Consider the following scenario. A sports economist has collected data to test the relationship between shot quality and points per game for a sample of NBA players. The data is a panel dataset comprised of 2,000 NBA players with season-level performance statistics over the past 50 NBA seasons. The sports economist is worried that the player's primary playing position might be correlated with both shot quality and points per game, and position is easy to measure. However, historical changes in analytics have influenced the types of shots players across the league have taken over time, and these changes are generally difficult to quantify. To address the potential omitted variable bias that could arise because of this, the economist should include in their regression: Time fixed effects O Neither time nor entity fixed effects Both time and entity fixed effects Entity fixed effects
Consider the following scenario. A sports economist has collected data to test the relationship between shot quality and points per game for a sample of NBA players. The data is a panel dataset comprised of 2,000 NBA players with season-level performance statistics over the past 50 NBA seasons. The sports economist is worried that the player's primary playing position might be correlated with both shot quality and points per game, and position is easy to measure. However, historical changes in analytics have influenced the types of shots players across the league have taken over time, and these changes are generally difficult to quantify. To address the potential omitted variable bias that could arise because of this, the economist should include in their regression: Time fixed effects O Neither time nor entity fixed effects Both time and entity fixed effects Entity fixed effects
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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![Consider the following scenario. A sports economist has collected data to test the
relationship between shot quality and points per game for a sample of NBA players.
The data is a panel dataset comprised of 2,000 NBA players with season-level
performance statistics over the past 50 NBA seasons. The sports economist is
worried that the player's primary playing position might be correlated with both shot
quality and points per game, and position is easy to measure. However, historical
changes in analytics have influenced the types of shots players across the league
have taken over time, and these changes are generally difficult to quantify. To
address the potential omitted variable bias that could arise because of this, the
economist should include in their regression:
Time fixed effects
O Neither time nor entity fixed effects
Both time and entity fixed effects
O Entity fixed effects](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb1134109-0e52-4f8c-919a-f525872f6371%2F836cba86-d7c6-47a3-b0d6-b5668bb69806%2Fipop7e4_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Consider the following scenario. A sports economist has collected data to test the
relationship between shot quality and points per game for a sample of NBA players.
The data is a panel dataset comprised of 2,000 NBA players with season-level
performance statistics over the past 50 NBA seasons. The sports economist is
worried that the player's primary playing position might be correlated with both shot
quality and points per game, and position is easy to measure. However, historical
changes in analytics have influenced the types of shots players across the league
have taken over time, and these changes are generally difficult to quantify. To
address the potential omitted variable bias that could arise because of this, the
economist should include in their regression:
Time fixed effects
O Neither time nor entity fixed effects
Both time and entity fixed effects
O Entity fixed effects
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