 
B)  rejected to be zero at the 99 percent confidence level. 
C)  estimated with any level of precision given a large enough sample from the population. 
D)  safely assumed to not suffer from heteroscedasticity. 
A)  rejected to be zero at the 95 percent confidence level. 
B)  rejected to be zero at the 99 percent confidence level. 
C)  estimated with any level of precision given a large enough sample from the population. 
D)  safely assumed to not suffer from heteroscedasticity.

Question 14

Suppose that you observed several key characteristics of a random sample of firms in your industry. You know that the semi-partial correlation of firm Productivity (Y) with R&D (Z) investment holding amount of Labor (X) fixed is positive. Furthermore, suppose you know that the covariance of R&D investment and amount of labor is positive. How will the coefficient on Labor when you run the regression of Productivity on Labor and R&D investment relate to the coefficient on Labor when you run a regression of Productivity on just Labor?

Accepted Answer

A)  It'll be equal to 
B)  It'll be greater than 
C)  It'll be less than 
D)  There is not enough information to determine. 
A)  It'll be equal to 
B)  It'll be greater than 
C)  It'll be less than 
D)  There is not enough information to determine.

Question 15

A typical remedy for perfect multicollinearity amongst control variables is to:

Accepted Answer

A)  use an instrumental variable. 
B)  use the within estimator. 
C)  drop one of the control variables that is exactly linear with other control variables. 
D)  use the t-test. 
A)  use an instrumental variable. 
B)  use the within estimator. 
C)  drop one of the control variables that is exactly linear with other control variables. 
D)  use the t-test.

Question 16

How does making functional form choices put less burden on the data collection process of the analyst?

Accepted Answer

A)  By making functional form choices, an analyst can interpolate/extrapolate measures of treatment effects within gaps/beyond the extent of the data sampled. 
B)  By making functional form choices, an analyst can avoid having to take random samples from the population. 
C)  By making functional form choices, an analyst can avoid having to ensure that the observations are carefully measured. 
D)  By making functional form choices, an analyst can avoid having to ensure the dataset is structured. 
A)  By making functional form choices, an analyst can interpolate/extrapolate measures of treatment effects within gaps/beyond the extent of the data sampled. 
B)  By making functional form choices, an analyst can avoid having to take random samples from the population. 
C)  By making functional form choices, an analyst can avoid having to ensure that the observations are carefully measured. 
D)  By making functional form choices, an analyst can avoid having to ensure the dataset is structured.

Question 17

Fortunately, even in cases where we suffer from an omitted variable bias, it is often the case that with careful reasoning it is possible to:

Accepted Answer

A)  sign the bias. 
B)  make the size of bias not statistically significant from zero. 
C)  report appropriately adjusted standard errors. 
D)  construct alternative p-values. 
A)  sign the bias. 
B)  make the size of bias not statistically significant from zero. 
C)  report appropriately adjusted standard errors. 
D)  construct alternative p-values.

Question 18

If the data-generating process you are working with has a population parameter that is not identified it will be the case that:

Accepted Answer

A)  you will need more data to get a precise estimate. 
B)  you will always need an instrumental variable to get an unbiased estimate. 
C)  a fixed effect model will be required. 
D)  None of the answers is correct. 
A)  you will need more data to get a precise estimate. 
B)  you will always need an instrumental variable to get an unbiased estimate. 
C)  a fixed effect model will be required. 
D)  None of the answers is correct.

Question 19

When one conducts extrapolation, one is:

Accepted Answer

A)  imputing values that have been mismeasured. 
B)  correcting values that have been mismeasured. 
C)  drawing conclusions where there are "gaps" in the data. 
D)  drawing conclusions beyond the extent of the data. 
A)  imputing values that have been mismeasured. 
B)  correcting values that have been mismeasured. 
C)  drawing conclusions where there are "gaps" in the data. 
D)  drawing conclusions beyond the extent of the data.

Question 20

In the event that your treatment variable suffers from perfect multicollinearity with one of your controls, a viable remedy is:

Accepted Answer

A)  drop the control variable. 
B)  drop the treatment variable of interest. 
C)  change the population to one, where the treatment and control are not exactly linearly related. 
D)  use the within estimator. 
A)  drop the control variable. 
B)  drop the treatment variable of interest. 
C)  change the population to one, where the treatment and control are not exactly linearly related. 
D)  use the within estimator.

Which of the following settings constitutes an interpolation?

Suppose you are estimating the following model: Y_i = β₀ + β₁X_i + U_i. You believe the variance of the unobserved factors (U) varies with X. If this is true, what is the consequence?

Defining and omitting a base group amongst a set of fixed effects is an attempt to remedy which identification challenge?

Which of the following is a step that can remedy an interpolation/extrapolation identification problem?

When making functional form choices to alleviate an interpolation/extrapolation identification challenge the justification will almost always take what form?

Perfect multicollinearity is when:

All of the following will cause an identification challenge except for what?

A useful diagnostic measure for imperfect multicollinearity is the:

For a parameter that is not identified, what should happen to the 95% confidence interval as the sample size gets larger?

When one conducts interpolation, one is:

A parameter is identified in the event that it can be:

A typical remedy for perfect multicollinearity amongst control variables is to:

How does making functional form choices put less burden on the data collection process of the analyst?

Fortunately, even in cases where we suffer from an omitted variable bias, it is often the case that with careful reasoning it is possible to:

If the data-generating process you are working with has a population parameter that is not identified it will be the case that:

When one conducts extrapolation, one is:

In the event that your treatment variable suffers from perfect multicollinearity with one of your controls, a viable remedy is:

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

Which of the following settings constitutes an interpolation?

Suppose you are estimating the following model: Yi = β0 + β1Xi + Ui. You believe the variance of the unobserved factors (U) varies with X. If this is true, what is the consequence?

Defining and omitting a base group amongst a set of fixed effects is an attempt to remedy which identification challenge?

Which of the following is a step that can remedy an interpolation/extrapolation identification problem?

When making functional form choices to alleviate an interpolation/extrapolation identification challenge the justification will almost always take what form?

Perfect multicollinearity is when:

All of the following will cause an identification challenge except for what?

A useful diagnostic measure for imperfect multicollinearity is the:

For a parameter that is not identified, what should happen to the 95% confidence interval as the sample size gets larger?

When one conducts interpolation, one is:

A parameter is identified in the event that it can be:

A typical remedy for perfect multicollinearity amongst control variables is to:

How does making functional form choices put less burden on the data collection process of the analyst?

Fortunately, even in cases where we suffer from an omitted variable bias, it is often the case that with careful reasoning it is possible to:

If the data-generating process you are working with has a population parameter that is not identified it will be the case that:

When one conducts extrapolation, one is:

In the event that your treatment variable suffers from perfect multicollinearity with one of your controls, a viable remedy is:

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

Suppose you are estimating the following model: Y_i = β₀ + β₁X_i + U_i. You believe the variance of the unobserved factors (U) varies with X. If this is true, what is the consequence?