Question 1

The controls for cross-sectional groups in the data generating process are known as:

Accepted Answer

A)  treatment effects. 
B)  fixed effects. 
C)  instrumental variables. 
D)  difference-in-differences. 
A)  treatment effects. 
B)  fixed effects. 
C)  instrumental variables. 
D)  difference-in-differences.

Question 2

A fixed effects model is one in which the data generating process includes:

Accepted Answer

A)  controls for cross-sectional groups. 
B)  controls for heteroscedasticity. 
C)  some control variables (e.g., Income) that vary over time. 
D)  a selected sample of cross-sectional units. 
A)  controls for cross-sectional groups. 
B)  controls for heteroscedasticity. 
C)  some control variables (e.g., Income) that vary over time. 
D)  a selected sample of cross-sectional units.

Question 3

If you conduct your estimation using two-stage least squares by separately estimating each regression (say in Excel), what condition should you be aware of when interpreting your second stage results?

Accepted Answer

A)  The coefficients will be too low. 
B)  The coefficients will be too high. 
C)  The standard errors reported in the second stage are not accurate. 
D)  The residuals do not sum to zero as they due in the first stage. 
A)  The coefficients will be too low. 
B)  The coefficients will be too high. 
C)  The standard errors reported in the second stage are not accurate. 
D)  The residuals do not sum to zero as they due in the first stage.

Question 4

Interpreting the coefficients on fixed effects will always be based on what?

Accepted Answer

A)  If the regression was run using STATA or Excel. 
B)  The sign of the coefficient on the instrumental variable in the first stage regression. 
C)  The base group omitted from the regression. 
D)  The standard error on the treatment variable. 
A)  If the regression was run using STATA or Excel. 
B)  The sign of the coefficient on the instrumental variable in the first stage regression. 
C)  The base group omitted from the regression. 
D)  The standard error on the treatment variable.

Question 5

Generally speaking, the difference-in-differences is defined to be the:

Accepted Answer

A)  difference in outcomes from random assignment. 
B)  difference in outcomes from different treatment levels. 
C)  difference in the temporal change for the outcome between treated and untreated groups. 
D)  None of the answers is correct. 
A)  difference in outcomes from random assignment. 
B)  difference in outcomes from different treatment levels. 
C)  difference in the temporal change for the outcome between treated and untreated groups. 
D)  None of the answers is correct.

Question 6

In order for a variable to be a valid instrumental variable it needs to satisfy which two conditions?

Accepted Answer

A)  Exogenous and consistent 
B)  Efficient and uncorrelated 
C)  Relevant and exogenous 
D)  Relevant and efficient 
A)  Exogenous and consistent 
B)  Efficient and uncorrelated 
C)  Relevant and exogenous 
D)  Relevant and efficient

Question 7

Two-stage least squares can be executed:

Accepted Answer

A)  only with advanced software. 
B)  only on small samples. 
C)  straightforwardly by combining two separate regressions. 
D)  only with panel data sets. 
A)  only with advanced software. 
B)  only on small samples. 
C)  straightforwardly by combining two separate regressions. 
D)  only with panel data sets.

Question 8

The following regression results are from the first stage regression of Price on income and wholesale costs-which is serving as the instrument for a particular grocery product across different markets: Pricei = 3.2(1.0) + 4.3(0.8)WholesaleCostsi + 5.5(2.7)Incomei, where standard errors are reported in parenthesis. What conclusion can be drawn about the instrumental variable?

Accepted Answer

A)  It's unbiased. 
B)  With a t-stat over 2.5 (4.3/0.8), the instrument is relevant. 
C)  With a t-stat over 2.5 (4.3/0.8), the instrument is exogenous. 
D)  It's under reporting the effect of income on price. 
A)  It's unbiased. 
B)  With a t-stat over 2.5 (4.3/0.8), the instrument is relevant. 
C)  With a t-stat over 2.5 (4.3/0.8), the instrument is exogenous. 
D)  It's under reporting the effect of income on price.

Question 9

Suppose you are estimating demand relationships, where you are attempting to identify the effect of price on quantity sold (i.e., Qi = α0 + α1Pricei + Ui). It will often be the case that the use of an instrumental variable for price will likely yield a coefficient (for α1) that relates to the use of multiple regression in what way?

Accepted Answer

A)  Is lower, because price and Ui are likely positively correlated. 
B)  Is lower, because price and Ui are likely negatively correlated. 
C)  Is higher, because price and Ui are likely negatively correlated. 
D)  Is higher, because price and Ui are likely positively correlated. 
A)  Is lower, because price and Ui are likely positively correlated. 
B)  Is lower, because price and Ui are likely negatively correlated. 
C)  Is higher, because price and Ui are likely negatively correlated. 
D)  Is higher, because price and Ui are likely positively correlated.

Question 10

In estimating the effect of price on sales (Salesi = α0 + α1Pricei + Ui), you are attempting to find an instrumental variable that will solve the endogeneity problem caused by the confounding factor of number of competitors being within Ui, which is correlated with price. Which of the following statements would suggest that wholesale costs would satisfy the exogenous condition to be a potential instrument variable?

Accepted Answer

A)  Wholesale costs are uncorrelated with price. 
B)  Wholesale costs are correlated with price. 
C)  Wholesale costs are uncorrelated with sales. 
D)  Wholesale costs are uncorrelated with number of competitors. 
A)  Wholesale costs are uncorrelated with price. 
B)  Wholesale costs are correlated with price. 
C)  Wholesale costs are uncorrelated with sales. 
D)  Wholesale costs are uncorrelated with number of competitors.

The controls for cross-sectional groups in the data generating process are known as:

A fixed effects model is one in which the data generating process includes:

If you conduct your estimation using two-stage least squares by separately estimating each regression (say in Excel), what condition should you be aware of when interpreting your second stage results?

Interpreting the coefficients on fixed effects will always be based on what?

Generally speaking, the difference-in-differences is defined to be the:

In order for a variable to be a valid instrumental variable it needs to satisfy which two conditions?

Two-stage least squares can be executed:

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

Prediction for a Dichotomous Variable

Identification and Data Assessment

Filters

Exam 8: Advanced Methods for Establishing Causal Inference

The controls for cross-sectional groups in the data generating process are known as:

A fixed effects model is one in which the data generating process includes:

If you conduct your estimation using two-stage least squares by separately estimating each regression (say in Excel), what condition should you be aware of when interpreting your second stage results?

Interpreting the coefficients on fixed effects will always be based on what?

Generally speaking, the difference-in-differences is defined to be the:

In order for a variable to be a valid instrumental variable it needs to satisfy which two conditions?

Two-stage least squares can be executed:

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

Prediction for a Dichotomous Variable

Identification and Data Assessment

Filters