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Exam 7: Basic Methods for Establishing Causal Inference

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Question 21

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A representative sample is one whose:

A) distribution is approximately normal as it gets larger.
B) sample is drawn randomly.
C) distribution approximately matches that of the population, for a subset of observed, independent variables.
D) distribution exactly matches the population distribution for the outcome variable.

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Question 22

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The use of a proxy variable changes how you must interpret which of the following statistics?

A) R-squared
B) P-value of coefficient on treatment
C) Standard errors on treatment
D) None of the answers is correct.

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Question 23

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A proxy variable is a variable used in a regression equation in order to:

Suppose you have estimated the following regression: Refrigerator Sales_i = β₀ + β₁Price_i + U_i. However, when presenting your results, someone in the audience claims your estimate is biased because your sample only contains sales figures for refrigerators that are priced over $300. Their point is:

Suppose you have the following regression results from a regression of home prices on house attributes for a random sample of house transactions: $\begin{array} { | l | r | r | } \hline & \text { Coefficierts } & \text { Stardard Error } \\\hline \text { Iratercept } & 16310.0 & 4114.5 \\\hline \text { Nurrber of Bedroorrs } & 7295.3 & 1399 . \\\hline \text { Number of Bathroorrs } & 23473.0 & 4032.0 \\\hline\end{array}$ r-squared = 0.302 Adjusted r-squared = 0.299 If we assume that the proper model to predict the market value of houses is given by this regression, and thus we are getting unbiased estimates of the true relationships between number of bedrooms/bathrooms and sales price, what is the effect on sales price of increasing the number of bathrooms in a house by one, holding number of bedrooms fixed?

Does the interpretation on the estimated coefficient on the treatment change if you use a proxy variable to control for a confounding factor?

In determining the causal effect of Price on Sales, if advertising spend is a good control variable, how will the correlation of the error terms from the regression, Sales_i = β₀ + β₁Price_i + U_i, and Sales_i = α₀ + α₁Price_i + α₂Advertising_i + V_i and price be related?

Irrelevant variables are ones that:

Which step is not involved with constructing a representative sample?

The determining function that drives share of accepted job offers for a company is given by the following equation: AcceptedOffers_t = α₀ + α₁StartingSalary_t + α₂EconomicClimate_t + U_t, where the unit of observation is particular month (t) . Suppose one wanted to use the national unemployment rate (unemployment_t) as a proxy for EconomicClimate. Which of the following describes a condition required to hold for this to be an adequate proxy variable?

In attempting to estimate the causal effect of employee hours worked on total units produced, you include the age of the production equipment as a control variable. Which of the following conditions would not be a good reason to include this control variable?

Which of the following regressions would yield a coefficient estimate that would be directly interpreted as the price elasticity of demand?

An ordinal variable is one that contains categories that:

Suppose you have the following regression results from a regression of home prices on house attributes for a random sample of house transactions: $\begin{array} { | l | r | r | } \hline & \text { Coefficierts } & \text { Stardard Error } \\\hline \text { Iratercept } & 16310.0 & 4114.5 \\\hline \text { Nurrber of Bedroorrs } & 7295.3 & 1399 . \\\hline \text { Number of Bathroorrs } & 23473.0 & 4032.0 \\\hline\end{array}$ Given these results which additional condition would be sufficient to ensure number of bathrooms satisfies the "primary criteria" for a good control variable in attempting to identify the causal effect of number of bedrooms on house prices?

Which of the following variables might be a proxy variable for the confounding factor of cognitive ability in a sample of workers and their ability to generate sales?

What condition best describes the endogeneity problem?

The determining function that drives movie ticket sales is given by the following equation: Sales_it = α₀ + α₁ HolidaySeason_it + α₂CastAwareness_it + U_it, where the unit of observation is a particular movie (i) in month (t) . Suppose one wanted to use cumulative past movie appearances of the entire cast of a movie as a proxy for CastAwareness. Which of the following conditions would not be one required for this to be an adequate proxy variable?

The determining function that drives share of accepted job offers for a company is given by the following equation: AcceptedOffers_t = α₀ + α₁StartingSalary_t + α₂EconomicClimate_t + U_t, where the unit of observation is particular month (t) . Suppose one wanted to use the national unemployment rate (unemployment_t) as a proxy for EconomicClimate. Which of the following would be a condition that would rule out the unemployment rate being a good proxy for economic climate?

Which of the following cannot be estimated using traditional linear regression techniques?

In estimating the effect of a difference in earnings for ivy league graduates, non-ivy league college graduates, and non-college graduates the following regression is run: Earnings_i = β₀ + β₁IvyLeague_i + β₂NonIvyCollegeGraduate_i + U_i. What does the coefficient β₁ represent?