Lecture+6+Regression+with+panel+data

# 44beertax 1988data drunkdriverate 201015beertax 1513

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Unformatted text preview: t change between 1982 and 1988. The math: consider drunk driving rates in 1988 and 1982: DrunkDriveRatei1988 = β0 + β1BeerTaxi1988 + β2wi + ui1988 DrunkDriveRatei1982 = β0 + β1BeerTaxi1982 + β2wi + ui1982 Suppose the OLS conditional mean zero assumption E(uit|BeerTaxit, wi) = 0 holds, which means the errors is not correlated with BeerTaxit and wi. Subtracting 1988 from 1982 (that is, calculating the change), eliminates the effect of wi, i.e., DrunkDriveRatei1988 – DrunkdriveRatei1982 = β1(BeerTaxi1988 – BeerTaxi1982) + (ui1988 – ui1982) • The new error term, (ui1988 – ui1982), is uncorrelated with either BeerTaxi1988 or BeerTaxi1982. • This “difference” equation can be estimated by OLS. • The omitted variable wi doesn’t change, so it cannot be a determinant of the change in Y 4 Example: Drunk driving and beer taxes 1982 data: (n = 48) DrunkDriveRate = 1.86 + 0.44BeerTax 1988 data: DrunkDriveRate = 2.01 + 0.15BeerTax (.15) (.13) (n = 48) (.11) (.13) Difference estimation (n = 48) DDR − DDR = –.072 – 1.04(BeerTax1988–BeerTax1982) 1988 1982 (.065) (.36) Figure: Change in drunk driving rate and Beer taxes, 1988‐1992 Fixed Effects Regression What if you have more than 2 time periods (T > 2)? For state i Yit = β0 + β1Xit + β2wi + uit, i =1,…,n, t= 1,…,T Let’s assume that T=3, Yi1...
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## This document was uploaded on 03/11/2014.

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