Couldcauseomittedvariablebiasiftheyareomitted 1

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Unformatted text preview: rs are that: • Vary across survey units but for the same unit, they stay constant over time • For example, innate ability, risk aversion, or state/firm unobserved characteristics that can be considered fixed over time. • Could cause omitted variable bias if they are omitted 1 The key idea: If an omitted variable does not change over time, then any changes in Y over time cannot be caused by the omitted variable, so one can look at changes in Y to get away from the omitted variable bias. Examples of a panel data set: 1. Drunk driving and alcohol taxes Observational unit: a U.S. state in a year • 48 U.S. states, so n = of entities = 48 • 7 years (1982,…, 1988), so T = # of time periods = 7 • So total # observations = 7×48 = 336 Variables: • Drunk driving rate (# drunk driving caught in that state in that year, per 10,000 state residents) • Alcohol tax • Other (legal driving age, average age, percentage of single males etc.) Higher alcohol taxes, more drunk driving? Figure: using data for a single year Higher alcohol taxes, more drunk driving? Why might there be more drunk driving in states that have higher alcohol taxes? Other stage characteristics that may be omitted: • “...
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This document was uploaded on 03/11/2014.

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