Exam 8: Advanced Methods for Establishing Causal Inference

Suppose you are trying to estimate the following regression: Y_i = β₀ + β₁X_1i + β₂X_2i + β₃X_3i + U_i, with an instrument for Z_i for X_2i. All of the following variables will be included (on the right- and left-hand side of this regression) in the first stage of two-stage least squares except for which one?

The difference-in-differences approach relaxes some of the required assumptions for establishing causality by leveraging what dimension of the empirical setting?

The process of using two regressions to measure the causal effect of a variable while utilizing an instrumental variable is known as:

A weak instrument is an instrumental variable:

In lieu of including a separate dummy variable for each different time period of a panel, often times the use of a simple time trend is chosen. This choice will lead to a(n):

Why do you use only some of the variation of your treatment variable (the component predicted in the first stage) when using an instrumental variable approach?

Suppose you are trying to estimate the following regression: Y_i = β₀ + β₁X_1i + β₂X_2i + β₃X_3i + U_i, with an instrument for Z_i for X_2i. All of the following variables will be included (on the right- and left-hand side of this regression) in the second stage of two-stage least squares except for which one?

In words, why can utilizing an instrumental variable be an effective means toward identifying the causal effect of a treatment on an outcome?

To estimate a difference-in-differences it requires that one has a:

Generally speaking a consequence of using an instrumental variable approach with a weak instrument will be:

All standard regression software should be able to help you in determining if your instrument is:

Suppose you have two departments and have daily data on complaints and number of products they oversee for the departments over two years. In the second year, you increased the number of products under the scope of department #2 by 10. To estimate the effect of this increase in the number of products, you estimate the following regression: Complaints_it = α + β₁ × Department #2_it + β₂ × Year2_it + β₃ × Department #2_it × Year2_it + U_it. Which coefficient controls for non-time varying effects that make department #1 have more complaints than department #2?

An instrumental variable is relevant if that variable is:

Why is it the case that an instrumental variable is not found directly in the determining function?

Which object from the first or second stage reports whether an instrument is relevant?

Suppose you have two departments and have daily data on complaints and number of products they oversee for the departments over two years. In the second year, you increased the number of products under the scope of department #2 by 10. To estimate the effect of this increase in the number of products, you estimate the following regression: Complaints_it = α + β₁ × Department #2_it + β₂ × Year2_it + β₃ × Department #2_it × Year2_it + U_it. In this regression, what is the "diff-in-diff"?

The hardest part of implementing the instrumental variables approach is:

In the regression model Unemployment Rate_it = α + γ₁ × Alabama_it + γ₂ × South Carolina_it + γ₃ × North Carolina_it + β₁Income_it+ φYear_it + ε_it, the coefficients for the fixed effects of the model will be given by:

All of the following conditions are necessary for an instrumental variable Z to be a valid instrument for X in the regression: Y_i = β₀ + β₁X_i + U_i except for which condition?

All of the following coefficients/statistics will be the same across the dummy variable and within estimator approaches for estimating a fixed effects model except: